Paper Digest: AAAI 2023 Highlights
To search or review papers within AAAI-2023 related to a specific topic, please use the search by venue and review by venue services. To browse papers by author, here is a list of top authors (AAAI-2023). You may also like to explore our “Best Paper” Digest (AAAI), which lists the most influential AAAI papers since 1982.
The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2023, it was held in Washington DC.
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TABLE 1: Paper Digest: AAAI 2023 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Back to The Future: Toward A Hybrid Architecture for Ad Hoc Teamwork Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our architecture builds on the principles of step-wise refinement and ecological rationality to enable an ad hoc agent to perform non-monotonic logical reasoning with prior commonsense domain knowledge and models learned rapidly from limited examples to predict the behavior of other agents. |
Hasra Dodampegama; Mohan Sridharan; |
| 2 | Reducing ANN-SNN Conversion Error Through Residual Membrane Potential Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we make a detailed analysis of unevenness error and divide it into four categories. |
Zecheng Hao; Tong Bu; Jianhao Ding; Tiejun Huang; Zhaofei Yu; |
| 3 | Hierarchical ConViT with Attention-Based Relational Reasoner for Visual Analogical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the challenges of visual perception and logic reasoning on RPMs, we propose a Hierarchical ConViT with Attention-based Relational Reasoner (HCV-ARR). |
Wentao He; Jialu Zhang; Jianfeng Ren; Ruibin Bai; Xudong Jiang; |
| 4 | Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. |
Liwei Huang; Zhengyu Ma; Liutao Yu; Huihui Zhou; Yonghong Tian; |
| 5 | A Semi-parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Approaches to characterizing high-dimensional sensory spaces either require strong parametric assumptions about these additional contextual dimensions, or fail to leverage known properties of classical psychometric curves. We overcome both limitations by introducing a semi-parametric model of sensory discrimination that applies traditional psychophysical models along a stimulus intensity dimension, but puts Gaussian process (GP) priors on the parameters of these models with respect to the remaining dimensions. |
Stephen Keeley; Benjamin Letham; Craig Sanders; Chase Tymms; Michael Shvartsman; |
| 6 | A Machine with Short-Term, Episodic, and Semantic Memory Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. |
Taewoon Kim; Michael Cochez; Vincent Francois-Lavet; Mark Neerincx; Piek Vossen; |
| 7 | Persuasion Strategies in Advertisements Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by persuasion literature in social psychology and marketing, we introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. |
Yaman Kumar; Rajat Jha; Arunim Gupta; Milan Aggarwal; Aditya Garg; Tushar Malyan; Ayush Bhardwaj; Rajiv Ratn Shah; Balaji Krishnamurthy; Changyou Chen; |
| 8 | Intensity-Aware Loss for Dynamic Facial Expression Recognition in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, if the expressions with different intensities are treated equally, the features learned by the networks will have large intra-class and small inter-class differences, which are harmful to DFER. To tackle this problem, we propose the global convolution-attention block (GCA) to rescale the channels of the feature maps. |
Hanting Li; Hongjing Niu; Zhaoqing Zhu; Feng Zhao; |
| 9 | AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. |
Pritam Sarkar; Aaron Posen; Ali Etemad; |
| 10 | ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. |
Jiangrong Shen; Qi Xu; Jian K. Liu; Yueming Wang; Gang Pan; Huajin Tang; |
| 11 | Zero-Shot Linear Combinations of Grounded Social Interactions with Linear Social MDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How an agent responds socially, should depend on what it thinks the other agent is doing at that point in time. To encode this notion, we take linear combinations of social interactions as defined in Social MDPs, and compute the weights on those combinations on the fly depending on the estimated goals of other agents. |
Ravi Tejwani; Yen-Ling Kuo; Tianmin Shu; Bennett Stankovits; Dan Gutfreund; Joshua B. Tenenbaum; Boris Katz; Andrei Barbu; |
| 12 | Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we introduce four types of neuronal dynamics to post-process the sequential patterns generated from the spiking transformer to get the complex dynamic neuron improved spiking transformer neural network (DyTr-SNN). |
Qingyu Wang; Tielin Zhang; Minglun Han; Yi Wang; Duzhen Zhang; Bo Xu; |
| 13 | Self-Supervised Graph Learning for Long-Tailed Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To relieve the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD) framework which leverages the self-supervised manner to assist the graph-based cognitive diagnosis, then the performance on those students with sparse data can be improved. |
Shanshan Wang; Zhen Zeng; Xun Yang; Xingyi Zhang; |
| 14 | CMNet: Contrastive Magnification Network for Micro-Expression Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we provide a reliable scheme to extract intensity clues while considering their variation on the time scale. |
Mengting Wei; Xingxun Jiang; Wenming Zheng; Yuan Zong; Cheng Lu; Jiateng Liu; |
| 15 | Disentangling Reafferent Effects By Doing Nothing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Toward the development of more general agents, we develop a framework that enables agents to disentangle self-caused and externally-caused sensory effects. |
Benedict Wilkins; Kostas Stathis; |
| 16 | Learning Temporal-Ordered Representation for Spike Streams Based on Discrete Wavelet Transforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to mine temporal-robust features of spikes in time-frequency space with wavelet transforms. |
Jiyuan Zhang; Shanshan Jia; Zhaofei Yu; Tiejun Huang; |
| 17 | ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. |
Ruizhe Zheng; Jun Li; Yi Wang; Tian Luo; Yuguo Yu; |
| 18 | Progress and Limitations of Deep Networks to Recognize Objects in Unusual Poses Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We create a synthetic dataset of images of objects in unusual orientations, and evaluate the robustness of a collection of 38 recent and competitive deep networks for image classification. We show that classifying these images is still a challenge for all networks tested, with an average accuracy drop of 29.5% compared to when the objects are presented upright. |
Amro Abbas; Stéphane Deny; |
| 19 | Denoising After Entropy-Based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we find that earlier approaches that used the provided labels to quantify difficulty could be affected by the small proportion of noisy labels. |
Sumyeong Ahn; Se-Young Yun; |
| 20 | Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current attempts to use `general’ input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. |
Naveed Akhtar; Mohammad Amir Asim Khan Jalwana; |
| 21 | Deep Digging Into The Generalization of Self-Supervised Monocular Depth Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the backbone networks (e.g., CNNs, Transformers, and CNN-Transformer hybrid models) toward the generalization of monocular depth estimation. |
Jinwoo Bae; Sungho Moon; Sunghoon Im; |
| 22 | Self-Contrastive Learning: Single-Viewed Supervised Contrastive Framework Using Sub-network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To exploit the strength of multi-views while avoiding the high computation cost, we introduce a multi-exit architecture that outputs multiple features of a single image in a single-viewed framework. |
Sangmin Bae; Sungnyun Kim; Jongwoo Ko; Gihun Lee; Seungjong Noh; Se-Young Yun; |
| 23 | Layout Representation Learning with Spatial and Structural Hierarchies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel hierarchical modeling method for layout representation learning, the core of design documents (e.g., user interface, poster, template). |
Yue Bai; Dipu Manandhar; Zhaowen Wang; John Collomosse; Yun Fu; |
| 24 | Cross-Modal Label Contrastive Learning for Unsupervised Audio-Visual Event Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose cross-modal label contrastive learning to exploit multi-modal information among unlabeled audio and visual streams as self-supervision signals. |
Peijun Bao; Wenhan Yang; Boon Poh Ng; Meng Hwa Er; Alex C. Kot; |
| 25 | Multi-Level Compositional Reasoning for Interactive Instruction Following Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The tasks given to the agents are often composite thus are challenging as completing them require to reason about multiple subtasks, e.g., bring a cup of coffee. To address the challenge, we propose to divide and conquer it by breaking the task into multiple subgoals and attend to them individually for better navigation and interaction. |
Suvaansh Bhambri; Byeonghwi Kim; Jonghyun Choi; |
| 26 | Self-Supervised Image Local Forgery Detection By JPEG Compression Trace Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we firstly analyzed the JPEG compression traces which are mainly caused by different JPEG compression chains, and designed a trace extractor to learn such traces. Then, we utilized the trace extractor as the backbone and trained self-supervised to strengthen the discrimination ability of learned traces. |
Xiuli Bi; Wuqing Yan; Bo Liu; Bin Xiao; Weisheng Li; Xinbo Gao; |
| 27 | VASR: Visual Analogies of Situation Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. |
Yonatan Bitton; Ron Yosef; Eliyahu Strugo; Dafna Shahaf; Roy Schwartz; Gabriel Stanovsky; |
| 28 | Parametric Surface Constrained Upsampler Network for Point Cloud Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these approaches are prone to produce outlier points due to the lack of explicit surface-level constraints. To solve this problem, we introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions, where the new generated points are then constrained on the underlying surface. |
Pingping Cai; Zhenyao Wu; Xinyi Wu; Song Wang; |
| 29 | Explicit Invariant Feature Induced Cross-Domain Crowd Counting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an innovative explicit Invariant Feature induced Cross-domain Knowledge Transformation framework to address the inconsistent domain-invariant features of different domains. |
Yiqing Cai; Lianggangxu Chen; Haoyue Guan; Shaohui Lin; Changhong Lu; Changbo Wang; Gaoqi He; |
| 30 | Painterly Image Harmonization in Dual Domains Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel painterly harmonization network consisting of a dual-domain generator and a dual-domain discriminator, which harmonizes the composite image in both spatial domain and frequency domain. |
Junyan Cao; Yan Hong; Li Niu; |
| 31 | MMTN: Multi-Modal Memory Transformer Network for Image-Report Consistent Medical Report Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they do not fully explore the relationships between multi-modal medical data, and generate inaccurate and inconsistent reports. To address these issues, this paper proposes a Multi-modal Memory Transformer Network (MMTN) to cope with multi-modal medical data for generating image-report consistent medical reports. |
Yiming Cao; Lizhen Cui; Lei Zhang; Fuqiang Yu; Zhen Li; Yonghui Xu; |
| 32 | KT-Net: Knowledge Transfer for Unpaired 3D Shape Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes. In this paper, we propose the novel KTNet to solve this task from the new perspective of knowledge transfer. |
Zhen Cao; Wenxiao Zhang; Xin Wen; Zhen Dong; Yu-Shen Liu; Xiongwu Xiao; Bisheng Yang; |
| 33 | Deconstructed Generation-Based Zero-Shot Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current literature has overlooked the fundamental principles of these methods and has made limited progress in a complex manner. In this paper, we aim to deconstruct the generator-classifier framework and provide guidance for its improvement and extension. |
Dubing Chen; Yuming Shen; Haofeng Zhang; Philip H.S. Torr; |
| 34 | Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we tackle the challenging task of jointly tracking hand object poses and reconstructing their shapes from depth point cloud sequences in the wild, given the initial poses at frame 0. |
Jiayi Chen; Mi Yan; Jiazhao Zhang; Yinzhen Xu; Xiaolong Li; Yijia Weng; Li Yi; Shuran Song; He Wang; |
| 35 | Amodal Instance Segmentation Via Prior-Guided Expansion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a prior-guided expansion framework, which builds on a two-stage segmentation model (i.e., Mask R-CNN) and performs box-level (resp., pixel-level) expansion for amodal box (resp., mask) prediction, by retrieving regression (resp., flow) transformations from a memory bank of expansion prior. |
Junjie Chen; Li Niu; Jianfu Zhang; Jianlou Si; Chen Qian; Liqing Zhang; |
| 36 | SwinRDM: Integrate SwinRNN with Diffusion Model Towards High-Resolution and High-Quality Weather Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to leverage a two-step strategy to achieve high-resolution predictions at 0.25-degree considering the trade-off between computation memory and forecasting accuracy. |
Lei Chen; Fei Du; Yuan Hu; Zhibin Wang; Fan Wang; |
| 37 | Take Your Model Further: A General Post-refinement Network for Light Field Disparity Estimation Via BadPix Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel idea called Bad Pixel (BadPix) correction for method modeling, then implement a general post-refinement network for LF disparity estimation: Bad-pixel Correction Network (BpCNet). |
Rongshan Chen; Hao Sheng; Da Yang; Sizhe Wang; Zhenglong Cui; Ruixuan Cong; |
| 38 | Improving Dynamic HDR Imaging with Fusion Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a transformer model for HDR imaging. |
Rufeng Chen; Bolun Zheng; Hua Zhang; Quan Chen; Chenggang Yan; Gregory Slabaugh; Shanxin Yuan; |
| 39 | Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a self-supervised joint learning framework for optical flow estimation and reconstruction of spiking camera. |
Shiyan Chen; Zhaofei Yu; Tiejun Huang; |
| 40 | Bidirectional Optical Flow NeRF: High Accuracy and High Quality Under Fewer Views Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to the lack of spatial consistency of the single-depth image and the poor performance of depth estimation with fewer views, the existing methods still have challenges in addressing this problem. So this paper proposes Bidirectional Optical Flow NeRF(BOF-NeRF), which addresses this problem by mining optical flow information between 2D images. |
Shuo Chen; Binbin Yan; Xinzhu Sang; Duo Chen; Peng Wang; Xiao Guo; Chongli Zhong; Huaming Wan; |
| 41 | Scalable Spatial Memory for Scene Rendering and Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Scene Memory Network (SMN) to achieve online spatial memory construction and expansion for view rendering in novel scenes. |
Wen-Cheng Chen; Chu-Song Chen; Wei-Chen Chiu; Min-Chun Hu; |
| 42 | Hybrid CNN-Transformer Feature Fusion for Single Image Deraining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, rich local-global information representations are increasingly indispensable for better satisfying rain removal. In this paper, we propose a lightweight Hybrid CNN-Transformer Feature Fusion Network (dubbed as HCT-FFN) in a stage-by-stage progressive manner, which can harmonize these two architectures to help image restoration by leveraging their individual learning strengths. |
Xiang Chen; Jinshan Pan; Jiyang Lu; Zhentao Fan; Hao Li; |
| 43 | MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. |
Yingxian Chen; Zhengzhe Liu; Baoheng Zhang; Wilton Fok; Xiaojuan Qi; Yik-Chung Wu; |
| 44 | Tagging Before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we integrate multi-modal information in an explicit manner by tagging, and use the tags as the anchors for better video-text alignment. |
Yizhen Chen; Jie Wang; Lijian Lin; Zhongang Qi; Jin Ma; Ying Shan; |
| 45 | DUET: Cross-Modal Semantic Grounding for Contrastive Zero-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a transformer-based end-to-end ZSL method named DUET, which integrates latent semantic knowledge from the pre-trained language models (PLMs) via a self-supervised multi-modal learning paradigm. |
Zhuo Chen; Yufeng Huang; Jiaoyan Chen; Yuxia Geng; Wen Zhang; Yin Fang; Jeff Z. Pan; Huajun Chen; |
| 46 | Imperceptible Adversarial Attack Via Invertible Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel Adversarial Attack via Invertible Neural Networks (AdvINN) method to produce robust and imperceptible adversarial examples. |
Zihan Chen; Ziyue Wang; Jun-Jie Huang; Wentao Zhao; Xiao Liu; Dejian Guan; |
| 47 | Cross-Modality Person Re-identification with Memory-Based Contrastive Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an aggregated memory-based cross-modality deep metric learning framework, which benefits from the increasing number of learned modality-aware and modality-agnostic centroid proxies for cluster contrast and mutual information learning. |
De Cheng; Xiaolong Wang; Nannan Wang; Zhen Wang; Xiaoyu Wang; Xinbo Gao; |
| 48 | User-Controllable Arbitrary Style Transfer Via Entropy Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel solution ensuring both efficiency and diversity for generating multiple user-controllable AST results by systematically modulating AST behavior at run-time. |
Jiaxin Cheng; Yue Wu; Ayush Jaiswal; Xu Zhang; Pradeep Natarajan; Prem Natarajan; |
| 49 | Neural Architecture Search for Wide Spectrum Adversarial Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we aim to find Neural Architectures that have improved robustness on a wide range of adversarial noise strengths through Neural Architecture Search. |
Zhi Cheng; Yanxi Li; Minjing Dong; Xiu Su; Shan You; Chang Xu; |
| 50 | Adversarial Alignment for Source Free Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While most existing SFOD methods generate pseudo labels via a source-pretrained model to guide training, these pseudo labels usually contain high noises due to heavy domain discrepancy. In order to obtain better pseudo supervisions, we divide the target domain into source-similar and source-dissimilar parts and align them in the feature space by adversarial learning.Specifically, we design a detection variance-based criterion to divide the target domain. |
Qiaosong Chu; Shuyan Li; Guangyi Chen; Kai Li; Xiu Li; |
| 51 | Weakly Supervised 3D Multi-Person Pose Estimation for Large-Scale Scenes Based on Monocular Camera and Single LiDAR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light. |
Peishan Cong; Yiteng Xu; Yiming Ren; Juze Zhang; Lan Xu; Jingya Wang; Jingyi Yu; Yuexin Ma; |
| 52 | OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous methods that depend on 3D convolution or frequent multi-head self-attention operations bring huge computations. To address this problem, we propose an octree-based Transformer compression method called OctFormer, which does not rely on the occupancy information of sibling nodes. |
Mingyue Cui; Junhua Long; Mingjian Feng; Boyang Li; Huang Kai; |
| 53 | Dual-Domain Attention for Image Deblurring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, to bridge the gaps between degraded/sharp image pairs in the spatial and frequency domains simultaneously, we develop the dual-domain attention mechanism for image deblurring. |
Yuning Cui; Yi Tao; Wenqi Ren; Alois Knoll; |
| 54 | Multi-Resolution Monocular Depth Map Fusion By Self-Supervised Gradient-Based Composition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We find that increasing input resolution is helpful to preserve more local details while the estimation at low resolution is more accurate globally. Therefore, we propose a novel depth map fusion module to combine the advantages of estimations with multi-resolution inputs. |
Yaqiao Dai; Renjiao Yi; Chenyang Zhu; Hongjun He; Kai Xu; |
| 55 | Improving Crowded Object Detection Via Copy-Paste Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). |
Jiangfan Deng; Dewen Fan; Xiaosong Qiu; Feng Zhou; |
| 56 | Defending Backdoor Attacks on Vision Transformer Via Patch Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the best of our knowledge, this paper presents the first defensive strategy that utilizes a unique characteristic of ViTs against backdoor attacks. |
Khoa D. Doan; Yingjie Lao; Peng Yang; Ping Li; |
| 57 | Head-Free Lightweight Semantic Segmentation with Linear Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a head-free lightweight architecture specifically for semantic segmentation, named Adaptive Frequency Transformer (AFFormer). |
Bo Dong; Pichao Wang; Fan Wang; |
| 58 | Hierarchical Contrast for Unsupervised Skeleton-Based Action Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper targets unsupervised skeleton-based action representation learning and proposes a new Hierarchical Contrast (HiCo) framework. |
Jianfeng Dong; Shengkai Sun; Zhonglin Liu; Shujie Chen; Baolong Liu; Xun Wang; |
| 59 | Exploring Tuning Characteristics of Ventral Stream’s Neurons for Few-Shot Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we computationally model two groups of neurons found in ventral stream which are respectively sensitive to shape cues and color cues. |
Lintao Dong; Wei Zhai; Zheng-Jun Zha; |
| 60 | Incremental-DETR: Incremental Few-Shot Object Detection Via Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. |
Na Dong; Yongqiang Zhang; Mingli Ding; Gim Hee Lee; |
| 61 | PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper explores a better prediction target for BERT pre-training of vision transformers. |
Xiaoyi Dong; Jianmin Bao; Ting Zhang; Dongdong Chen; Weiming Zhang; Lu Yuan; Dong Chen; Fang Wen; Nenghai Yu; Baining Guo; |
| 62 | Domain-General Crowd Counting in Unseen Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we instead target to train a model based on a single source domain which can generalize well on any unseen domain. |
Zhipeng Du; Jiankang Deng; Miaojing Shi; |
| 63 | Few-Shot Defect Image Generation Via Defect-Aware Feature Manipulation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose the first defect image generation method in the challenging few-shot cases. |
Yuxuan Duan; Yan Hong; Li Niu; Liqing Zhang; |
| 64 | Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. |
Wan-Cyuan Fan; Yen-Chun Chen; DongDong Chen; Yu Cheng; Lu Yuan; Yu-Chiang Frank Wang; |
| 65 | Target-Free Text-Guided Image Manipulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training. To address this challenging task, we propose a Cyclic-Manipulation GAN (cManiGAN) in this paper, which is able to realize where and how to edit the image regions of interest. |
Wan-Cyuan Fan; Cheng-Fu Yang; Chiao-An Yang; Yu-Chiang Frank Wang; |
| 66 | One Is All: Bridging The Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. |
Shuangkang Fang; Weixin Xu; Heng Wang; Yi Yang; Yufeng Wang; Shuchang Zhou; |
| 67 | Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. |
Zijie Fang; Yang Chen; Yifeng Wang; Zhi Wang; Xiangyang Ji; Yongbing Zhang; |
| 68 | Uncertainty-Aware Image Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an uncertainty-aware image captioning framework, which parallelly and iteratively operates insertion of discontinuous candidate words between existing words from easy to difficult until converged. |
Zhengcong Fei; Mingyuan Fan; Li Zhu; Junshi Huang; Xiaoming Wei; Xiaolin Wei; |
| 69 | Unsupervised Domain Adaptation for Medical Image Segmentation By Selective Entropy Constraints and Adaptive Semantic Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new unsupervised domain adaptation framework for cross-modality medical image segmentation. |
Wei Feng; Lie Ju; Lin Wang; Kaimin Song; Xin Zhao; Zongyuan Ge; |
| 70 | SEFormer: Structure Embedding Transformer for 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Structure-Embedding transFormer (SEFormer), which can not only preserve the local structure as a traditional Transformer but also have the ability to encode the local structure. |
Xiaoyu Feng; Heming Du; Hehe Fan; Yueqi Duan; Yongpan Liu; |
| 71 | Exploit Domain-Robust Optical Flow in Domain Adaptive Video Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we try to find a domain-robust clue to construct more reliable supervision signals. |
Yuan Gao; Zilei Wang; Jiafan Zhuang; Yixin Zhang; Junjie Li; |
| 72 | Scene-Level Sketch-Based Image Retrieval with Minimal Pairwise Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, a more general scene-level SBIR task is explored, where sketches and images can both contain multiple object instances. |
Ce Ge; Jingyu Wang; Qi Qi; Haifeng Sun; Tong Xu; Jianxin Liao; |
| 73 | Causal Intervention for Human Trajectory Prediction with Cross Attention Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on causal intervention rather than conventional likelihood, we propose a Social Environment ADjustment (SEAD) method, to remove the confounding effect of the social environment. |
Chunjiang Ge; Shiji Song; Gao Huang; |
| 74 | Point-Teaching: Weakly Semi-supervised Object Detection with Point Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. |
Yongtao Ge; Qiang Zhou; Xinlong Wang; Chunhua Shen; Zhibin Wang; Hao Li; |
| 75 | Progressive Multi-View Human Mesh Recovery with Self-Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. |
Xuan Gong; Liangchen Song; Meng Zheng; Benjamin Planche; Terrence Chen; Junsong Yuan; David Doermann; Ziyan Wu; |
| 76 | Incremental Image De-raining Via Associative Memory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue the importance of sample diversity in the episodes on the iterative optimization, and propose a novel memory management method, Associative Memory, to achieve incremental image de-raining. |
Yi Gu; Chao Wang; Jie Li; |
| 77 | Flexible 3D Lane Detection By Hierarchical Shape Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds. |
Zhihao Guan; Ruixin Liu; Zejian Yuan; Ao Liu; Kun Tang; Tong Zhou; Erlong Li; Chao Zheng; Shuqi Mei; |
| 78 | Underwater Ranker: Learn Which Is Better and How to Be Better Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. |
Chunle Guo; Ruiqi Wu; Xin Jin; Linghao Han; Weidong Zhang; Zhi Chai; Chongyi Li; |
| 79 | ShadowFormer: Global Context Helps Shadow Removal Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on that, we propose a Shadow-Interaction Module (SIM) with Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model the context correlation between shadow and non-shadow regions. |
Lanqing Guo; Siyu Huang; Ding Liu; Hao Cheng; Bihan Wen; |
| 80 | RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). |
Longwei Guo; Hao Zhu; Yuanxun Lu; Menghua Wu; Xun Cao; |
| 81 | RankDNN: Learning to Rank for Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. |
Qianyu Guo; Gong Haotong; Xujun Wei; Yanwei Fu; Yizhou Yu; Wenqiang Zhang; Weifeng Ge; |
| 82 | Social Relation Reasoning Based on Triangular Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the paradigm of the higher-order constraints in social relations into triangular relational closed-loop structures, i.e., triangular constraints, and further introduce the triangular reasoning graph attention network (TRGAT). |
Yunfei Guo; Fei Yin; Wei Feng; Xudong Yan; Tao Xue; Shuqi Mei; Cheng-Lin Liu; |
| 83 | CALIP: Zero-Shot Enhancement of CLIP with Parameter-Free Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a free-lunch enhancement method, CALIP, to boost CLIP’s zero-shot performance via a parameter-free attention module. |
Ziyu Guo; Renrui Zhang; Longtian Qiu; Xianzheng Ma; Xupeng Miao; Xuming He; Bin Cui; |
| 84 | Few-Shot Object Detection Via Variational Feature Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples. To address this issue, we propose a meta-learning framework with two novel feature aggregation schemes. |
Jiaming Han; Yuqiang Ren; Jian Ding; Ke Yan; Gui-Song Xia; |
| 85 | Generating Transferable 3D Adversarial Point Cloud Via Random Perturbation Factorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we revisit the transferability of adversarial 3D point clouds. |
Bangyan He; Jian Liu; Yiming Li; Siyuan Liang; Jingzhi Li; Xiaojun Jia; Xiaochun Cao; |
| 86 | Target-Aware Tracking with Long-Term Context Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above problem, we propose a long-term context attention (LCA) module that can perform extensive information fusion on the target and its context from long-term frames, and calculate the target correlation while enhancing target features. |
Kaijie He; Canlong Zhang; Sheng Xie; Zhixin Li; Zhiwen Wang; |
| 87 | Weakly-Supervised Camouflaged Object Detection with Scribble Annotations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the first weakly-supervised COD method, using scribble annotations as supervision. |
Ruozhen He; Qihua Dong; Jiaying Lin; Rynson W.H. Lau; |
| 88 | Efficient Mirror Detection Via Multi-Level Heterogeneous Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present HetNet (Multi-level Heterogeneous Network), a highly efficient mirror detection network. |
Ruozhen He; Jiaying Lin; Rynson W.H. Lau; |
| 89 | TransVCL: Attention-Enhanced Video Copy Localization Network with Flexible Supervision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose TransVCL: an attention-enhanced video copy localization network, which is optimized directly from initial frame-level features and trained end-to-end with three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for similarity matrix generation, and a temporal alignment module for copied segments localization. |
Sifeng He; Yue He; Minlong Lu; Chen Jiang; Xudong Yang; Feng Qian; Xiaobo Zhang; Lei Yang; Jiandong Zhang; |
| 90 | Open-Vocabulary Multi-Label Classification Via Multi-Modal Knowledge Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the success of OV-based methods, we propose a novel open-vocabulary framework, named multi-modal knowledge transfer (MKT), for multi-label classification. |
Sunan He; Taian Guo; Tao Dai; Ruizhi Qiao; Xiujun Shu; Bo Ren; Shu-Tao Xia; |
| 91 | Parameter-Efficient Model Adaptation for Vision Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to study parameter-efficient model adaptation strategies for vision transformers on the image classification task. |
Xuehai He; Chunyuan Li; Pengchuan Zhang; Jianwei Yang; Xin Eric Wang; |
| 92 | DarkFeat: Noise-Robust Feature Detector and Descriptor for Extremely Low-Light RAW Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DarkFeat, a deep learning model which directly detects and describes local features from extreme low-light RAW images in an end-to-end manner. |
Yuze He; Yubin Hu; Wang Zhao; Jisheng Li; Yong-Jin Liu; Yuxing Han; Jiangtao Wen; |
| 93 | GAM: Gradient Attention Module of Optimization for Point Clouds Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper argues that fine-grained geometric information (FGGI) plays an important role in the aggregation of local features. Based on this, we propose a gradient-based local attention module to address the above problem, which is called Gradient Attention Module (GAM). |
Haotian Hu; Fanyi Wang; Zhiwang Zhang; Yaonong Wang; Laifeng Hu; Yanhao Zhang; |
| 94 | Self-Supervised Learning for Multilevel Skeleton-Based Forgery Detection Via Temporal-Causal Consistency of Actions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an approach to self-supervised learning of the temporal causality behind human action, which can effectively check TII in skeletal sequences. |
Liang Hu; Dora D. Liu; Qi Zhang; Usman Naseem; Zhong Yuan Lai; |
| 95 | Self-Emphasizing Network for Continuous Sign Language Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a temporal self-emphasizing module to adaptively emphasize those discriminative frames and suppress redundant ones. |
Lianyu Hu; Liqing Gao; Zekang Liu; Wei Feng; |
| 96 | Store and Fetch Immediately: Everything Is All You Need for Space-Time Video Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the limitation, this paper proposes an immediate storeand-fetch network to promote long-range correlation learning, where the stored information from the past and future can be refetched to help the representation of the current frame. |
Mengshun Hu; Kui Jiang; Zhixiang Nie; Jiahuan Zhou; Zheng Wang; |
| 97 | PointCA: Evaluating The Robustness of 3D Point Cloud Completion Models Against Adversarial Examples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. |
Shengshan Hu; Junwei Zhang; Wei Liu; Junhui Hou; Minghui Li; Leo Yu Zhang; Hai Jin; Lichao Sun; |
| 98 | High-Resolution Iterative Feedback Network for Camouflaged Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. |
Xiaobin Hu; Shuo Wang; Xuebin Qin; Hang Dai; Wenqi Ren; Donghao Luo; Ying Tai; Ling Shao; |
| 99 | Leveraging Sub-class Discimination for Compositional Zero-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a simple yet effective approach with leveraging sub-class discrimination. |
Xiaoming Hu; Zilei Wang; |
| 100 | GPTR: Gestalt-Perception Transformer for Diagram Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. |
Xin Hu; Lingling Zhang; Jun Liu; Jinfu Fan; Yang You; Yaqiang Wu; |
| 101 | Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct extensive experiments on CIFAR100 and Ima- geNet100 datasets. |
Bingchen Huang; Zhineng Chen; Peng Zhou; Jiayin Chen; Zuxuan Wu; |
| 102 | ClassFormer: Exploring Class-Aware Dependency with Transformer for Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their strong capability of modeling long-range dependencies, the current methods still give rise to two main concerns in a class-level perspective: (1) intra-class problem: the existing methods lacked in extracting class-specific correspondences of different pixels, which may lead to poor object coverage and/or boundary prediction; (2) inter-class problem: the existing methods failed to model explicit category-dependencies among various objects, which may result in inaccurate localization. In light of these two issues, we propose a novel transformer, called ClassFormer, powered by two appealing transformers, i.e., intra-class dynamic transformer and inter-class interactive transformer, to address the challenge of fully exploration on compactness and discrepancy. |
Huimin Huang; Shiao Xie; Lanfen Lin; Ruofeng Tong; Yen-Wei Chen; Hong Wang; Yuexiang Li; Yawen Huang; Yefeng Zheng; |
| 103 | NLIP: Noise-Robust Language-Image Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to automatically mitigate the impact of noise by solely mining over existing data, we propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. |
Runhui Huang; Yanxin Long; Jianhua Han; Hang Xu; Xiwen Liang; Chunjing Xu; Xiaodan Liang; |
| 104 | Symmetry-Aware Transformer-Based Mirror Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing works mainly focus on integrating the semantic features and structural features to mine specific relations between mirror and non-mirror regions, or introducing mirror properties like depth or chirality to help analyze the existence of mirrors. In this work, we observe that a real object typically forms a loose symmetry relationship with its corresponding reflection in the mirror, which is beneficial in distinguishing mirrors from real objects. |
Tianyu Huang; Bowen Dong; Jiaying Lin; Xiaohui Liu; Rynson W.H. Lau; Wangmeng Zuo; |
| 105 | AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: One of the key problems of current spatial audio rendering methods is the lack of personalization based on different anatomies of individuals, which is essential to produce accurate sound source positions. In this work, we address this problem from an interdisciplinary perspective. |
Xiaoyang Huang; Yanjun Wang; Yang Liu; Bingbing Ni; Wenjun Zhang; Jinxian Liu; Teng Li; |
| 106 | Boosting Point Clouds Rendering Via Radiance Mapping Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. |
Xiaoyang Huang; Yi Zhang; Bingbing Ni; Teng Li; Kai Chen; Wenjun Zhang; |
| 107 | FreeEnricher: Enriching Face Landmarks Without Additional Cost Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though dense facial landmark is highly demanded in various scenarios, e.g., cosmetic medicine and facial beautification, most works only consider sparse face alignment. To address this problem, we present a framework that can enrich landmark density by existing sparse landmark datasets, e.g., 300W with 68 points and WFLW with 98 points. |
Yangyu Huang; Xi Chen; Jongyoo Kim; Hao Yang; Chong Li; Jiaolong Yang; Dong Chen; |
| 108 | PATRON: Perspective-Aware Multitask Model for Referring Expression Grounding Using Embodied Multimodal Cues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To make it exacerbate, these models are often trained on datasets collected in non-embodied settings without nonverbal gestures and curated from an exocentric perspective. To address these issues, in this paper, we present a perspective-aware multitask learning model, called PATRON, for relation and object grounding tasks in embodied settings by utilizing verbal utterances and nonverbal cues. |
Md Mofijul Islam; Alexi Gladstone; Tariq Iqbal; |
| 109 | Unifying Vision-Language Representation Space with Single-Tower Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the hypothesis that an image and caption can be regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. |
Jiho Jang; Chaerin Kong; DongHyeon Jeon; Seonhoon Kim; Nojun Kwak; |
| 110 | Delving Deep Into Pixel Alignment Feature for Accurate Multi-View Human Mesh Recovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Pixel-aligned Feedback Fusion (PaFF) for accurate yet efficient human mesh recovery from multi-view images. |
Kai Jia; Hongwen Zhang; Liang An; Yebin Liu; |
| 111 | Semi-attention Partition for Occluded Person Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a Semi-Attention Partition (SAP) method to learn well-aligned part features for occluded person re-identification (re-ID). |
Mengxi Jia; Yifan Sun; Yunpeng Zhai; Xinhua Cheng; Yi Yang; Ying Li; |
| 112 | Fast Online Hashing with Multi-Label Projection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Fast Online Hashing (FOH) method which only updates the binary codes of a small part of the database. |
Wenzhe Jia; Yuan Cao; Junwei Liu; Jie Gui; |
| 113 | Fourier-Net: Fast Image Registration with Band-Limited Deformation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. |
Xi Jia; Joseph Bartlett; Wei Chen; Siyang Song; Tianyang Zhang; Xinxing Cheng; Wenqi Lu; Zhaowen Qiu; Jinming Duan; |
| 114 | Semi-supervised Deep Large-Baseline Homography Estimation with Progressive Equivalence Constraint Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. |
Hai Jiang; Haipeng Li; Yuhang Lu; Songchen Han; Shuaicheng Liu; |
| 115 | Multi-Modality Deep Network for Extreme Learned Image Compression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. |
Xuhao Jiang; Weimin Tan; Tian Tan; Bo Yan; Liquan Shen; |
| 116 | PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this paper we advocate the exploitation of the Polar coordinate system and propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird’s-eye-view (BEV) taking as input only multi-camera 2D images. |
Yanqin Jiang; Li Zhang; Zhenwei Miao; Xiatian Zhu; Jin Gao; Weiming Hu; Yu-Gang Jiang; |
| 117 | 3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module. |
Zutao Jiang; Guansong Lu; Xiaodan Liang; Jihua Zhu; Wei Zhang; Xiaojun Chang; Hang Xu; |
| 118 | FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to further promote the efficiency of PETL to meet the extreme storage constraint in real-world applications. |
Shibo Jie; Zhi-Hong Deng; |
| 119 | Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It becomes more challenging when the input image contains shadows or specular highlights, which often render an inaccurate estimate of the reflectance layer. Therefore, we propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. |
Yeying Jin; Ruoteng Li; Wenhan Yang; Robby T. Tan; |
| 120 | Weakly-Guided Self-Supervised Pretraining for Temporal Activity Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel weakly-guided self-supervised pretraining method for detection. |
Kumara Kahatapitiya; Zhou Ren; Haoxiang Li; Zhenyu Wu; Michael S. Ryoo; Gang Hua; |
| 121 | Correlation Loss: Enforcing Correlation Between Classification and Localization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by these works, we focus on the correlation between classification and localization and make two main contributions: (i) We provide an analysis about the effects of correlation between classification and localization tasks in object detectors. We identify why correlation affects the performance of various NMS-based and NMS-free detectors, and we devise measures to evaluate the effect of correlation and use them to analyze common detectors. (ii) Motivated by our observations, e.g., that NMS-free detectors can also benefit from correlation, we propose Correlation Loss, a novel plug-in loss function that improves the performance of various object detectors by directly optimizing correlation coefficients: E.g., Correlation Loss on Sparse R-CNN, an NMS-free method, yields 1.6 AP gain on COCO and 1.8 AP gain on Cityscapes dataset. |
Fehmi Kahraman; Kemal Oksuz; Sinan Kalkan; Emre Akbas; |
| 122 | GuidedMixup: An Efficient Mixup Strategy Guided By Saliency Maps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods incur a significant computational burden to optimize the mixup mask. From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead. |
Minsoo Kang; Suhyun Kim; |
| 123 | 3D Human Pose Lifting with Grid Convolution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In sharp contrast to them, this paper presents Grid Convolution (GridConv), mimicking the wisdom of regular convolution operations in image space. |
Yangyuxuan Kang; Yuyang Liu; Anbang Yao; Shandong Wang; Enhua Wu; |
| 124 | Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper systematically studies the impact of mixup under the domain adaptive semantic segmentation task and presents a simple yet effective mixup strategy called Bidirectional Domain Mixup (BDM). |
Daehan Kim; Minseok Seo; Kwanyong Park; Inkyu Shin; Sanghyun Woo; In So Kweon; Dong-Geol Choi; |
| 125 | Frequency Selective Augmentation for Video Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose frequency augmentation (FreqAug), a spatio-temporal data augmentation method in the frequency domain for video representation learning. |
Jinhyung Kim; Taeoh Kim; Minho Shim; Dongyoon Han; Dongyoon Wee; Junmo Kim; |
| 126 | Pose-Guided 3D Human Generation in Indoor Scene Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the problem of scene-aware 3D human avatar generation based on human-scene interactions. |
Minseok Kim; Changwoo Kang; Jeongin Park; Kyungdon Joo; |
| 127 | Semantic-Aware Superpixel for Weakly Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore a self-supervised vision transformer to mitigate the heavy efforts on generation of pixel-level annotations. |
Sangtae Kim; Daeyoung Park; Byonghyo Shim; |
| 128 | Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack Against Multispectral Object Detectors Using Transparent Low-E Films Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the vulnerability of multispectral detectors against physical attacks by proposing a new physical method: Multispectral Invisible Coating. |
Taeheon Kim; Youngjoon Yu; Yong Man Ro; |
| 129 | CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we propose a novel proposal-level early fusion approach that effectively exploits both spatial and contextual properties of camera and radar for 3D object detection. |
Youngseok Kim; Sanmin Kim; Jun Won Choi; Dongsuk Kum; |
| 130 | Simple and Effective Synthesis of Indoor 3D Scenes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. |
Jing Yu Koh; Harsh Agrawal; Dhruv Batra; Richard Tucker; Austin Waters; Honglak Lee; Yinfei Yang; Jason Baldridge; Peter Anderson; |
| 131 | MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel 3D object detection architecture, which can encode LiDAR point cloud sequences acquired by multiple successive scans. |
Junho Koh; Junhyung Lee; Youngwoo Lee; Jaekyum Kim; Jun Won Choi; |
| 132 | InstanceFormer: An Online Video Instance Segmentation Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a single-stage transformer-based efficient online VIS framework named InstanceFormer, which is especially suitable for long and challenging videos. |
Rajat Koner; Tanveer Hannan; Suprosanna Shit; Sahand Sharifzadeh; Matthias Schubert; Thomas Seidl; Volker Tresp; |
| 133 | Pixel-Wise Warping for Deep Image Stitching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. |
Hyeokjun Kweon; Hyeonseong Kim; Yoonsu Kang; Youngho Yoon; WooSeong Jeong; Kuk-Jin Yoon; |
| 134 | Learning to Learn Better for Video Object Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, how to reasonably fuse the target features in the two different branches rather than simply adding them together to avoid the adverse effect of one dominant branch has not been investigated. In this paper, we propose a novel framework that emphasizes Learning to Learn Better (LLB) target features for SVOS, termed LLB, where we design the discriminative label generation module (DLGM) and the adaptive fusion module to address these issues. |
Meng Lan; Jing Zhang; Lefei Zhang; Dacheng Tao; |
| 135 | Curriculum Multi-Negative Augmentation for Debiased Video Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent studies have found that current VG models are prone to over-rely the groundtruth moment annotation distribution biases in the training set. To discourage the standard VG model’s behavior of exploiting such temporal annotation biases and improve the model generalization ability, we propose multiple negative augmentations in a hierarchical way, including cross-video augmentations from clip-/video-level, and self-shuffled augmentations with masks. |
Xiaohan Lan; Yitian Yuan; Hong Chen; Xin Wang; Zequn Jie; Lin Ma; Zhi Wang; Wenwu Zhu; |
| 136 | Weakly Supervised 3D Segmentation Via Receptive-Driven Pseudo Label Consistency and Structural Consistency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a weakly supervised point cloud semantic segmentation framework via receptive-driven pseudo label consistency and structural consistency to mine potential knowledge. |
Yuxiang Lan; Yachao Zhang; Yanyun Qu; Cong Wang; Chengyang Li; Jia Cai; Yuan Xie; Zongze Wu; |
| 137 | MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present MultiAct, the first framework to generate long-term 3D human motion from multiple action labels. |
Taeryung Lee; Gyeongsik Moon; Kyoung Mu Lee; |
| 138 | Not All Neighbors Matter: Point Distribution-Aware Pruning for 3D Point Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new weight pruning technique for 3D point cloud based on spatial point distribution. |
Yejin Lee; Donghyun Lee; JungUk Hong; Jae W. Lee; Hongil Yoon; |
| 139 | Symbolic Replay: Scene Graph As Prompt for Continual Learning on VQA Task Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we propose a real-data-free replay-based method tailored for CL on VQA, named Scene Graph as Prompt for Symbolic Replay. |
Stan Weixian Lei; Difei Gao; Jay Zhangjie Wu; Yuxuan Wang; Wei Liu; Mengmi Zhang; Mike Zheng Shou; |
| 140 | Linking People Across Text and Images Based on Social Relation Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe that humans are adept at exploring social relations to assist identifying people. Therefore, we propose a Social Relation Reasoning (SRR) model to address the aforementioned issues. |
Yang Lei; Peizhi Zhao; Pijian Li; Yi Cai; Qingbao Huang; |
| 141 | ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing encoder-based or optimization-based StyleGAN inversion methods attempt to mitigate the trade-off but see limited performance. To fundamentally resolve this problem, we propose a novel two-phase framework by designating two separate networks to tackle editing and reconstruction respectively, instead of balancing the two. |
Bingchuan Li; Tianxiang Ma; Peng Zhang; Miao Hua; Wei Liu; Qian He; Zili Yi; |
| 142 | SWBNet: A Stable White Balance Network for SRGB Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The white balance methods for sRGB images (sRGB-WB) aim to directly remove their color temperature shifts. Despite achieving promising white balance (WB) performance, the existing methods suffer from WB instability, i.e., their results are inconsistent for images with different color temperatures. We propose a stable white balance network (SWBNet) to alleviate this problem. |
Chunxiao Li; Xuejing Kang; Zhifeng Zhang; Anlong Ming; |
| 143 | Frequency Domain Disentanglement for Arbitrary Neural Style Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, these methods always suffer from low-quality results because of the sub-optimal disentanglement. To address such a challenge, this paper proposes the frequency mixer (FreMixer) module that disentangles and re-entangles the frequency spectrum of content and style components in the frequency domain. |
Dongyang Li; Hao Luo; Pichao Wang; Zhibin Wang; Shang Liu; Fan Wang; |
| 144 | Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. |
Han Li; Bowen Shi; Wenrui Dai; Hongwei Zheng; Botao Wang; Yu Sun; Min Guo; Chenglin Li; Junni Zou; Hongkai Xiong; |
| 145 | CEE-Net: Complementary End-to-End Network for 3D Human Pose Generation and Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the CEE-Net, a Complementary End-to-End Network for 3D human pose generation and estimation. |
Haolun Li; Chi-Man Pun; |
| 146 | Real-World Deep Local Motion Deblurring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. |
Haoying Li; Ziran Zhang; Tingting Jiang; Peng Luo; Huajun Feng; Zhihai Xu; |
| 147 | Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Following the theoretical guidance, we propose a backdoor adjustment-based knowledge distillation method for the few-shot object detection task, namely Disentangle and Remerge (D&R), to perform conditional causal intervention toward the corresponding Structural Causal Model. |
Jiangmeng Li; Yanan Zhang; Wenwen Qiang; Lingyu Si; Chengbo Jiao; Xiaohui Hu; Changwen Zheng; Fuchun Sun; |
| 148 | Learning Motion-Robust Remote Photoplethysmography Through Arbitrary Resolution Videos Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Different from the previous rPPG models designed for a constant distance between camera and participants, in this paper, we propose two plug-and-play blocks (i.e., physiological signal feature extraction block (PFE) and temporal face alignment block (TFA)) to alleviate the degradation of changing distance and head motion. |
Jianwei Li; Zitong Yu; Jingang Shi; |
| 149 | FSR: A General Frequency-Oriented Framework to Accelerate Image Super-resolution Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods usually require substantial computations by operating in spatial domain. To address this issue, we propose a general frequency-oriented framework (FSR) to accelerate SR networks by considering data characteristics in frequency domain. |
Jinmin Li; Tao Dai; Mingyan Zhu; Bin Chen; Zhi Wang; Shu-Tao Xia; |
| 150 | Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. |
Kaicheng Li; Hongyu Yang; Binghui Chen; Pengyu Li; Biao Wang; Di Huang; |
| 151 | Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. |
Meng Li; Yahan Yu; Yi Yang; Guanghao Ren; Jian Wang; |
| 152 | Spatial-Spectral Transformer for Hyperspectral Image Denoising Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, we propose a Spatial-Spectral Transformer (SST) to alleviate this problem. |
Miaoyu Li; Ying Fu; Yulun Zhang; |
| 153 | Learning Semantic Alignment with Global Modality Reconstruction for Video-Language Pre-training Towards Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the problem, we propose a video-language pre-training framework, termed videolanguage pre-training For lEarning sEmantic aLignments (FEEL), to learn semantic alignments at the sequence level. |
Mingchao Li; Xiaoming Shi; Haitao Leng; Wei Zhou; Hai-Tao Zheng; Kuncai Zhang; |
| 154 | Layout-Aware Dreamer for Embodied Visual Referring Expression Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the problem of Embodied Referring Expression Grounding, where an agent needs to navigate in a previously unseen environment and localize a remote object described by a concise high-level natural language instruction. |
Mingxiao Li; Zehao Wang; Tinne Tuytelaars; Marie-Francine Moens; |
| 155 | NeAF: Learning Neural Angle Fields for Point Normal Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods are not generalized well to unseen scenarios and are sensitive to parameter settings. To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF). |
Shujuan Li; Junsheng Zhou; Baorui Ma; Yu-Shen Liu; Zhizhong Han; |
| 156 | CLIP-ReID: Exploiting Vision-Language Model for Image Re-identification Without Concrete Text Labels Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper first finds out that simply fine-tuning the visual model initialized by the image encoder in CLIP, has already obtained competitive performances in various ReID tasks. Then we propose a two-stage strategy to facilitate a better visual representation. |
Siyuan Li; Li Sun; Qingli Li; |
| 157 | DC-Former: Diverse and Compact Transformer for Person Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Diverse and Compact Transformer (DC-Former) that can achieve a similar effect by splitting embedding space into multiple diverse and compact subspaces. |
Wen Li; Cheng Zou; Meng Wang; Furong Xu; Jianan Zhao; Ruobing Zheng; Yuan Cheng; Wei Chu; |
| 158 | Panoramic Video Salient Object Detection with Ambisonic Audio Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios. |
Xiang Li; Haoyuan Cao; Shijie Zhao; Junlin Li; Li Zhang; Bhiksha Raj; |
| 159 | LWSIS: LiDAR-Guided Weakly Supervised Instance Segmentation for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i.e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models. |
Xiang Li; Junbo Yin; Botian Shi; Yikang Li; Ruigang Yang; Jianbing Shen; |
| 160 | Adaptive Texture Filtering for Single-Domain Generalized Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. |
Xinhui Li; Mingjia Li; Yaxing Wang; Chuan-Xian Ren; Xiaojie Guo; |
| 161 | MEID: Mixture-of-Experts with Internal Distillation for Long-Tailed Video Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To highlight the multi-label challenge in long-tailed video recognition, we create two additional benchmarks based on Charades and CharadesEgo videos with the multi-label property, called CharadesLT and CharadesEgoLT. |
Xinjie Li; Huijuan Xu; |
| 162 | Gradient Corner Pooling for Keypoint-Based Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a method named Gradient Corner Pooling. |
Xuyang Li; Xuemei Xie; Mingxuan Yu; Jiakai Luo; Chengwei Rao; Guangming Shi; |
| 163 | Towards Real-Time Segmentation on The Edge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to combine the self attention block with lightweight convolutions to form new building blocks, and employ latency constraints to search an efficient sub-network. |
Yanyu Li; Changdi Yang; Pu Zhao; Geng Yuan; Wei Niu; Jiexiong Guan; Hao Tang; Minghai Qin; Qing Jin; Bin Ren; Xue Lin; Yanzhi Wang; |
| 164 | BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird’s-Eye-View~(BEV) 3D object detection. |
Yinhao Li; Zheng Ge; Guanyi Yu; Jinrong Yang; Zengran Wang; Yukang Shi; Jianjian Sun; Zeming Li; |
| 165 | BEVStereo: Enhancing Depth Estimation in Multi-View 3D Object Detection with Temporal Stereo Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose an effective method for creating temporal stereo by dynamically determining the center and range of the temporal stereo. |
Yinhao Li; Han Bao; Zheng Ge; Jinrong Yang; Jianjian Sun; Zeming Li; |
| 166 | Learning Single Image Defocus Deblurring with Misaligned Training Pairs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a joint deblurring and reblurring learning (JDRL) framework for single image defocus deblurring with misaligned training pairs. |
Yu Li; Dongwei Ren; Xinya Shu; Wangmeng Zuo; |
| 167 | Curriculum Temperature for Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple curriculum-based technique, termed Curriculum Temperature for Knowledge Distillation (CTKD), which controls the task difficulty level during the student’s learning career through a dynamic and learnable temperature. |
Zheng Li; Xiang Li; Lingfeng Yang; Borui Zhao; Renjie Song; Lei Luo; Jun Li; Jian Yang; |
| 168 | Actionness Inconsistency-Guided Contrastive Learning for Weakly-Supervised Temporal Action Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel Actionness Inconsistency-guided Contrastive Learning (AICL) method which utilizes the consistent segments to boost the representation learning of the inconsistent segments. |
Zhilin Li; Zilei Wang; Qinying Liu; |
| 169 | READ: Large-Scale Neural Scene Rendering for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to generate large-scale driving scenes in real time on a PC through a variety of sampling schemes. |
Zhuopeng Li; Lu Li; Jianke Zhu; |
| 170 | CDTA: A Cross-Domain Transfer-Based Attack with Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a Cross-Domain Transfer-Based Attack (CDTA), which works in the cross-domain scenario. |
Zihan Li; Weibin Wu; Yuxin Su; Zibin Zheng; Michael R. Lyu; |
| 171 | HybridCap: Inertia-Aid Monocular Capture of Challenging Human Motions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a light-weight, hybrid mocap technique called HybridCap that augments the camera with only 4 Inertial Measurement Units (IMUs) in a novel learning-and-optimization framework. |
Han Liang; Yannan He; Chengfeng Zhao; Mutian Li; Jingya Wang; Jingyi Yu; Lan Xu; |
| 172 | Global Dilated Attention and Target Focusing Network for Robust Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it brings two challenges: First, its global receptive field has less attention on local structure and inter-channel associations, which limits the semantics to distinguish objects and backgrounds; Second, its feature fusion with linear process cannot avoid the interference of non-target semantic objects. To solve the above issues, this paper proposes a robust tracking method named GdaTFT by defining the Global Dilated Attention (GDA) and Target Focusing Network (TFN). |
Yun Liang; Qiaoqiao Li; Fumian Long; |
| 173 | Only A Few Classes Confusing: Pixel-Wise Candidate Labels Disambiguation for Foggy Scene Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The true semantics of most pixels have a high likelihood of appearing in the few top classes according to confidence ranking. In this paper, we replace the one-hot pseudo label with a candidate label set (CLS) that consists of only a few ambiguous classes and exploit its effects on self-training-based unsupervised domain adaptation. |
Liang Liao; Wenyi Chen; Zhen Zhang; Jing Xiao; Yan Yang; Chia-Wen Lin; Shin’ichi Satoh; |
| 174 | Actional Atomic-Concept Learning for Demystifying Vision-Language Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Actional Atomic-Concept Learning (AACL), which maps visual observations to actional atomic concepts for facilitating the alignment. |
Bingqian Lin; Yi Zhu; Xiaodan Liang; Liang Lin; Jianzhuang Liu; |
| 175 | Probability Guided Loss for Long-Tailed Multi-Label Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Long-tailed multi-label image classification is one subtask and remains challenging and poorly researched. In this paper, we provide a fresh perspective from probability to tackle this problem. |
Dekun Lin; |
| 176 | Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We devise a new regularization for denoising with self-supervised learning. |
Huangxing Lin; Yihong Zhuang; Xinghao Ding; Delu Zeng; Yue Huang; Xiaotong Tu; John Paisley; |
| 177 | Accelerating The Training of Video Super-resolution Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that it is possible to gradually train video models from small to large spatial/temporal sizes, \ie, in an easy-to-hard manner. |
Lijian Lin; Xintao Wang; Zhongang Qi; Ying Shan; |
| 178 | SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective approach, dubbed SelectAugment, to select samples for augmentation in a deterministic and online manner based on the sample contents and the network training status. |
Shiqi Lin; Zhizheng Zhang; Xin Li; Zhibo Chen; |
| 179 | AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-Realistic Style Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the Adaptive ColorMLP (AdaCM), an effective and efficient framework for universal photo-realistic style transfer. |
Tianwei Lin; Honglin Lin; Fu Li; Dongliang He; Wenhao Wu; Meiling Wang; Xin Li; Yong Liu; |
| 180 | SEPT: Towards Scalable and Efficient Visual Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. |
Yiqi Lin; Huabin Zheng; Huaping Zhong; Jinjing Zhu; Weijia Li; Conghui He; Lin Wang; |
| 181 | Cross-Modality Earth Mover’s Distance for Visible Thermal Person Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Cross-Modality Earth Mover’s Distance (CM-EMD) that can alleviate the impact of the intra-identity variations during modality alignment. |
Yongguo Ling; Zhun Zhong; Zhiming Luo; Fengxiang Yang; Donglin Cao; Yaojin Lin; Shaozi Li; Nicu Sebe; |
| 182 | Hypotheses Tree Building for One-Shot Temporal Sentence Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we target another more practical and challenging setting: one-shot temporal sentence localization (one-shot TSL), which learns to retrieve the query information among the entire video with only one annotated frame. |
Daizong Liu; Xiang Fang; Pan Zhou; Xing Di; Weining Lu; Yu Cheng; |
| 183 | The Devil Is in The Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Different from them, we shift the perspective to the Fourier domain which naturally has global perspective and present a new Masked Image Modeling (MIM), termed Geminated Gestalt Autoencoder (Ge^2-AE) for visual pre-training. |
Hao Liu; Xinghua Jiang; Xin Li; Antai Guo; Yiqing Hu; Deqiang Jiang; Bo Ren; |
| 184 | M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel two-stage framework for brain tumor segmentation with missing modalities. |
Hong Liu; Dong Wei; Donghuan Lu; Jinghan Sun; Liansheng Wang; Yefeng Zheng; |
| 185 | From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to design a new attention block whose insights are from the interpretation of Local Attribution Map (LAM) for SR networks. |
Jie Liu; Chao Chen; Jie Tang; Gangshan Wu; |
| 186 | Fast Fluid Simulation Via Dynamic Multi-Scale Gridding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent works on learning-based frameworks for Lagrangian (i.e., particle-based) fluid simulation, though bypassing iterative pressure projection via efficient convolution operators, are still time-consuming due to excessive amount of particles. To address this challenge, we propose a dynamic multi-scale gridding method to reduce the magnitude of elements that have to be processed, by observing repeated particle motion patterns within certain consistent regions. |
Jinxian Liu; Ye Chen; Bingbing Ni; Wei Ren; Zhenbo Yu; Xiaoyang Huang; |
| 187 | TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we rethink the structure of point transformer. |
Jiuming Liu; Guangming Wang; Chaokang Jiang; Zhe Liu; Hesheng Wang; |
| 188 | Low-Light Video Enhancement with Synthetic Event Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, inspired by the low latency and high dynamic range of events, we use synthetic events from multiple frames to guide the enhancement and restoration of low-light videos. |
Lin Liu; Junfeng An; Jianzhuang Liu; Shanxin Yuan; Xiangyu Chen; Wengang Zhou; Houqiang Li; Yan Feng Wang; Qi Tian; |
| 189 | Novel Motion Patterns Matter for Practical Skeleton-Based Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As it is laborious to collect sufficient training samples to enumerate various types of novel motion patterns, this paper presents a practical skeleton-based action recognition task where the training set contains common motion patterns of action samples and the test set contains action samples that suffer from novel motion patterns. |
Mengyuan Liu; Fanyang Meng; Chen Chen; Songtao Wu; |
| 190 | EMEF: Ensemble Multi-Exposure Image Fusion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the MEF problem from a new perspective. |
Renshuai Liu; Chengyang Li; Haitao Cao; Yinglin Zheng; Ming Zeng; Xuan Cheng; |
| 191 | Reducing Domain Gap in Frequency and Spatial Domain for Cross-Modality Domain Adaptation on Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple yet effective UDA method based on frequency and spatial domain transfer under multi-teacher distillation framework. |
Shaolei Liu; Siqi Yin; Linhao Qu; Manning Wang; |
| 192 | DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the problem of visual grounding by considering both phrase extraction and grounding (PEG). |
Shilong Liu; Shijia Huang; Feng Li; Hao Zhang; Yaoyuan Liang; Hang Su; Jun Zhu; Lei Zhang; |
| 193 | Progressive Neighborhood Aggregation for Semantic Segmentation Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate a progressive neighborhood aggregation (PNA) framework to refine the semantic segmentation prediction, resulting in an end-to-end solution that can perform the coarse prediction and refinement in a unified network. |
Ting Liu; Yunchao Wei; Yanning Zhang; |
| 194 | CoordFill: Efficient High-Resolution Image Inpainting Via Parameterized Coordinate Querying Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Image inpainting aims to fill the missing hole of the input. It is hard to solve this task efficiently when facing high-resolution images due to two reasons: (1) Large reception field needs to be handled for high-resolution image inpainting. (2) The general encoder and decoder network synthesizes many background pixels synchronously due to the form of the image matrix. In this paper, we try to break the above limitations for the first time thanks to the recent development of continuous implicit representation. |
Weihuang Liu; Xiaodong Cun; Chi-Man Pun; Menghan Xia; Yong Zhang; Jue Wang; |
| 195 | CCQ: Cross-Class Query Network for Partially Labeled Organ Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing algorithms of multi-organ segmentation on partially-labeled datasets neglect the semantic relations and anatomical priors between different categories of organs, which is crucial for partially-labeled multi-organ segmentation. In this paper, we tackle the limitations above by proposing the Cross-Class Query Network (CCQ). |
Xuyang Liu; Bingbing Wen; Sibei Yang; |
| 196 | Counterfactual Dynamics Forecasting – A New Setting of Quantitative Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on it, we propose a method to infer counterfactual dynamics considering the factual dynamics as demonstration. |
Yanzhu Liu; Ying Sun; Joo-Hwee Lim; |
| 197 | Self-Decoupling and Ensemble Distillation for Efficient Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, most of the Self-KD algorithms are specific to classification tasks based on soft-labels, and not suitable for semantic segmentation. To alleviate these contradictions, we revisit the label and feature distillation problem in segmentation, and propose Self-Decoupling and Ensemble Distillation for Efficient Segmentation (SDES). |
Yuang Liu; Wei Zhang; Jun Wang; |
| 198 | Token Mixing: Parameter-Efficient Transfer Learning from Image-Language to Video-Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, based on our analysis of the core ideas of different temporal modeling components in existing approaches, we propose a token mixing strategy to enable cross-frame interactions, which enables transferring from the pre-trained image-language model to video-language tasks through selecting and mixing a key set and a value set from the input video samples. |
Yuqi Liu; Luhui Xu; Pengfei Xiong; Qin Jin; |
| 199 | StereoDistill: Pick The Cream from LiDAR for Distilling Stereo-Based 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation. |
Zhe Liu; Xiaoqing Ye; Xiao Tan; Errui Ding; Xiang Bai; |
| 200 | Good Helper Is Around You: Attention-Driven Masked Image Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Attention-driven Masking and Throwing Strategy (AMT), which could solve both problems above. |
Zhengqi Liu; Jie Gui; Hao Luo; |
| 201 | RADIANT: Radar-Image Association Network for 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, leveraging radar depths is hampered by difficulties in precisely associating radar returns with 3D estimates from monocular methods, effectively erasing its benefits. This paper proposes a fusion network that addresses this radar-camera association challenge. |
Yunfei Long; Abhinav Kumar; Daniel Morris; Xiaoming Liu; Marcos Castro; Punarjay Chakravarty; |
| 202 | CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation Via Centrifugal Reference Frame Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the CRIN, namely Centrifugal Rotation-Invariant Network. |
Yujing Lou; Zelin Ye; Yang You; Nianjuan Jiang; Jiangbo Lu; Weiming Wang; Lizhuang Ma; Cewu Lu; |
| 203 | See Your Emotion from Gait Using Unlabeled Skeleton Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Cross-coordinate contrastive learning framework utilizing Ambiguity samples for self-supervised Gait-based Emotion representation (CAGE). |
Haifeng Lu; Xiping Hu; Bin Hu; |
| 204 | Learning Progressive Modality-Shared Transformers for Effective Visible-Infrared Person Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. |
Hu Lu; Xuezhang Zou; Pingping Zhang; |
| 205 | Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an Information-Coupled Prototype Elaboration (ICPE) method to generate specific and representative prototypes for each query image. |
Xiaonan Lu; Wenhui Diao; Yongqiang Mao; Junxi Li; Peijin Wang; Xian Sun; Kun Fu; |
| 206 | ParaFormer: Parallel Attention Transformer for Efficient Feature Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are expected to be matched. This paper tackles this problem and proposes two concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a graph based U-Net architecture with attentional pooling. |
Xiaoyong Lu; Yaping Yan; Bin Kang; Songlin Du; |
| 207 | Robust One-Shot Segmentation of Brain Tissues Via Image-Aligned Style Transformation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues. |
Jinxin Lv; Xiaoyu Zeng; Sheng Wang; Ran Duan; Zhiwei Wang; Qiang Li; |
| 208 | HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. |
Jiefeng Ma; Jun Du; Pengfei Hu; Zhenrong Zhang; Jianshu Zhang; Huihui Zhu; Cong Liu; |
| 209 | Semantic 3D-Aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a Compositional Neural Radiance Field (CNeRF) for semantic 3D-aware portrait synthesis and manipulation. |
Tianxiang Ma; Bingchuan Li; Qian He; Jing Dong; Tieniu Tan; |
| 210 | CFFT-GAN: Cross-Domain Feature Fusion Transformer for Exemplar-Based Image Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a more general learning approach by considering two domain features as a whole and learning both inter-domain correspondence and intra-domain potential information interactions. |
Tianxiang Ma; Bingchuan Li; Wei Liu; Miao Hua; Jing Dong; Tieniu Tan; |
| 211 | StyleTalk: One-Shot Talking Head Generation with Controllable Speaking Styles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. |
Yifeng Ma; Suzhen Wang; Zhipeng Hu; Changjie Fan; Tangjie Lv; Yu Ding; Zhidong Deng; Xin Yu; |
| 212 | Intriguing Findings of Frequency Selection for Image Deblurring Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). |
Xintian Mao; Yiming Liu; Fengze Liu; Qingli Li; Wei Shen; Yan Wang; |
| 213 | DocEdit: Language-Guided Document Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a new task of language-guided localized document editing, where the user provides a document and an open vocabulary editing request, and the intelligent system produces a command that can be used to automate edits in real-world document editing software. |
Puneet Mathur; Rajiv Jain; Jiuxiang Gu; Franck Dernoncourt; Dinesh Manocha; Vlad I. Morariu; |
| 214 | Progressive Few-Shot Adaptation of Generative Model with Align-Free Spatial Correlation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, it can bring visual artifacts if source and target domain images are not nicely aligned. In this paper, we propose a few-shot generative model adaptation method free from such assumption, based on a motivation that generative models are progressively adapting from the source domain to the target domain. |
Jongbo Moon; Hyunjun Kim; Jae-Pil Heo; |
| 215 | Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A dramatic increase in real-world video volume with extremely diverse and emerging topics naturally forms a long-tailed video distribution in terms of their categories, and it spotlights the need for Video Long-Tailed Recognition (VLTR). In this work, we summarize the challenges in VLTR and explore how to overcome them. |
WonJun Moon; Hyun Seok Seong; Jae-Pil Heo; |
| 216 | Show, Interpret and Tell: Entity-Aware Contextualised Image Captioning in Wikipedia Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the novel task of captioning Wikipedia images by integrating contextual knowledge. |
Khanh Nguyen; Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas; |
| 217 | TaCo: Textual Attribute Recognition Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, their performance drop severely in real-world scenarios where unexpected and obvious imaging distortions appear. In this paper, we aim to tackle these problems by proposing TaCo, a contrastive framework for textual attribute recognition tailored toward the most common document scenes. |
Chang Nie; Yiqing Hu; Yanqiu Qu; Hao Liu; Deqiang Jiang; Bo Ren; |
| 218 | GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, seed points with different importance are treated equally in the voting process, aggravating this defect. To address these issues, we propose a novel global-local transformer voting scheme to provide more informative cues and guide the model pay more attention on potential seed points, promoting the generation of high-quality 3D proposals. |
Jiahao Nie; Zhiwei He; Yuxiang Yang; Mingyu Gao; Jing Zhang; |
| 219 | Adapting Object Size Variance and Class Imbalance for Semi-supervised Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering different object classes usually have different detection difficulty levels due to scale variance and data distribution imbalance, conventional pseudo-labeling-based methods are arduous to explore the value of unlabeled data sufficiently. To address these issues, we propose an adaptive pseudo labeling strategy, which can assign thresholds to classes with respect to their “hardness”. |
Yuxiang Nie; Chaowei Fang; Lechao Cheng; Liang Lin; Guanbin Li; |
| 220 | MIMO Is All You Need：A Strong Multi-in-Multi-Out Baseline for Video Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by that, we conduct a comprehensive investigation in this paper to thoroughly exploit how far a simple MIMO architecture can go. |
Shuliang Ning; Mengcheng Lan; Yanran Li; Chaofeng Chen; Qian Chen; Xunlai Chen; Xiaoguang Han; Shuguang Cui; |
| 221 | Universe Points Representation Learning for Partial Multi-Graph Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the more general partial matching problem with multi-graph cycle consistency guarantees. |
Zhakshylyk Nurlanov; Frank R. Schmidt; Florian Bernard; |
| 222 | Robust Image Denoising of No-Flash Images Guided By Consistent Flash Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a learning-based technique that robustly fuses the image pairs while considering their inconsistency. |
Geunwoo Oh; Jonghee Back; Jae-Pil Heo; Bochang Moon; |
| 223 | Coarse2Fine: Local Consistency Aware Re-prediction for Weakly Supervised Object Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Local Consistency Aware Re-prediction (LCAR) framework, which aims to recover the complete fine object mask from locally inconsistent activation map and hence obtain a tight bounding box. |
Yixuan Pan; Yao Yao; Yichao Cao; Chongjin Chen; Xiaobo Lu; |
| 224 | Find Beauty in The Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel Contrastive Composition Clustering (C2C) to regularize the composition features by contrasting dynamically established similar and dissimilar pairs. |
Zhiyu Pan; Yinpeng Chen; Jiale Zhang; Hao Lu; Zhiguo Cao; Weicai Zhong; |
| 225 | Domain Decorrelation with Potential Energy Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Behind this, domain shift is one of the primary factors to be blamed. In order to tackle this problem, we propose using Potential Energy Ranking (PoER) to decouple the object feature and the domain feature in given images, promoting the learning of label-discriminative representations while filtering out the irrelevant correlations between the objects and the background. |
Sen Pei; Jiaxi Sun; Richard Yi Da Xu; Shiming Xiang; Gaofeng Meng; |
| 226 | PDRF: Progressively Deblurring Radiance Field for Fast Scene Reconstruction from Blurry Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Progressively Deblurring Radiance Field (PDRF), a novel approach to efficiently reconstruct high quality radiance fields from blurry images. |
Cheng Peng; Rama Chellappa; |
| 227 | Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a new method for end-to-end Video Question Answering (VideoQA), aside from the current popularity of using large-scale pre-training with huge feature extractors. |
Min Peng; Chongyang Wang; Yu Shi; Xiang-Dong Zhou; |
| 228 | CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation with disparate point densities and point arrangements. By exploring domain-invariant 3D geometric characteristics and motion patterns, we present an unsupervised domain adaptation method that overcomes above difficulties. |
Xidong Peng; Xinge Zhu; Yuexin Ma; |
| 229 | Better and Faster: Adaptive Event Conversion for Event-Based Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on building better and faster event-based object detectors. |
Yansong Peng; Yueyi Zhang; Peilin Xiao; Xiaoyan Sun; Feng Wu; |
| 230 | CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the structured sparse models obtained by the existing works suffer severe performance degradation for both benign and robust accuracy, thereby causing a challenging dilemma between robustness and structuredness of compact DNNs. To address this problem, in this paper, we propose CSTAR, an efficient solution that simultaneously impose Compactness, high STructuredness and high Adversarial Robustness on the target DNN models. |
Huy Phan; Miao Yin; Yang Sui; Bo Yuan; Saman Zonouz; |
| 231 | Exploring Stochastic Autoregressive Image Modeling for Visual Representation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we try to find the reason why autoregressive modeling does not work well on vision tasks. |
Yu Qi; Fan Yang; Yousong Zhu; Yufei Liu; Liwei Wu; Rui Zhao; Wei Li; |
| 232 | Context-Aware Transformer for 3D Point Cloud Automatic Annotation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a simple yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box labeler to generate precise 3D box annotations from 2D boxes, trained with a small number of human annotations. |
Xiaoyan Qian; Chang Liu; Xiaojuan Qi; Siew-Chong Tan; Edmund Lam; Ngai Wong; |
| 233 | Data-Efficient Image Quality Assessment with Attention-Panel Decoder Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data. |
Guanyi Qin; Runze Hu; Yutao Liu; Xiawu Zheng; Haotian Liu; Xiu Li; Yan Zhang; |
| 234 | FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a Few-shot guided Prototypical (FoPro) representation learning method, which only needs a few labeled examples from reality and can significantly improve the performance in the real-world domain. |
Yulei Qin; Xingyu Chen; Chao Chen; Yunhang Shen; Bo Ren; Yun Gu; Jie Yang; Chunhua Shen; |
| 235 | Exposing The Self-Supervised Space-Time Correspondence Learning Via Graph Kernels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the VideoHiGraph, a space-time correspondence framework based on a learnable graph kernel. |
Zheyun Qin; Xiankai Lu; Xiushan Nie; Yilong Yin; Jianbing Shen; |
| 236 | Exploring Stroke-Level Modifications for Scene Text Editing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. |
Yadong Qu; Qingfeng Tan; Hongtao Xie; Jianjun Xu; YuXin Wang; Yongdong Zhang; |
| 237 | Unsupervised Deep Learning for Phase Retrieval Via Teacher-Student Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the challenge of collecting ground-truth (GT) images in many domains, this paper proposes a fully-unsupervised learning approach for PR, which trains an end-to-end deep model via a GT-free teacher-student online distillation framework. |
Yuhui Quan; Zhile Chen; Tongyao Pang; Hui Ji; |
| 238 | A Learnable Radial Basis Positional Embedding for Coordinate-MLPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel method to enhance the performance of coordinate-MLPs (also referred to as neural fields) by learning instance-specific positional embeddings. |
Sameera Ramasinghe; Simon Lucey; |
| 239 | Action-Conditioned Generation of Bimanual Object Manipulation Sequences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We evaluate our approach on the KIT Motion Capture and KIT RGBD Bimanual Manipulation datasets and show improvements over a simplified approach that treats the entire body as a single entity, and existing whole-body-only methods. |
Haziq Razali; Yiannis Demiris; |
| 240 | Mean-Shifted Contrastive Loss for Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We take the approach of transferring representations pre-trained on external datasets for anomaly detection. |
Tal Reiss; Yedid Hoshen; |
| 241 | Two Heads Are Better Than One: Image-Point Cloud Network for Depth-Based 3D Hand Pose Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an Image-Point cloud Network (IPNet) for accurate and robust 3D hand pose estimation. |
Pengfei Ren; Yuchen Chen; Jiachang Hao; Haifeng Sun; Qi Qi; Jingyu Wang; Jianxin Liao; |
| 242 | MAGIC: Mask-Guided Image Synthesis By Inverting A Quasi-robust Classifier Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. |
Mozhdeh Rouhsedaghat; Masoud Monajatipoor; C.-C. Jay Kuo; Iacopo Masi; |
| 243 | Domain Generalised Faster R-CNN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is the first paper to address domain generalisation in the context of object detection, with a rigorous mathematical analysis of domain shift, without the covariate shift assumption. |
Karthik Seemakurthy; Charles Fox; Erchan Aptoula; Petra Bosilj; |
| 244 | MIDMs: Matching Interleaved Diffusion Models for Exemplar-Based Image Translation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). |
Junyoung Seo; Gyuseong Lee; Seokju Cho; Jiyoung Lee; Seungryong Kim; |
| 245 | JR2Net: Joint Monocular 3D Face Reconstruction and Reenactment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose a novel cascade framework named JR2Net for Joint Face Reconstruction and Reenactment, which begins with the training of a coarse reconstruction network, followed by a 3D-aware face reenactment network based on the coarse reconstruction results. |
Jiaxiang Shang; Yu Zeng; Xin Qiao; Xin Wang; Runze Zhang; Guangyuan Sun; Vishal Patel; Hongbo Fu; |
| 246 | HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a hierarchical vision Transformer framework named HVTSurv, which can encode the local-level relative spatial information, strengthen WSI-level context-aware communication, and establish patient-level hierarchical interaction. |
Zhuchen Shao; Yang Chen; Hao Bian; Jian Zhang; Guojun Liu; Yongbing Zhang; |
| 247 | Channel Regeneration: Improving Channel Utilization for Compact DNNs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Overparameterized deep neural networks have redundant neurons that do not contribute to the network’s accuracy. In this paper, we introduce a novel channel regeneration technique that reinvigorates these redundant channels by re-initializing its batch normalization scaling factor gamma. |
Ankit Sharma; Hassan Foroosh; |
| 248 | Adaptive Dynamic Filtering Network for Image Denoising Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, dynamic convolution has exhibited powerful capabilities in processing high-frequency information (e.g., edges, corners, textures), but previous works lack sufficient spatial contextual information in filter generation. To alleviate these issues, we propose to employ dynamic convolution to improve the learning of high-frequency and multi-scale features. |
Hao Shen; Zhong-Qiu Zhao; Wandi Zhang; |
| 249 | Edge Structure Learning Via Low Rank Residuals for Robust Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, filtering out such structural information could hamper the discriminative details in images, especially in heavy corruptions. In order to address this limitation, this paper proposes a novel method named ESL-LRR, which preserves image edges by finding image projections from low-rank residuals. |
Xiang-Jun Shen; Stanley Ebhohimhen Abhadiomhen; Yang Yang; Zhifeng Liu; Sirui Tian; |
| 250 | Memory-Oriented Structural Pruning for Efficient Image Restoration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reveal the overlooked memory redundancy of the IR models and propose a Memory-Oriented Structural Pruning (MOSP) method. |
Xiangsheng Shi; Xuefei Ning; Lidong Guo; Tianchen Zhao; Enshu Liu; Yi Cai; Yuhan Dong; Huazhong Yang; Yu Wang; |
| 251 | YOLOV: Making Still Image Object Detectors Great at Video Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these detectors are usually computationally expensive due to their two-stage nature. This work proposes a simple yet effective strategy to address the above concerns, which costs marginal overheads with significant gains in accuracy. |
Yuheng Shi; Naiyan Wang; Xiaojie Guo; |
| 252 | FeedFormer: Revisiting Transformer Decoder for Efficient Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we aim to directly use the encoder features as the queries. |
Jae-hun Shim; Hyunwoo Yu; Kyeongbo Kong; Suk-Ju Kang; |
| 253 | Task-Specific Scene Structure Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. |
Jisu Shin; Seunghyun Shin; Hae-Gon Jeon; |
| 254 | Diversified and Realistic 3D Augmentation Via Iterative Construction, Random Placement, and HPR Occlusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a diversified and realistic augmentation method that can flexibly construct a whole-body object, freely locate and rotate the object, and apply self-occlusion and external-occlusion accordingly. |
Jungwook Shin; Jaeill Kim; Kyungeun Lee; Hyunghun Cho; Wonjong Rhee; |
| 255 | SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel solution for unpaired image-to-image (I2I) translation. |
Seokbeom Song; Suhyeon Lee; Hongje Seong; Kyoungwon Min; Euntai Kim; |
| 256 | Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We formulate the puzzle reassembly as a combinatorial optimization problem and propose a Siamese-Discriminant Deep Reinforcement Learning (SD2RL) to solve it. |
Xingke Song; Jiahuan Jin; Chenglin Yao; Shihe Wang; Jianfeng Ren; Ruibin Bai; |
| 257 | CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce CLIPVG, a text-guided image manipulation framework using differentiable vector graphics, which is also the first CLIP-based general image manipulation framework that does not require any additional generative models. |
Yiren Song; Xuning Shao; Kang Chen; Weidong Zhang; Zhongliang Jing; Minzhe Li; |
| 258 | Compact Transformer Tracker with Correlative Masked Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we prove that the vanilla self-attention structure is sufficient for information aggregation, and structural adaption is unnecessary. |
Zikai Song; Run Luo; Junqing Yu; Yi-Ping Phoebe Chen; Wei Yang; |
| 259 | Text-DIAE: A Self-Supervised Degradation Invariant Autoencoder for Text Recognition and Document Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. |
Mohamed Ali Souibgui; Sanket Biswas; Andres Mafla; Ali Furkan Biten; Alicia Fornés; Yousri Kessentini; Josep Lladós; Lluis Gomez; Dimosthenis Karatzas; |
| 260 | PUPS: Point Cloud Unified Panoptic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple but effective point cloud unified panoptic segmentation (PUPS) framework, which use a set of point-level classifiers to directly predict semantic and instance groupings in an end-to-end manner. |
Shihao Su; Jianyun Xu; Huanyu Wang; Zhenwei Miao; Xin Zhan; Dayang Hao; Xi Li; |
| 261 | Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present an Efficient edge-Preserving multi-view stereo Network (EPNet) for practical depth estimation. |
Wanjuan Su; Wenbing Tao; |
| 262 | Referring Expression Comprehension Using Language Adaptive Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Concretely, we propose a neat yet efficient framework named Language Adaptive Dynamic Subnets (LADS), which can extract language-adaptive subnets from the REC model conditioned on the referring expressions. |
Wei Su; Peihan Miao; Huanzhang Dou; Yongjian Fu; Xi Li; |
| 263 | Rethinking Data Augmentation for Single-Source Domain Generalization in Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we rethink the data augmentation strategy for SDG in medical image segmentation. |
Zixian Su; Kai Yao; Xi Yang; Kaizhu Huang; Qiufeng Wang; Jie Sun; |
| 264 | Hybrid Pixel-Unshuffled Network for Lightweight Image Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task. |
Bin Sun; Yulun Zhang; Songyao Jiang; Yun Fu; |
| 265 | Learning Event-Relevant Factors for Video Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to explicitly learn event-relevant factors to eliminate the interferences from event-irrelevant factors on anomaly predictions. |
Che Sun; Chenrui Shi; Yunde Jia; Yuwei Wu; |
| 266 | Superpoint Transformer for 3D Scene Instance Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. |
Jiahao Sun; Chunmei Qing; Junpeng Tan; Xiangmin Xu; |
| 267 | Asynchronous Event Processing with Local-Shift Graph Convolutional Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a local-shift graph convolutional network (LSNet), which utilizes a novel local-shift operation equipped with a local spatio-temporal attention component to achieve efficient and adaptive aggregation of neighbor features. |
Linhui Sun; Yifan Zhang; Jian Cheng; Hanqing Lu; |
| 268 | DENet: Disentangled Embedding Network for Visible Watermark Removal Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, inspired by the two-stage coarse-refinement network, we propose a novel contrastive learning mechanism to disentangle the high-level embedding semantic information of the images and watermarks, driving the respective network branch more oriented. |
Ruizhou Sun; Yukun Su; Qingyao Wu; |
| 269 | Deep Manifold Attack on Point Clouds Via Parameter Plane Stretching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. |
Keke Tang; Jianpeng Wu; Weilong Peng; Yawen Shi; Peng Song; Zhaoquan Gu; Zhihong Tian; Wenping Wang; |
| 270 | Fair Generative Models Via Transfer Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under this setup, a weakly-supervised approach has been proposed, which achieves state-of-the-art quality and fairness in generated samples. In our work, based on this setup, we propose a simple yet effective approach. |
Christopher T.H. Teo; Milad Abdollahzadeh; Ngai-Man Cheung; |
| 271 | Learning Context-Aware Classifier for Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Different from the mainstream literature where the efficacy of strong backbones and effective decoder heads has been well studied, in this paper, additional contextual hints are instead exploited via learning a context-aware classifier whose content is data-conditioned, decently adapting to different latent distributions. |
Zhuotao Tian; Jiequan Cui; Li Jiang; Xiaojuan Qi; Xin Lai; Yixin Chen; Shu Liu; Jiaya Jia; |
| 272 | TopicFM: Robust and Interpretable Topic-Assisted Feature Matching Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel image-matching method that applies a topic-modeling strategy to encode high-level contexts in images. |
Khang Truong Giang; Soohwan Song; Sungho Jo; |
| 273 | Learning Fractals By Gradient Descent Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel approach that learns the parameters underlying a fractal image via gradient descent. |
Cheng-Hao Tu; Hong-You Chen; David Carlyn; Wei-Lun Chao; |
| 274 | Leveraging Weighted Cross-Graph Attention for Visual and Semantic Enhanced Video Captioning Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These semantic features contain significant information that helps to generate highly informative human description-like captions. Therefore, we propose a novel visual and semantic enhanced video captioning network, named as VSVCap, that efficiently utilizes multiple ground truth captions. |
Deepali Verma; Arya Haldar; Tanima Dutta; |
| 275 | Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we contribute a new Doodle2Object (D2O) dataset consisting of 8,992 3D shapes and over 7M sketches spanning 50 categories. |
Bingrui Wang; Yuan Zhou; |
| 276 | Controlling Class Layout for Deep Ordinal Classification Via Constrained Proxies Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we propose two kinds of strategies: hard layout constraint and soft layout constraint. |
Cong Wang; Zhiwei Jiang; Yafeng Yin; Zifeng Cheng; Shiping Ge; Qing Gu; |
| 277 | Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose to learn an event representation optimized for event-based object detection. |
Dongsheng Wang; Xu Jia; Yang Zhang; Xinyu Zhang; Yaoyuan Wang; Ziyang Zhang; Dong Wang; Huchuan Lu; |
| 278 | Text to Point Cloud Localization with Relation-Enhanced Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the two challenges, we propose a unified Relation-Enhanced Transformer (RET) to improve representation discriminability for both point cloud and nature language queries. |
Guangzhi Wang; Hehe Fan; Mohan Kankanhalli; |
| 279 | UCoL: Unsupervised Learning of Discriminative Facial Representations Via Uncertainty-Aware Contrast Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel uncertainty-aware consistency K-nearest neighbors algorithm to generate predicted positive pairs, which enables efficient discriminative learning from large-scale open-world unlabeled data. |
Hao Wang; Min Li; Yangyang Song; Youjian Zhang; Liying Chi; |
| 280 | Calibrated Teacher for Sparsely Annotated Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, the current methods with fixed thresholds have sub-optimal performance, and are difficult to be applied to other detectors. In order to resolve this obstacle, we propose a Calibrated Teacher, of which the confidence estimation of the prediction is well calibrated to match its real precision. |
Haohan Wang; Liang Liu; Boshen Zhang; Jiangning Zhang; Wuhao Zhang; Zhenye Gan; Yabiao Wang; Chengjie Wang; Haoqian Wang; |
| 281 | Towards Real-Time Panoptic Narrative Grounding By An End-to-End Grounding Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a one-stage network for real-time PNG, termed End-to-End Panoptic Narrative Grounding network (EPNG), which directly generates masks for referents. |
Haowei Wang; Jiayi Ji; Yiyi Zhou; Yongjian Wu; Xiaoshuai Sun; |
| 282 | LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Different from ROSA that rely on various pre- and post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to defend adversarial attacks for SOD models. |
He Wang; Lin Wan; He Tang; |
| 283 | Defending Black-Box Skeleton-Based Human Activity Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose the first black-box defense method for skeleton-based HAR to our best knowledge. |
He Wang; Yunfeng Diao; Zichang Tan; Guodong Guo; |
| 284 | Exploring CLIP for Assessing The Look and Feel of Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we go beyond the conventional paradigms by exploring the rich visual language prior encapsulated in Contrastive Language-Image Pre-training (CLIP) models for assessing both the quality perception (look) and abstract perception (feel) of images without explicit task-specific training. |
Jianyi Wang; Kelvin C.K. Chan; Chen Change Loy; |
| 285 | Robust Video Portrait Reenactment Via Personalized Representation Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes the Video Portrait via Non-local Quantization Modeling (VPNQ) framework, which produces pose- and disturbance-robust reenactable video portraits. |
Kaisiyuan Wang; Changcheng Liang; Hang Zhou; Jiaxiang Tang; Qianyi Wu; Dongliang He; Zhibin Hong; Jingtuo Liu; Errui Ding; Ziwei Liu; Jingdong Wang; |
| 286 | De-biased Teacher: Rethinking IoU Matching for Semi-supervised Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To de-bias the training proposals generated by the pseudo-label-based IoU matching, we propose a general framework — De-biased Teacher, which abandons both the IoU matching and pseudo labeling processes by directly generating favorable training proposals for consistency regularization between the weak/strong augmented image pairs. |
Kuo Wang; Jingyu Zhuang; Guanbin Li; Chaowei Fang; Lechao Cheng; Liang Lin; Fan Zhou; |
| 287 | Learning to Generate An Unbiased Scene Graph By Using Attribute-Guided Predicate Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, a decoupled learning framework is proposed for unbiased scene graph generation by using attribute-guided predicate features to construct a balanced training set. |
Lei Wang; Zejian Yuan; Badong Chen; |
| 288 | Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific supervised and alignment-aware contrastive objective. |
Lei Wang; Jiabang He; Xing Xu; Ning Liu; Hui Liu; |
| 289 | Flora: Dual-Frequency LOss-Compensated ReAl-Time Monocular 3D Video Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a real-time monocular 3D video reconstruction approach named Flora for reconstructing delicate and complete 3D scenes from RGB video sequences in an end-to-end manner. |
Likang Wang; Yue Gong; Qirui Wang; Kaixuan Zhou; Lei Chen; |
| 290 | Efficient Image Captioning for Edge Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose LightCap, a lightweight image captioner for resource-limited devices. |
Ning Wang; Jiangrong Xie; Hang Luo; Qinglin Cheng; Jihao Wu; Mingbo Jia; Linlin Li; |
| 291 | Controllable Image Captioning Via Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. |
Ning Wang; Jiahao Xie; Jihao Wu; Mingbo Jia; Linlin Li; |
| 292 | ECO-3D: Equivariant Contrastive Learning for Pre-training on Perturbed 3D Point Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate contrastive learning on perturbed point clouds and find that the contrasting process may widen the domain gap caused by random perturbations, making the pre-trained network fail to generalize on testing data. |
Ruibin Wang; Xianghua Ying; Bowei Xing; Jinfa Yang; |
| 293 | Global-Local Characteristic Excited Cross-Modal Attacks from Images to Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective cross-modal attack method which considers both the global and local characteristics of video data. |
Ruikui Wang; Yuanfang Guo; Yunhong Wang; |
| 294 | Fine-Grained Retrieval Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompting and feature adaptation. |
Shijie Wang; Jianlong Chang; Zhihui Wang; Haojie Li; Wanli Ouyang; Qi Tian; |
| 295 | Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. |
Tao Wang; Kaihao Zhang; Tianrun Shen; Wenhan Luo; Bjorn Stenger; Tong Lu; |
| 296 | 3D Assembly Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose FiT, a framework for Finishing the incomplete 3D assembly with Transformer. |
Weihao Wang; Rufeng Zhang; Mingyu You; Hongjun Zhou; Bin He; |
| 297 | A Benchmark and Asymmetrical-Similarity Learning for Practical Image Copy Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on existing ICD datasets, this paper constructs a new dataset by additionally adding 100,000 and 24, 252 hard negative pairs into the training and test set, respectively. |
Wenhao Wang; Yifan Sun; Yi Yang; |
| 298 | Revisiting Unsupervised Local Descriptor Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents HybridDesc, an unsupervised approach that learns powerful local descriptor models with fast convergence speed by combining the rule-based and clustering-based approaches to construct training tuples. |
Wufan Wang; Lei Zhang; Hua Huang; |
| 299 | Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, MVS often suffers from texture-less regions, non-Lambertian surfaces, and moving objects, especially in real-world video sequences without known camera motion and depth supervision. Therefore, we propose MOVEDepth, which exploits the MOnocular cues and VElocity guidance to improve multi-frame Depth learning. |
Xiaofeng Wang; Zheng Zhu; Guan Huang; Xu Chi; Yun Ye; Ziwei Chen; Xingang Wang; |
| 300 | Learning Continuous Depth Representation Via Geometric Spatial Aggregator Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inefficient real-numbered scale upsampling. To explicitly address this issue, we propose a novel continuous depth representation for DSR. |
Xiaohang Wang; Xuanhong Chen; Bingbing Ni; Zhengyan Tong; Hang Wang; |
| 301 | SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. |
Yan Wang; Junbo Yin; Wei Li; Pascal Frossard; Ruigang Yang; Jianbing Shen; |
| 302 | High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A generic method for generating a high-quality image from the degraded one is in demand. In this paper, we present a novel GAN inversion framework that utilizes the powerful generative ability of StyleGAN-XL for this problem. |
Yanbo Wang; Chuming Lin; Donghao Luo; Ying Tai; Zhizhong Zhang; Yuan Xie; |
| 303 | GAN Prior Based Null-Space Learning for Consistent Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While the realness has been dramatically improved with the use of GAN prior, the state-of-the-art methods still suffer inconsistencies in local structures and colors (e.g., tooth and eyes). In this paper, we show that these inconsistencies can be analytically eliminated by learning only the null-space component while fixing the range-space part. |
Yinhuai Wang; Yujie Hu; Jiwen Yu; Jian Zhang; |
| 304 | Contrastive Masked Autoencoders for Self-Supervised Video Hashing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple yet effective one-stage SSVH method called ConMH, which incorporates video semantic information and video similarity relationship understanding in a single stage. |
Yuting Wang; Jinpeng Wang; Bin Chen; Ziyun Zeng; Shu-Tao Xia; |
| 305 | MicroAST: Towards Super-fast Ultra-Resolution Arbitrary Style Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. |
Zhizhong Wang; Lei Zhao; Zhiwen Zuo; Ailin Li; Haibo Chen; Wei Xing; Dongming Lu; |
| 306 | Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose two new strategies for video analysis with noisy labels: 1) a lightweight channel selection method dubbed as Channel Truncation for feature-based label noise detection. |
Zixiao Wang; Junwu Weng; Chun Yuan; Jue Wang; |
| 307 | Active Token Mixer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate contextual information from other tokens in the global scope into the given query token. |
Guoqiang Wei; Zhizheng Zhang; Cuiling Lan; Yan Lu; Zhibo Chen; |
| 308 | Exploring Non-target Knowledge for Improving Ensemble Universal Adversarial Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we simply adopt a KL loss that only considers the non-target classes for addressing the dominant bias issue. |
Juanjuan Weng; Zhiming Luo; Zhun Zhong; Dazhen Lin; Shaozi Li; |
| 309 | Towards Good Practices for Missing Modality Robust Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper seeks a set of good practices for multi-modal action recognition, with a particular interest in circumstances where some modalities are not available at an inference time. |
Sangmin Woo; Sumin Lee; Yeonju Park; Muhammad Adi Nugroho; Changick Kim; |
| 310 | Reject Decoding Via Language-Vision Models for Text-to-Image Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we build tiny multi-modal models to evaluate the similarities between the partial paths and the caption at multi scales. |
Fuxiang Wu; Liu Liu; Fusheng Hao; Fengxiang He; Lei Wang; Jun Cheng; |
| 311 | Transformation-Equivariant 3D Object Detection for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present TED, an efficient Transformation-Equivariant 3D Detector to overcome the computation cost and speed issues. |
Hai Wu; Chenglu Wen; Wei Li; Xin Li; Ruigang Yang; Cheng Wang; |
| 312 | Super-efficient Echocardiography Video Segmentation Via Proxy- and Kernel-Based Semi-supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Particularly, the real-time demand in clinical practice makes this task even harder. In this paper, we propose a novel proxy- and kernel-based semi-supervised segmentation network (PKEcho-Net) to comprehensively address these challenges. |
Huisi Wu; Jingyin Lin; Wende Xie; Jing Qin; |
| 313 | ACL-Net: Semi-supervised Polyp Segmentation Via Affinity Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel semi-supervised polyp segmentation framework using affinity contrastive learning (ACL-Net), which is implemented between student and teacher networks to consistently refine the pseudo-labels for semi-supervised polyp segmentation. |
Huisi Wu; Wende Xie; Jingyin Lin; Xinrong Guo; |
| 314 | Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. |
Jijie Wu; Dongliang Chang; Aneeshan Sain; Xiaoxu Li; Zhanyu Ma; Jie Cao; Jun Guo; Yi-Zhe Song; |
| 315 | Preserving Structural Consistency in Arbitrary Artist and Artwork Style Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These methods not only homogenize the artist-style of different artworks of the same artist but also lack generalization for the unseen artists. To solve these challenges, we propose a double-style transferring module (DSTM). |
Jingyu Wu; Lefan Hou; Zejian Li; Jun Liao; Li Liu; Lingyun Sun; |
| 316 | End-to-End Zero-Shot HOI Detection Via Vision and Language Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The fundamental challenges are to discover potential human-object pairs and identify novel HOI categories. To overcome the above challenges, we propose a novel End-to-end zero-shot HOI Detection (EoID) framework via vision-language knowledge distillation. |
Mingrui Wu; Jiaxin Gu; Yunhang Shen; Mingbao Lin; Chao Chen; Xiaoshuai Sun; |
| 317 | Revisiting Classifier: Transferring Vision-Language Models for Video Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we focus on transferring knowledge for video classification tasks. |
Wenhao Wu; Zhun Sun; Wanli Ouyang; |
| 318 | Scene Graph to Image Synthesis Via Knowledge Consensus Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study graph-to-image generation conditioned exclusively on scene graphs, in which we seek to disentangle the veiled semantics between knowledge graphs and images. |
Yang Wu; Pengxu Wei; Liang Lin; |
| 319 | Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, synthetic data usually has lower quality than real data, and using synthetic data may not improve CL compared with using real data. To tackle this problem, we propose a data generation framework with two methods to improve CL training by joint sample generation and contrastive learning. |
Yawen Wu; Zhepeng Wang; Dewen Zeng; Yiyu Shi; Jingtong Hu; |
| 320 | Multi-Stream Representation Learning for Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the procedure that the hippocampus processes and integrates spatio-temporal information to form memories, we propose a novel multi-stream representation learning module to learn complex spatio-temporal features of pedestrian trajectory. |
Yuxuan Wu; Le Wang; Sanping Zhou; Jinghai Duan; Gang Hua; Wei Tang; |
| 321 | Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). |
Zhenyu Wu; Lin Wang; Wei Wang; Qing Xia; Chenglizhao Chen; Aimin Hao; Shuo Li; |
| 322 | Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, inevitable errors from estimated depth priors may lead to misaligned semantic information and 3D localization, hence resulting in feature smearing and suboptimal predictions. To mitigate this issue, we propose ADD, an Attention-based Depth knowledge Distillation framework with 3D-aware positional encoding. |
Zizhang Wu; Yunzhe Wu; Jian Pu; Xianzhi Li; Xiaoquan Wang; |
| 323 | Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most learning-based methods struggle for two reasons: 1) each move in figure skating changes quickly, hence simply applying traditional frame sampling will lose a lot of valuable information, especially in 3 to 5 minutes lasting videos; 2) prior methods rarely considered the critical audio-visual relationship in their models. Due to these reasons, we introduce a novel architecture, named Skating-Mixer. |
Jingfei Xia; Mingchen Zhuge; Tiantian Geng; Shun Fan; Yuantai Wei; Zhenyu He; Feng Zheng; |
| 324 | SVFI: Spiking-Based Video Frame Interpolation for High-Speed Motion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of estimating motions by optical flow from RGB frames, we present a new dual-modal pipeline adopting both RGB frames and the corresponding spike stream as inputs (SVFI). |
Lujie Xia; Jing Zhao; Ruiqin Xiong; Tiejun Huang; |
| 325 | FEditNet: Few-Shot Editing of Latent Semantics in GAN Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a GAN-based method called FEditNet, aiming to discover latent semantics using very few labeled data without any pretrained predictors or prior knowledge. |
Mengfei Xia; Yezhi Shu; Yuji Wang; Yu-Kun Lai; Qiang Li; Pengfei Wan; Zhongyuan Wang; Yong-Jin Liu; |
| 326 | Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The existing adversarial training strategies are difficult to generalized into medical imaging field challenged by complex medical texture features. To overcome this challenge, we propose a Contour Attention Preserving (CAP) method based on lung cavity edge extraction. |
Kun Xiang; Xing Zhang; Jinwen She; Jinpeng Liu; Haohan Wang; Shiqi Deng; Shancheng Jiang; |
| 327 | Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, there exist inaccurate pseudo-labels which map the ambiguous representations of pixels to the wrong classes due to the limited cognitive ability of the model. In this paper, we define pixel-wise representations from a new perspective of probability theory and propose a Probabilistic Representation Contrastive Learning (PRCL) framework that improves representation quality by taking its probability into consideration. |
Haoyu Xie; Changqi Wang; Mingkai Zheng; Minjing Dong; Shan You; Chong Fu; Chang Xu; |
| 328 | Less Is More Important: An Attention Module Guided By Probability Density Function for Convolutional Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing attention modules are heuristic without a sound interpretation, and thus, require empirical engineering to design structure and operators within the modules. To handle the above issue, based on our ‘less is more important’ observation, we propose an Attention Module guided by Probability Density Function (PDF), dubbed PdfAM, which enjoys a rational motivation and requires few empirical structure designs. |
Jingfen Xie; Jian Zhang; |
| 329 | Mitigating Artifacts in Real-World Video Super-resolution Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the observations, we propose a Hidden State Attention (HSA) module to mitigate artifacts in real-world video super-resolution. |
Liangbin Xie; Xintao Wang; Shuwei Shi; Jinjin Gu; Chao Dong; Ying Shan; |
| 330 | Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-Driven Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we investigate the JND modeling from an end-to-end homologous multimodal perspective, namely hmJND-Net. |
Wuyuan Xie; Shukang Wang; Sukun Tian; Lirong Huang; Ye Liu; Miaohui Wang; |
| 331 | Cross-Modal Contrastive Learning for Domain Adaptation in 3D Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on it, in this paper, we propose a novel cross-modal contrastive learning scheme to further improve the adaptation effects. |
Bowei Xing; Xianghua Ying; Ruibin Wang; Jinfa Yang; Taiyan Chen; |
| 332 | ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we revisit feature fusion between depth and semantic information and propose an efficient local adaptive attention method for geometric aware representation enhancement. |
Daitao Xing; Jinglin Shen; Chiuman Ho; Anthony Tzes; |
| 333 | LORE: Logical Location Regression Network for Table Structure Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, they either count on additional heuristic rules to recover the table structures, or require a huge amount of training data and time-consuming sequential decoders. In this paper, we propose an alternative paradigm. |
Hangdi Xing; Feiyu Gao; Rujiao Long; Jiajun Bu; Qi Zheng; Liangcheng Li; Cong Yao; Zhi Yu; |
| 334 | Revisiting The Spatial and Temporal Modeling for Few-Shot Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SloshNet, a new framework that revisits the spatial and temporal modeling for few-shot action recognition in a finer manner. |
Jiazheng Xing; Mengmeng Wang; Yong Liu; Boyu Mu; |
| 335 | Unsupervised Multi-Exposure Image Fusion Breaking Exposure Limits Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an unsupervised multi-exposure image fusion (MEF) method via contrastive learning, termed as MEF-CL. |
Han Xu; Liang Haochen; Jiayi Ma; |
| 336 | CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion By Dense Feature Fusion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion. |
Jinfeng Xu; Xianzhi Li; Yuan Tang; Qiao Yu; Yixue Hao; Long Hu; Min Chen; |
| 337 | Learning A Generalized Gaze Estimator from Gaze-Consistent Feature Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new domain generalization method based on gaze-consistent features. |
Mingjie Xu; Haofei Wang; Feng Lu; |
| 338 | Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these, we propose a categorical domain discriminator guided by uncertainty to explicitly model and directly align categorical distributions P(Z|Y). |
Pengcheng Xu; Boyu Wang; Charles Ling; |
| 339 | Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a simple and novel Self Correspondence Distillation (SCD) method to refine pseudo-labels without introducing external supervision. |
Rongtao Xu; Changwei Wang; Jiaxi Sun; Shibiao Xu; Weiliang Meng; Xiaopeng Zhang; |
| 340 | Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. |
Xiaogang Xu; Ruixing Wang; Chi-Wing Fu; Jiaya Jia; |
| 341 | Inter-image Contrastive Consistency for Multi-Person Pose Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a novel framework, termed Inter-image Contrastive consistency (ICON), to strengthen the keypoint consistency among images for MPPE. |
Xixia Xu; Yingguo Gao; Xingjia Pan; Ke Yan; Xiaoyu Chen; Qi Zou; |
| 342 | DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer for multi-task learning of dense prediction. |
Yangyang Xu; Yibo Yang; Lefei Zhang; |
| 343 | VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Following the human perception process, where the scene is effectively understood by decomposing it into visual (e.g. human, animal) and non-visual components (e.g. action, relations) under the mutual influence of vision and language, we first propose a visual-linguistic (VL) feature. In the proposed VL feature, the scene is modeled by three modalities including (i) a global visual environment; (ii) local visual main agents; (iii) linguistic scene elements. We then introduce an autoregressive Transformer-in-Transformer (TinT) to simultaneously capture the semantic coherence of intra- and inter-event contents within a video. |
Kashu Yamazaki; Khoa Vo; Quang Sang Truong; Bhiksha Raj; Ngan Le; |
| 344 | Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable. To address this problem, we propose a disparity-based MVS method based on the epipolar disparity flow (E-flow), called DispMVS, which infers the depth information from the pixel movement between two views. |
Qingsong Yan; Qiang Wang; Kaiyong Zhao; Bo Li; Xiaowen Chu; Fei Deng; |
| 345 | Video-Text Pre-training with Learned Regions for Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs. |
Rui Yan; Mike Zheng Shou; Yixiao Ge; Jinpeng Wang; Xudong Lin; Guanyu Cai; Jinhui Tang; |
| 346 | DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose the decomposed scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation, contributing to individual learning benefits. |
Zhiqiang Yan; Kun Wang; Xiang Li; Zhenyu Zhang; Jun Li; Jian Yang; |
| 347 | Self-Supervised Video Representation Learning Via Latent Time Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This leads to loss of pertinent information related to temporal relationships, rendering actions such as `enter’ and `leave’ to be indistinguishable. To mitigate this limitation, we propose Latent Time Navigation (LTN), a time parameterized contrastive learning strategy that is streamlined to capture fine-grained motions. |
Di Yang; Yaohui Wang; Quan Kong; Antitza Dantcheva; Lorenzo Garattoni; Gianpiero Francesca; François Brémond; |
| 348 | One-Shot Replay: Boosting Incremental Object Detection Via Retrospecting One Object Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a novel One-Shot Replay (OSR) method for incremental object detection, which is an augmentation-based method. |
Dongbao Yang; Yu Zhou; Xiaopeng Hong; Aoting Zhang; Weiping Wang; |
| 349 | Video Event Extraction Via Tracking Visual States of Arguments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the definition of events as changes of states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. |
Guang Yang; Manling Li; Jiajie Zhang; Xudong Lin; Heng Ji; Shih-Fu Chang; |
| 350 | CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a single-model self-supervised hybrid pre-training framework for RGB and depth modalities, termed as CoMAE. |
Jiange Yang; Sheng Guo; Gangshan Wu; Limin Wang; |
| 351 | Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. |
Jinhai Yang; Mengxi Guo; Shijie Zhao; Junlin Li; Li Zhang; |
| 352 | Stop-Gradient Softmax Loss for Deep Metric Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this letter, we look into the characteristic of softmax-based approaches and propose a novel learning objective function Stop-Gradient Softmax Loss (SGSL) to solve the convergence problem in softmax-based deep metric learning with L2-normalization. |
Lu Yang; Peng Wang; Yanning Zhang; |
| 353 | Local Path Integration for Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We devise a method to identify the local input distribution and propose a technique to stochastically integrate the model gradients over the paths defined by the references sampled from that distribution. |
Peiyu Yang; Naveed Akhtar; Zeyi Wen; Ajmal Mian; |
| 354 | Spatiotemporal Deformation Perception for Fisheye Video Rectification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For different frames of the fisheye video, the existing image correction methods ignore the correlation of sequences, resulting in temporal jitter in the corrected video. To solve this problem, we propose a temporal weighting scheme to get a plausible global optical flow, which mitigates the jitter effect by progressively reducing the weight of frames. |
Shangrong Yang; Chunyu Lin; Kang Liao; Yao Zhao; |
| 355 | Contrastive Multi-Task Dense Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel multi-task contrastive regularization method based on the consistency to effectively boost the representation learning of the different sub-tasks, which can also be easily generalized to different multi-task dense prediction frameworks, and costs no additional computation in the inference. |
Siwei Yang; Hanrong Ye; Dan Xu; |
| 356 | AutoStegaFont: Synthesizing Vector Fonts for Hiding Information in Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, none of the existing methods can satisfy these requirements well and simultaneously. To satisfy the above requirements, we propose AutoStegaFont, an automatic vector font synthesis scheme for hiding information in documents. |
Xi Yang; Jie Zhang; Han Fang; Chang Liu; Zehua Ma; Weiming Zhang; Nenghai Yu; |
| 357 | Towards Global Video Scene Segmentation with Context-Aware Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce a novel Context-Aware Transformer (CAT) with a self-supervised learning framework to learn high-quality shot representations, for generating well-bounded scenes. |
Yang Yang; Yurui Huang; Weili Guo; Baohua Xu; Dingyin Xia; |
| 358 | Low-Light Image Enhancement Network Based on Multi-Scale Feature Complementation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although most current enhancement methods can obtain high-contrast images, they still suffer from noise amplification and color distortion. To address these issues, this paper proposes a low-light image enhancement network based on multi-scale feature complementation (LIEN-MFC), which is a U-shaped encoder-decoder network supervised by multiple images of different scales. |
Yong Yang; Wenzhi Xu; Shuying Huang; Weiguo Wan; |
| 359 | Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a simple yet effective alternative for progressively learning discriminative multi-modal features. |
Zhao Yang; Jiaqi Wang; Yansong Tang; Kai Chen; Hengshuang Zhao; Philip H.S. Torr; |
| 360 | LidarMultiNet: Towards A Unified Multi-Task Network for LiDAR Perception Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult to adapt to each other. This paper presents LidarMultiNet, a LiDAR-based multi-task network that unifies these three major LiDAR perception tasks. |
Dongqiangzi Ye; Zixiang Zhou; Weijia Chen; Yufei Xie; Yu Wang; Panqu Wang; Hassan Foroosh; |
| 361 | DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. |
Maoyuan Ye; Jing Zhang; Shanshan Zhao; Juhua Liu; Bo Du; Dacheng Tao; |
| 362 | Learning Second-Order Attentive Context for Efficient Correspondence Pruning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective and efficient method for correspondence pruning. |
Xinyi Ye; Weiyue Zhao; Hao Lu; Zhiguo Cao; |
| 363 | Infusing Definiteness Into Randomness: Rethinking Composition Styles for Deep Image Matting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we first show that naive foreground combination can be problematic and therefore derive an alternative formulation to reasonably combine foregrounds. Our second contribution is an observation that matting performance can benefit from a certain occurrence frequency of combined foregrounds and their associated source foregrounds during training. Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet. |
Zixuan Ye; Yutong Dai; Chaoyi Hong; Zhiguo Cao; Hao Lu; |
| 364 | Can We Find Strong Lottery Tickets in Generative Models? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. |
Sangyeop Yeo; Yoojin Jang; Jy-yong Sohn; Dongyoon Han; Jaejun Yoo; |
| 365 | Class-Independent Regularization for Learning with Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a class-independent regularization (CIR) method that can effectively alleviate the negative impact of noisy labels in DNN training. |
Rumeng Yi; Dayan Guan; Yaping Huang; Shijian Lu; |
| 366 | Unbiased Heterogeneous Scene Graph Generation with Relation-Aware Message Passing Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. |
Kanghoon Yoon; Kibum Kim; Jinyoung Moon; Chanyoung Park; |
| 367 | Lifelong Person Re-identification Via Knowledge Refreshing and Consolidation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: More specifically, a knowledge refreshing scheme is incorporated with the knowledge rehearsal mechanism to enable bi-directional knowledge transfer by introducing a dynamic memory model and an adaptive working model. |
Chunlin Yu; Ye Shi; Zimo Liu; Shenghua Gao; Jingya Wang; |
| 368 | Generalizing Multiple Object Tracking to Unseen Domains By Introducing Natural Language Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge this gap, we first draw the observation that the high-level information contained in natural language is domain invariant to different tracking domains. Based on this observation, we propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. |
En Yu; Songtao Liu; Zhuoling Li; Jinrong Yang; Zeming Li; Shoudong Han; Wenbing Tao; |
| 369 | Rethinking Rotation Invariance with Point Cloud Registration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we review rotation invariance (RI) in terms of point cloud registration (PCR) and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. |
Jianhui Yu; Chaoyi Zhang; Weidong Cai; |
| 370 | Frame-Level Label Refinement for Skeleton-Based Weakly-Supervised Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by advances in handling the noisy label problem, we introduce a label cleaning strategy of the frame-level pseudo labels to guide the learning process. |
Qing Yu; Kent Fujiwara; |
| 371 | Recurrent Structure Attention Guidance for Depth Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a recurrent structure attention guided (RSAG) framework, consisting of two important parts. |
Jiayi Yuan; Haobo Jiang; Xiang Li; Jianjun Qian; Jun Li; Jian Yang; |
| 372 | Structure Flow-Guided Network for Real Depth Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel structure flow-guided DSR framework, where a cross-modality flow map is learned to guide the RGB-structure information transferring for precise depth upsampling. |
Jiayi Yuan; Haobo Jiang; Xiang Li; Jianjun Qian; Jun Li; Jian Yang; |
| 373 | Pseudo Label-Guided Model Inversion Attack Via Conditional Generative Adversarial Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the widely used cross-entropy loss in these attacks suffers from gradient vanishing. To address these problems, we propose Pseudo Label-Guided MI (PLG-MI) attack via conditional GAN (cGAN). |
Xiaojian Yuan; Kejiang Chen; Jie Zhang; Weiming Zhang; Nenghai Yu; Yang Zhang; |
| 374 | Cyclically Disentangled Feature Translation for Face Anti-spoofing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we tackle cross-scenario face anti-spoofing by proposing a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN). |
Haixiao Yue; Keyao Wang; Guosheng Zhang; Haocheng Feng; Junyu Han; Errui Ding; Jingdong Wang; |
| 375 | FlowFace: Semantic Flow-Guided Shape-Aware Face Swapping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a semantic flow-guided two-stage framework for shape-aware face swapping, namely FlowFace. |
Hao Zeng; Wei Zhang; Changjie Fan; Tangjie Lv; Suzhen Wang; Zhimeng Zhang; Bowen Ma; Lincheng Li; Yu Ding; Xin Yu; |
| 376 | Multi-Modal Knowledge Hypergraph for Diverse Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we pioneer a degree-free hypergraph solution that models many-to-many relations to address the challenge of heterogeneous sources and heterogeneous modalities. |
Yawen Zeng; Qin Jin; Tengfei Bao; Wenfeng Li; |
| 377 | Learnable Blur Kernel for Single-Image Defocus Deblurring in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the deblurred image generated by the defocus deblurring network lacks high-frequency details, which is unsatisfactory in human perception. To overcome this issue, we propose a novel defocus deblurring method that uses the guidance of the defocus map to implement image deblurring. |
Jucai Zhai; Pengcheng Zeng; Chihao Ma; Jie Chen; Yong Zhao; |
| 378 | Darwinian Model Upgrades: Model Evolving with Selective Compatibility Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Darwinian Model Upgrades (DMU), which disentangle the inheritance and variation in the model evolving with selective backward compatibility and forward adaptation, respectively. |
Binjie Zhang; Shupeng Su; Yixiao Ge; Xuyuan Xu; Yexin Wang; Chun Yuan; Mike Zheng Shou; Ying Shan; |
| 379 | Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The results are not ideal when the domain gap is large. To solve the problem of lacking supervision, we introduce masked modeling into this task and propose a method Mx2M, which utilizes masked cross-modality modeling to reduce the large domain gap. |
Boxiang Zhang; Zunran Wang; Yonggen Ling; Yuanyuan Guan; Shenghao Zhang; Wenhui Li; |
| 380 | Few-Shot 3D Point Cloud Semantic Segmentation Via Stratified Class-Specific Attention Based Transformer Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we further address these problems by developing a new multi-layer transformer network for few-shot point cloud semantic segmentation. |
Canyu Zhang; Zhenyao Wu; Xinyi Wu; Ziyu Zhao; Song Wang; |
| 381 | PaRot: Patch-Wise Rotation-Invariant Network Via Feature Disentanglement and Pose Restoration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a novel Patch-wise Rotation-invariant network (PaRot), which achieves rotation invariance via feature disentanglement and produces consistent predictions for samples with arbitrary rotations. |
Dingxin Zhang; Jianhui Yu; Chaoyi Zhang; Weidong Cai; |
| 382 | Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition. |
Jiahang Zhang; Lilang Lin; Jiaying Liu; |
| 383 | ImageNet Pre-training Also Transfers Non-robustness Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: ImageNet pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized contribution to generalization, we observed in this study that ImageNet pre-training also transfers adversarial non-robustness from pre-trained model into fine-tuned model in the downstream classification tasks. |
Jiaming Zhang; Jitao Sang; Qi Yi; Yunfan Yang; Huiwen Dong; Jian Yu; |
| 384 | Language-Assisted 3D Feature Learning for Semantic Scene Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To guide 3D feature learning toward important geometric attributes and scene context, we explore the help of textual scene descriptions. |
Junbo Zhang; Guofan Fan; Guanghan Wang; Zhengyuan Su; Kaisheng Ma; Li Yi; |
| 385 | IKOL: Inverse Kinematics Optimization Layer for 3D Human Pose and Shape Estimation Via Gauss-Newton Differentiation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents an inverse kinematic optimization layer (IKOL) for 3D human pose and shape estimation that leverages the strength of both optimization- and regression-based methods within an end-to-end framework. |
Juze Zhang; Ye Shi; Yuexin Ma; Lan Xu; Jingyi Yu; Jingya Wang; |
| 386 | Mind The Gap: Polishing Pseudo Labels for Accurate Semi-supervised Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, due to the limited generalization capacity of the teacher detector caused by the scarce annotations, the produced pseudo labels often deviate from ground truth, especially those with relatively low classification confidences, thus limiting the generalization performance of SSOD. To mitigate this problem, we propose a dual pseudo-label polishing framework for SSOD. |
Lei Zhang; Yuxuan Sun; Wei Wei; |
| 387 | ConvMatch: Rethinking Network Design for Two-View Correspondence Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage convolutional neural network (CNN) as the backbone to capture better context, thus avoiding the complex design of extra blocks. |
Shihua Zhang; Jiayi Ma; |
| 388 | Cross-View Geo-Localization Via Learning Disentangled Geometric Layout Correspondence Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose GeoDTR which explicitly disentangles geometric information from raw features and learns the spatial correlations among visual features from aerial and ground pairs with a novel geometric layout extractor module. |
Xiaohan Zhang; Xingyu Li; Waqas Sultani; Yi Zhou; Safwan Wshah; |
| 389 | Video Compression Artifact Reduction By Fusing Motion Compensation and Global Context in A Swin-CNN Based Parallel Architecture Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The key idea of this paper is to fuse the motion compensation and global context together to gain more compensation information to improve the quality of compressed videos. |
Xinjian Zhang; Su Yang; Wuyang Luo; Longwen Gao; Weishan Zhang; |
| 390 | MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Modality Restitution and Compensation Network (MRCN) to narrow the gap between the two modalities. |
Yukang Zhang; Yan Yan; Jie Li; Hanzi Wang; |
| 391 | A Simple Baseline for Multi-Camera 3D Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present SimMOD, a Simple baseline for Multi-camera Object Detection, to solve the problem. |
Yunpeng Zhang; Wenzhao Zheng; Zheng Zhu; Guan Huang; Jiwen Lu; Jie Zhou; |
| 392 | Positional Label for Self-Supervised Vision Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: General effectiveness has been proven in ViT. In our work we propose to train ViT to recognize the positional label of patches of the input image, this apparently simple task actually yields a meaningful self-supervisory task. |
Zhemin Zhang; Xun Gong; |
| 393 | Cross-Category Highlight Detection Via Feature Decomposition and Modality Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under this framework, we propose a novel module, named Multi-task Feature Decomposition Branch which jointly conducts label prediction, cyclic feature reconstruction, and adversarial feature reconstruction to decompose the video features into two independent components: highlight-related component and category-related component. |
Zhenduo Zhang; |
| 394 | TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a transformer module towards the temporal correlations among the input features within pedestrian video sequences and a deep evidential learning model to capture the AI uncertainty under scene complexities. |
Zhengming Zhang; Renran Tian; Zhengming Ding; |
| 395 | DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous works fail to generate high-fidelity dubbing results. To address the above problem, this paper proposes a Deformation Inpainting Network (DINet) for high-resolution face visually dubbing. |
Zhimeng Zhang; Zhipeng Hu; Wenjin Deng; Changjie Fan; Tangjie Lv; Yu Ding; |
| 396 | ShiftDDPMs: Exploring Conditional Diffusion Models By Shifting Diffusion Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel and flexible conditional diffusion model by introducing conditions into the forward process. |
Zijian Zhang; Zhou Zhao; Jun Yu; Qi Tian; |
| 397 | Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, for challenge A, we propose an effective bias-conflicting scoring method to boost the identification accuracy with two practical strategies — peer-picking and epoch-ensemble. |
Bowen Zhao; Chen Chen; Qian-Wei Wang; Anfeng He; Shu-Tao Xia; |
| 398 | RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. |
Boxuan Zhao; Jun Zhang; Deheng Ye; Jian Cao; Xiao Han; Qiang Fu; Wei Yang; |
| 399 | Learning to Super-resolve Dynamic Scenes for Neuromorphic Spike Camera Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, as a trade-off for high temporal resolution, its spatial resolution is limited, resulting in inferior reconstruction details. To address this issue, this paper develops a network (SpikeSR-Net) to super-resolve a high-resolution image sequence from the low-resolution binary spike streams. |
Jing Zhao; Ruiqin Xiong; Jian Zhang; Rui Zhao; Hangfan Liu; Tiejun Huang; |
| 400 | TinyNeRF: Towards 100 X Compression of Voxel Radiance Fields Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods accelerate the original NeRF at the expense of extra storage demand, which hinders their applications in many scenarios. To solve this limitation, we present TinyNeRF, a three-stage pipeline: frequency domain transformation, pruning and quantization that work together to reduce the storage demand of the voxel grids with little to no effects on their speed and synthesis quality. |
Tianli Zhao; Jiayuan Chen; Cong Leng; Jian Cheng; |
| 401 | BEST: BERT Pre-training for Sign Language Recognition with Coupling Tokenization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. |
Weichao Zhao; Hezhen Hu; Wengang Zhou; Jiaxin Shi; Houqiang Li; |
| 402 | MulGT: Multi-Task Graph-Transformer with Task-Aware Knowledge Injection and Domain Knowledge-Driven Pooling for Whole Slide Image Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present a novel multi-task framework (i.e., MulGT) for WSI analysis by the specially designed Graph-Transformer equipped with Task-aware Knowledge Injection and Domain Knowledge-driven Graph Pooling modules. |
Weiqin Zhao; Shujun Wang; Maximus Yeung; Tianye Niu; Lequan Yu; |
| 403 | Grouped Knowledge Distillation for Deep Face Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We experimentally found that (1) Primary-KD and Binary-KD are indispensable for KD, and (2) Secondary-KD is the culprit restricting KD at the bottleneck. Therefore, we propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and Binary-KD but omits Secondary-KD in the ultimate KD loss calculation. |
Weisong Zhao; Xiangyu Zhu; Kaiwen Guo; Xiao-Yu Zhang; Zhen Lei; |
| 404 | Style-Content Metric Learning for Multidomain Remote Sensing Object Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a style-content metric learning framework to address the generalizable remote sensing object recognition issue. |
Wenda Zhao; Ruikai Yang; Yu Liu; You He; |
| 405 | Occupancy Planes for Single-View RGB-D Human Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For more accurate results we propose the occupancy planes (OPlanes) representation, which enables to formulate single-view RGB-D human reconstruction as occupancy prediction on planes which slice through the camera’s view frustum. |
Xiaoming Zhao; Yuan-Ting Hu; Zhongzheng Ren; Alexander G. Schwing; |
| 406 | Deep Equilibrium Models for Snapshot Compressive Imaging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose deep equilibrium models (DEQ) for video SCI, fusing data-driven regularization and stable convergence in a theoretically sound manner. |
Yaping Zhao; Siming Zheng; Xin Yuan; |
| 407 | Unsupervised Deep Video Denoising with Untrained Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Collecting noise-free videos can be costly and challenging in many applications. Therefore, this paper aims to develop an unsupervised deep learning method for video denoising that only uses a single test noisy video for training. |
Huan Zheng; Tongyao Pang; Hui Ji; |
| 408 | Attack Can Benefit: An Adversarial Approach to Recognizing Facial Expressions Under Noisy Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned issues, in this paper, we propose a novel and flexible method to spot noisy labels by leveraging adversarial attack, termed as Geometry Aware Adversarial Vulnerability Estimation (GAAVE). |
Jiawen Zheng; Bo Li; Shengchuan Zhang; Shuang Wu; Liujuan Cao; Shouhong Ding; |
| 409 | Phrase-Level Temporal Relationship Mining for Temporal Sentence Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the problem of video temporal sentence localization, which aims to localize a target moment from videos according to a given language query. |
Minghang Zheng; Sizhe Li; Qingchao Chen; Yuxin Peng; Yang Liu; |
| 410 | Learning Semantic Degradation-Aware Guidance for Recognition-Driven Unsupervised Low-Light Image Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose to learn a Semantic Degradation-Aware Guidance (SDAG) that perceives the low-light degradation effect on semantic levels in a self-supervised manner, which is further utilized to guide the ULLIE methods. |
Naishan Zheng; Jie Huang; Man Zhou; Zizheng Yang; Qi Zhu; Feng Zhao; |
| 411 | Memory-Aided Contrastive Consensus Learning for Co-salient Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories. |
Peng Zheng; Jie Qin; Shuo Wang; Tian-Zhu Xiang; Huan Xiong; |
| 412 | MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MaskBooster for sparsely supervised instance segmentation (SpSIS) with comprehensive usage of pseudo masks. |
Shida Zheng; Chenshu Chen; Xi Yang; Wenming Tan; |
| 413 | RSPT: Reconstruct Surroundings and Predict Trajectory for Generalizable Active Object Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a framework called RSPT to form a structure-aware motion representation by Reconstructing Surroundings and Predicting the target Trajectory. |
Fangwei Zhong; Xiao Bi; Yudi Zhang; Wei Zhang; Yizhou Wang; |
| 414 | STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose STOA-VLP, a pre-training framework that jointly models object and action information across spatial and temporal dimensions. |
Weihong Zhong; Mao Zheng; Duyu Tang; Xuan Luo; Heng Gong; Xiaocheng Feng; Bing Qin; |
| 415 | Refined Semantic Enhancement Towards Frequency Diffusion for Video Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel Refined Semantic enhancement method towards Frequency Diffusion (RSFD), a captioning model that constantly perceives the linguistic representation of the infrequent tokens. |
Xian Zhong; Zipeng Li; Shuqin Chen; Kui Jiang; Chen Chen; Mang Ye; |
| 416 | Aesthetically Relevant Image Captioning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). |
Zhipeng Zhong; Fei Zhou; Guoping Qiu; |
| 417 | Polarization-Aware Low-Light Image Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Stokes-domain enhancement pipeline along with a dual-branch neural network to handle the problem in a polarization-aware manner. |
Chu Zhou; Minggui Teng; Youwei Lyu; Si Li; Chao Xu; Boxin Shi; |
| 418 | Progressive Bayesian Inference for Scribble-Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The scribble-supervised semantic segmentation is an important yet challenging task in the field of computer vision. To deal with the pixel-wise sparse annotation problem, we propose a Progressive Bayesian Inference (PBI) framework to boost the performance of the scribble-supervised semantic segmentation, which can effectively infer the semantic distribution of these unlabeled pixels to guide the optimization of the segmentation network. |
Chuanwei Zhou; Chunyan Xu; Zhen Cui; |
| 419 | Exploratory Inference Learning for Scribble Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel exploratory inference learning (EIL) framework, which facilitates efficient probing on unlabeled pixels and promotes selecting confident candidates for boosting the evolved segmentation. |
Chuanwei Zhou; Zhen Cui; Chunyan Xu; Cao Han; Jian Yang; |
| 420 | Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We observe that such a scheme is sub-optimal, i.e., for better distinguishing anomaly one needs to understand what is a normal state, and may yield a higher false alarm rate. To address this issue, we propose an Uncertainty Regulated Dual Memory Units (UR-DMU) model to learn both the representations of normal data and discriminative features of abnormal data. |
Hang Zhou; Junqing Yu; Wei Yang; |
| 421 | Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the first unsupervised framework for adverse weather optical flow via hierarchical motion-boundary adaptation. |
Hanyu Zhou; Yi Chang; Gang Chen; Luxin Yan; |
| 422 | PASS: Patch Automatic Skip Scheme for Efficient Real-Time Video Perception on Edge Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a general and task-independent Patch Automatic Skip Scheme (PASS), a novel end-to-end learning pipeline to support diverse video perception settings by decoupling acceleration and tasks. |
Qihua Zhou; Song Guo; Jun Pan; Jiacheng Liang; Zhenda Xu; Jingren Zhou; |
| 423 | Robust Feature Rectification of Pretrained Vision Models for Object Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a RObust FEature Rectification module (ROFER) to improve the performance of pretrained models against degradations. |
Shengchao Zhou; Gaofeng Meng; Zhaoxiang Zhang; Richard Yi Da Xu; Shiming Xiang; |
| 424 | Video Object of Interest Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a new computer vision task named video object of interest segmentation (VOIS). |
Siyuan Zhou; Chunru Zhan; Biao Wang; Tiezheng Ge; Yuning Jiang; Li Niu; |
| 425 | Tree-Structured Trajectory Encoding for Vision-and-Language Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that the sequential encoding may largely lose this kind of fine-grained structure in the trajectory, which could hamper the later state estimation and decision making. In order to solve this problem, this work proposes a novel tree-structured trajectory encoding strategy. |
Xinzhe Zhou; Yadong Mu; |
| 426 | Self-Supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Partial Spatio-Temporal Learning (PSTL) framework to exploit the local relationship from a partial skeleton sequences built by a unique spatio-temporal masking strategy. |
Yujie Zhou; Haodong Duan; Anyi Rao; Bing Su; Jiaqi Wang; |
| 427 | Debiased Fine-Tuning for Vision-Language Models By Prompt Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new paradigm for fine-tuning large-scale vision-language pre-trained models on downstream task, dubbed Prompt Regularization (ProReg). |
Beier Zhu; Yulei Niu; Saeil Lee; Minhoe Hur; Hanwang Zhang; |
| 428 | Improving Scene Text Image Super-resolution Via Dual Prior Modulation Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our work addresses these gaps and proposes a plug-and-play module dubbed Dual Prior Modulation Network (DPMN), which leverages dual image-level priors to bring performance gain over existing approaches. |
Shipeng Zhu; Zuoyan Zhao; Pengfei Fang; Hui Xue; |
| 429 | SRoUDA: Meta Self-Training for Robust Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. |
Wanqing Zhu; Jia-Li Yin; Bo-Hao Chen; Ximeng Liu; |
| 430 | Gradient-Based Graph Attention for Scene Text Image Super-resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel gradient-based graph attention method to embed patch-wise text layout contexts into image feature representations for high-resolution text image reconstruction in an implicit and elegant manner. |
Xiangyuan Zhu; Kehua Guo; Hui Fang; Rui Ding; Zheng Wu; Gerald Schaefer; |
| 431 | RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the dataset deficiency issue, a new RGB-D dataset named RGBD1K is released in this paper. |
Xue-Feng Zhu; Tianyang Xu; Zhangyong Tang; Zucheng Wu; Haodong Liu; Xiao Yang; Xiao-Jun Wu; Josef Kittler; |
| 432 | Learn More for Food Recognition Via Progressive Self-Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of locating multiple regions, we propose a Progressive Self-Distillation (PSD) method, which progressively enhances the ability of network to mine more details for food recognition. |
Yaohui Zhu; Linhu Liu; Jiang Tian; |
| 433 | Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. |
Zhiwen Zuo; Lei Zhao; Ailin Li; Zhizhong Wang; Zhanjie Zhang; Jiafu Chen; Wei Xing; Dongming Lu; |
| 434 | Improved Algorithms for Maximum Satisfiability and Its Special Cases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the (n,3)-MAXSAT problem, we design a O*(1.1749^n) algorithm improving on the previous record running time of O*(1.191^n). |
Kirill Brilliantov; Vasily Alferov; Ivan Bliznets; |
| 435 | Lifting (D)QBF Preprocessing and Solving Techniques to (D)SSAT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To date, no decision procedure has been implemented for solving DSSAT formulas. This work provides the first such tool by converting DSSAT into SSAT with dependency elimination, similar to converting dependency quantified Boolean formula (DQBF) to quantified Boolean formula (QBF). |
Che Cheng; Jie-Hong R. Jiang; |
| 436 | NuWLS: Improving Local Search for (Weighted) Partial MaxSAT By New Weighting Techniques Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we identify two issues of existing clause weighting techniques for (W)PMS, and propose two ideas correspondingly. |
Yi Chu; Shaowei Cai; Chuan Luo; |
| 437 | Separate But Equal: Equality in Belief Propagation for Single Cycle Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove that on a single cycle graph, belief equality can be avoided only when the algorithm converges to the optimal solution. |
Erel Cohen; Omer Lev; Roie Zivan; |
| 438 | Complexity of Reasoning with Cardinality Minimality Conditions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the CardMinSat problem, which asks, given a formula φ and an atom x, whether x is true in some cardinality-minimal model of φ. |
Nadia Creignou; Frédéric Olive; Johannes Schmidt; |
| 439 | DASH: A Distributed and Parallelizable Algorithm for Size-Constrained Submodular Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the SMCC problem in a distributed setting and propose the first MR algorithms with sublinear adaptive complexity. |
Tonmoy Dey; Yixin Chen; Alan Kuhnle; |
| 440 | SharpSSAT: A Witness-Generating Stochastic Boolean Satisfiability Solver Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a new witness-generating SSAT solver, SharpSSAT, which integrates techniques, including component caching, clause learning, and pure literal detection. |
Yu-Wei Fan; Jie-Hong R. Jiang; |
| 441 | Submodular Maximization Under The Intersection of Matroid and Knapsack Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the problem of submodular maximization under the intersection of two commonly used constraints, i.e., k-matroid constraint and m-knapsack constraint, and propose a new algorithm SPROUT by incorporating partial enumeration into the simultaneous greedy framework. |
Yu-Ran Gu; Chao Bian; Chao Qian; |
| 442 | A Framework to Design Approximation Algorithms for Finding Diverse Solutions in Combinatorial Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a main result, we propose a framework to design approximation algorithms for finding diverse solutions, which yields several outcomes including constant-factor approximation algorithms for finding diverse matchings in graphs and diverse common bases in two matroids and PTASes for finding diverse minimum cuts and interval schedulings. |
Tesshu Hanaka; Masashi Kiyomi; Yasuaki Kobayashi; Yusuke Kobayashi; Kazuhiro Kurita; Yota Otachi; |
| 443 | An Improved Approximation Algorithm for Wage Determination and Online Task Allocation in Crowd-Sourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We tackle an optimization problem for wage determination and online task allocation in crowd-sourcing and propose a fast 1-1/(k+3)^(1/2)-approximation algorithm, where k is the minimum of tasks’ budgets (numbers of possible assignments). |
Yuya Hikima; Yasunori Akagi; Hideaki Kim; Taichi Asami; |
| 444 | Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the notion of a correction function, and an additional penalty term in the loss function, modelling practical scenarios where an estimated optimal solution can be modified into a feasible solution after the true parameters are revealed, but at an additional cost. |
Xinyi Hu; Jasper C.H. Lee; Jimmy H.M. Lee; |
| 445 | Solving Explainability Queries with Quantification: The Case of Feature Relevancy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast with earlier work, that studied FRP for specific classifiers, this paper proposes a novel algorithm for the \fprob quantification problem which is applicable to any ML classifier that meets minor requirements. |
Xuanxiang Huang; Yacine Izza; Joao Marques-Silva; |
| 446 | Second-Order Quantified Boolean Logic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the second-order quantified Boolean logic with the following main results: First, we present a procedure of quantifier elimination converting SOQBFs to QBFs and a game interpretation of SOQBF semantics. Second, we devise a sound and complete refutation-proof system for SOQBF. Third, we develop an algorithm for countermodel extraction from a refutation proof. |
Jie-Hong R. Jiang; |
| 447 | Learning Markov Random Fields for Combinatorial Structures Via Sampling Through Lovász Local Lemma Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop NEural Lovasz Sampler (NELSON), which embeds the sampler through Lovasz Local Lemma (LLL) as a fully differentiable neural network layer. |
Nan Jiang; Yi Gu; Yexiang Xue; |
| 448 | Fast Converging Anytime Model Counting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper designs a new anytime approach called PartialKC for approximate model counting. |
Yong Lai; Kuldeep S. Meel; Roland H.C. Yap; |
| 449 | Finding Good Partial Assignments During Restart-Based Branch and Bound Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approach to find good partial assignments to jumpstart search at each restart for general COPs, which are identified by comparing different best solutions found in different restart runs. |
Hongbo Li; Jimmy H.M. Lee; |
| 450 | Hybrid Learning with New Value Function for The Maximum Common Induced Subgraph Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new value function and a hybrid selection strategy used in reinforcement learning to define a new vertex selection method, and propose a new BnB algorithm, called McSplitDAL, for MCIS. |
Yanli Liu; Jiming Zhao; Chu-Min Li; Hua Jiang; Kun He; |
| 451 | Self-Supervised Primal-Dual Learning for Constrained Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper takes a different route and proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference. |
Seonho Park; Pascal Van Hentenryck; |
| 452 | Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose retro branching; a simple yet effective approach to RL for branching. |
Christopher W. F. Parsonson; Alexandre Laterre; Thomas D. Barrett; |
| 453 | Constraint Optimization Over Semirings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The present work investigates the complexity of constraint optimization problems over semirings. |
A. Pavan; Kuldeep S. Meel; N. V. Vinodchandran; Arnab Bhattacharyya; |
| 454 | Generalized Confidence Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, it is restricted to a conjunction of binary inequalities In this paper, we generalize the Confidence constraint to any constraint and propose an implementation based on Multi-valued Decision Diagrams (MDDs). |
Guillaume Perez; Steve Malalel; Gael Glorian; Victor Jung; Alexandre Papadopoulos; Marie Pelleau; Wijnand Suijlen; Jean-Charles Régin; Arnaud Lallouet; |
| 455 | Circuit Minimization with QBF-Based Exact Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a rewriting method for Boolean circuits that minimizes small subcircuits with exact synthesis. |
Franz-Xaver Reichl; Friedrich Slivovsky; Stefan Szeider; |
| 456 | Probabilistic Generalization of Backdoor Trees with Application to SAT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the fact that in a ρ-backdoor-based decomposition a portion of hard subproblems remain, in practice the narrowing of the search space often allows solving the problem faster with such a backdoor than without it. In this paper, we significantly improve on the concept of ρ-backdoors by extending this concept to backdoor trees: we introduce ρ-backdoor trees, show the interconnections between SBS, ρ-backdoors, and the corresponding backdoor trees, and establish some new theoretical properties of backdoor trees. |
Alexander Semenov; Daniil Chivilikhin; Stepan Kochemazov; Ibragim Dzhiblavi; |
| 457 | The Expressive Power of Ad-Hoc Constraints for Modelling CSPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we ask a more fundamental question which bears on modelling constraints in a CSP as ad-hoc constraints, how the choice of constraints and operations affect tractability. |
Ruiwei Wang; Roland H.C. Yap; |
| 458 | Graphs, Constraints, and Search for The Abstraction and Reasoning Corpus Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Abstract Reasoning with Graph Abstractions (ARGA), a new object-centric framework that first represents images using graphs and then performs a search for a correct program in a DSL that is based on the abstracted graph space. |
Yudong Xu; Elias B. Khalil; Scott Sanner; |
| 459 | Eliminating The Impossible, Whatever Remains Must Be True: On Extracting and Applying Background Knowledge in The Context of Formal Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show how one can apply background knowledge to give more succinct “why” formal explanations, that are presumably easier to interpret by humans, and give more accurate “why not” explanations. |
Jinqiang Yu; Alexey Ignatiev; Peter J. Stuckey; Nina Narodytska; Joao Marques-Silva; |
| 460 | Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we observe that most local search (W)PMS solvers usually flip a single variable per iteration. |
Jiongzhi Zheng; Kun He; Jianrong Zhou; |
| 461 | LANCER: A Lifetime-Aware News Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By further developing the characteristics of the lifetime of news, then we present a novel approach for news recommendation, namely, Lifetime-Aware News reCommEndeR System (LANCER) that carefully exploits the lifetime of news during training and recommendation. |
Hong-Kyun Bae; Jeewon Ahn; Dongwon Lee; Sang-Wook Kim; |
| 462 | Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A small amount of recent CDR works have investigated privacy protection, while they still suffer from satisfying practical requirements (e.g., limited privacy-preserving ability) and preventing the potential risk of negative transfer. To address the above challenging problems, we propose a novel and unified privacy-preserving federated framework for dual-target CDR, namely P2FCDR. |
Gaode Chen; Xinghua Zhang; Yijun Su; Yantong Lai; Ji Xiang; Junbo Zhang; Yu Zheng; |
| 463 | Enhanced Multi-Relationships Integration Graph Convolutional Network for Inferring Substitutable and Complementary Items Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The substitutable items are interchangeable and might be compared with each other before purchasing, while the complementary items are used in conjunction and are usually bought together with the query item. In this paper, we focus on two issues of inferring the substitutable and complementary items: 1) how to model their mutual influence to improve the performance of downstream tasks, 2) how to further discriminate them by considering the strength of relationship for different item pairs. |
Huajie Chen; Jiyuan He; Weisheng Xu; Tao Feng; Ming Liu; Tianyu Song; Runfeng Yao; Yuanyuan Qiao; |
| 464 | PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. |
Jianhao Chen; Junyang Ren; Wentao Ding; Yuzhong Qu; |
| 465 | End-to-End Entity Linking with Hierarchical Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose to model the EL task as a hierarchical decision-making process and design a hierarchical reinforcement learning algorithm to solve the problem. |
Lihan Chen; Tinghui Zhu; Jingping Liu; Jiaqing Liang; Yanghua Xiao; |
| 466 | Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. |
Mingyang Chen; Wen Zhang; Zhen Yao; Yushan Zhu; Yang Gao; Jeff Z. Pan; Huajun Chen; |
| 467 | Dual Low-Rank Graph Autoencoder for Semantic and Topological Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, limited works on GAE were devoted to leveraging both semantic and topological graphs, and they only indirectly extracted the relationships between graphs via weights shared by features. To better capture the connections between nodes from these two types of graphs, this paper proposes a graph neural network dubbed Dual Low-Rank Graph AutoEncoder (DLR-GAE), which takes both semantic and topological homophily into consideration. |
Zhaoliang Chen; Zhihao Wu; Shiping Wang; Wenzhong Guo; |
| 468 | Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics. |
Junsu Cho; Dongmin Hyun; Dong won Lim; Hyeon jae Cheon; Hyoung-iel Park; Hwanjo Yu; |
| 469 | Learning Representations of Bi-level Knowledge Graphs for Reasoning Beyond Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we define a higher-level triplet to represent a relationship between triplets, e.g., where PrerequisiteFor is a higher-level relation. |
Chanyoung Chung; Joyce Jiyoung Whang; |
| 470 | Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. |
Yuanning Cui; Yuxin Wang; Zequn Sun; Wenqiang Liu; Yiqiao Jiang; Kexin Han; Wei Hu; |
| 471 | Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. |
Yizhou Dang; Enneng Yang; Guibing Guo; Linying Jiang; Xingwei Wang; Xiaoxiao Xu; Qinghui Sun; Hong Liu; |
| 472 | Rule Induction in Knowledge Graphs Using Linear Programming Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. |
Sanjeeb Dash; Joao Goncalves; |
| 473 | Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addition, due to the disturbance of incomplete observations in the data, random contextual conditions lead to spurious correlations between data and features, making the prediction of the model ineffective in special scenarios. To overcome this issue, we propose a Spatio-temporal Neural Structure Causal Model(STNSCM) from the perspective of causality. |
Pan Deng; Yu Zhao; Junting Liu; Xiaofeng Jia; Mulan Wang; |
| 474 | DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Compared with traditional compression methods, deep learning methods have intrinsic flaws for OoD generalization. In this work, we make the attempt to tackle this challenge via exploiting a zoo of Deep Autoregressive models (DAMix). |
Qishi Dong; Fengwei Zhou; Ning Kang; Chuanlong Xie; Shifeng Zhang; Jiawei Li; Heng Peng; Zhenguo Li; |
| 475 | Soft Target-Enhanced Matching Framework for Deep Entity Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Then, we propose a novel Soft Target-EnhAnced Matching (Steam) framework, which exploits the automatically generated soft targets as label-wise regularizers to constrain the model training. Specifically, Steam regards the EM model trained in previous iteration as a virtual teacher and takes its softened output as the extra regularizer to train the EM model in the current iteration. |
Wenzhou Dou; Derong Shen; Xiangmin Zhou; Tiezheng Nie; Yue Kou; Hang Cui; Ge Yu; |
| 476 | DropMessage: Unifying Random Dropping for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the propagated messages during the message-passing process. |
Taoran Fang; Zhiqing Xiao; Chunping Wang; Jiarong Xu; Xuan Yang; Yang Yang; |
| 477 | Contrastive Pre-training with Adversarial Perturbations for Check-In Sequence Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper we propose a contrastive pre-training model with adversarial perturbations for check-in sequence representation learning (CACSR). |
Letian Gong; Youfang Lin; Shengnan Guo; Yan Lin; Tianyi Wang; Erwen Zheng; Zeyu Zhou; Huaiyu Wan; |
| 478 | MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, previous GCL methods employ two view encoders with exactly the same neural architecture and tied parameters, which further harms the diversity of augmented views. To address this limitation, we propose a novel paradigm named model augmented GCL (MA-GCL), which will focus on manipulating the architectures of view encoders instead of perturbing graph inputs. |
Xumeng Gong; Cheng Yang; Chuan Shi; |
| 479 | Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Different from the existing research, this paper aims to provide a generic and dynamic representation learning method for crowd flow modeling. |
Liangzhe Han; Ruixing Zhang; Leilei Sun; Bowen Du; Yanjie Fu; Tongyu Zhu; |
| 480 | Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. |
Han Huang; Leilei Sun; Bowen Du; Weifeng Lv; |
| 481 | SAH: Shifting-Aware Asymmetric Hashing for Reverse K Maximum Inner Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the first subquadratic-time algorithm, i.e., Shifting-aware Asymmetric Hashing (SAH), to tackle the RkMIPS problem. |
Qiang Huang; Yanhao Wang; Anthony K. H. Tung; |
| 482 | Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the patch matching at the image domain is not robust to the variance of scale, shape, and illumination caused by the different viewing angles, and can not make full use of the rich texture information of the side information image. To resolve this issue, we propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes side information at the decoder of the distributed image compression model. |
Yujun Huang; Bin Chen; Shiyu Qin; Jiawei Li; Yaowei Wang; Tao Dai; Shu-Tao Xia; |
| 483 | Constrained Market Share Maximization By Signal-Guided Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel two-stage optimization method to address the challenges. |
Bo Hui; Yuchen Fang; Tian Xia; Sarp Aykent; Wei-Shinn Ku; |
| 484 | T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure Via Teacher-Student Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. |
Cuiying Huo; Di Jin; Yawen Li; Dongxiao He; Yu-Bin Yang; Lingfei Wu; |
| 485 | Detecting Sources of Healthcare Associated Infections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Prior techniques for showing submodularity, such as the "live graph" technique are not applicable for the load sharing model and our key technical contribution is to use a more sophisticated "coupling" technique to show the submodularity result. We propose algorithms for our two problem formulations by extending existing algorithmic results from submodular optimization and combining these with an expectation propagation heuristic for the load sharing model that leads to orders-of-magnitude speedup. |
Hankyu Jang; Andrew Fu; Jiaming Cui; Methun Kamruzzaman; B. Aditya Prakash; Anil Vullikanti; Bijaya Adhikari; Sriram V. Pemmaraju; |
| 486 | Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions’ flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. |
Jiahao Ji; Jingyuan Wang; Chao Huang; Junjie Wu; Boren Xu; Zhenhe Wu; Junbo Zhang; Yu Zheng; |
| 487 | PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel Propagation Delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. |
Jiawei Jiang; Chengkai Han; Wayne Xin Zhao; Jingyuan Wang; |
| 488 | Continuous Trajectory Generation Based on Two-Stage GAN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although many previous works have studied the problem of trajectory generation, the continuity of the generated trajectories has been neglected, which makes these methods useless for practical urban simulation scenarios. To solve this problem, we propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network, namely TS-TrajGen, which efficiently integrates prior domain knowledge of human mobility with model-free learning paradigm. |
Wenjun Jiang; Wayne Xin Zhao; Jingyuan Wang; Jiawei Jiang; |
| 489 | Let Graph Be The Go Board: Gradient-Free Node Injection Attack for Graph Neural Networks Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. |
Mingxuan Ju; Yujie Fan; Chuxu Zhang; Yanfang Ye; |
| 490 | GLCC: A General Framework for Graph-Level Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs. |
Wei Ju; Yiyang Gu; Binqi Chen; Gongbo Sun; Yifang Qin; Xingyuming Liu; Xiao Luo; Ming Zhang; |
| 491 | Parameterized Algorithms for Colored Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such smaller parameters are obtained by considering the difference between k or r and some lower bound on these values. We give both algorithms and lower bounds for Colored Clustering with such parameterizations. |
Leon Kellerhals; Tomohiro Koana; Pascal Kunz; Rolf Niedermeier; |
| 492 | Towards Reliable Item Sampling for Recommendation Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, despite existing efforts, there is still a lack of rigorous theoretical understanding of the proposed metric estimators, and the basic item sampling also suffers from the “blind spot” issue, i.e., estimation accuracy to recover the top-K metrics when K is small can still be rather substantial. In this paper, we provide an in-depth investigation into these problems and make two innovative contributions. First, we propose a new item-sampling estimator that explicitly optimizes the error with respect to the ground truth, and theoretically highlights its subtle difference against prior work. |
Dong Li; Ruoming Jin; Zhenming Liu; Bin Ren; Jing Gao; Zhi Liu; |
| 493 | Multiple Robust Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. |
Haoxuan Li; Quanyu Dai; Yuru Li; Yan Lyu; Zhenhua Dong; Xiao-Hua Zhou; Peng Wu; |
| 494 | Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. |
Jingtao Li; Xinyu Wang; Hengwei Zhao; Shaoyu Wang; Yanfei Zhong; |
| 495 | Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To train and deploy the CTR models efficiently and economically, it is necessary to compress their embedding tables. |
Shiwei Li; Huifeng Guo; Lu Hou; Wei Zhang; Xing Tang; Ruiming Tang; Rui Zhang; Ruixuan Li; |
| 496 | Signed Laplacian Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel signed graph representation learning framework, called Signed Laplacian Graph Neural Network (SLGNN), which combines the advantages of both. |
Yu Li; Meng Qu; Jian Tang; Yi Chang; |
| 497 | PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing work on cross-domain recommendation (CDR) reaches advanced and satisfying recommendation performance, but mostly neglects preserving privacy. To fill this gap, we propose a privacy-preserving generative cross-domain recommendation (PPGenCDR) framework for PPCDR. |
Xinting Liao; Weiming Liu; Xiaolin Zheng; Binhui Yao; Chaochao Chen; |
| 498 | COLA: Improving Conversational Recommender Systems By Collaborative Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose a collaborative augmentation (COLA) method to simultaneously improve both item representation learning and user preference modeling to address these issues. |
Dongding Lin; Jian Wang; Wenjie Li; |
| 499 | Scalable and Effective Conductance-Based Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on our framework, we propose two novel algorithms PCon_core and PCon_de with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. |
Longlong Lin; Ronghua Li; Tao Jia; |
| 500 | Multi-Domain Generalized Graph Meta Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the multi-domain generalized graph meta learning problem, which is challenging due to non-Euclidean data, inequivalent feature spaces, and heterogeneous distributions. |
Mingkai Lin; Wenzhong Li; Ding Li; Yizhou Chen; Guohao Li; Sanglu Lu; |
| 501 | IterDE: An Iterative Knowledge Distillation Framework for Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose IterDE, a novel knowledge distillation framework for KGEs. |
Jiajun Liu; Peng Wang; Ziyu Shang; Chenxiao Wu; |
| 502 | Learning By Applying: A General Framework for Mathematical Reasoning Via Enhancing Explicit Knowledge Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. |
Jiayu Liu; Zhenya Huang; ChengXiang Zhai; Qi Liu; |
| 503 | Low-Resource Personal Attribute Prediction from Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework PEARL to predict personal attributes from conversations by leveraging the abundant personal attribute knowledge from utterances under a low-resource setting in which no labeled utterances or external data are utilized. |
Yinan Liu; Hu Chen; Wei Shen; Jiaoyan Chen; |
| 504 | Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. |
Yixin Liu; Yizhen Zheng; Daokun Zhang; Vincent CS Lee; Shirui Pan; |
| 505 | On Generalized Degree Fairness in Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the bias in the context of node classification, we propose a novel GNN framework called Generalized Degree Fairness-centric Graph Neural Network (DegFairGNN). |
Zemin Liu; Trung-Kien Nguyen; Yuan Fang; |
| 506 | Time Series Contrastive Learning with Information-Aware Augmentations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the problem by encouraging both high fidelity and variety based on information theory. |
Dongsheng Luo; Wei Cheng; Yingheng Wang; Dongkuan Xu; Jingchao Ni; Wenchao Yu; Xuchao Zhang; Yanchi Liu; Yuncong Chen; Haifeng Chen; Xiang Zhang; |
| 507 | NQE: N-ary Query Embedding for Complex Query Answering Over Hyper-Relational Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. |
Haoran Luo; Haihong E; Yuhao Yang; Gengxian Zhou; Yikai Guo; Tianyu Yao; Zichen Tang; Xueyuan Lin; Kaiyang Wan; |
| 508 | FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, this paper presents a simple two-stream feature interaction model, namely FinalMLP, which employs only MLPs in both streams yet achieves surprisingly strong performance. |
Kelong Mao; Jieming Zhu; Liangcai Su; Guohao Cai; Yuru Li; Zhenhua Dong; |
| 509 | GMDNet: A Graph-Based Mixture Density Network for Estimating Packages’ Multimodal Travel Time Distribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Graph-based Mixture Density Network, named GMDNet, which takes the benefits of both graph neural network and mixture density network for estimating MTTD conditioned on graph-structure data (i.e., the logistics network). |
Xiaowei Mao; Huaiyu Wan; Haomin Wen; Fan Wu; Jianbin Zheng; Yuting Qiang; Shengnan Guo; Lixia Wu; Haoyuan Hu; Youfang Lin; |
| 510 | Logic and Commonsense-Guided Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. |
Guanglin Niu; Bo Li; |
| 511 | Graph Structure Learning on User Mobility Data for Social Relationship Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present Social Relationship Inference Network (SRINet), a novel Graph Neural Network (GNN) framework, to improve inference performance by learning to remove noisy data. |
Guangming Qin; Lexue Song; Yanwei Yu; Chao Huang; Wenzhe Jia; Yuan Cao; Junyu Dong; |
| 512 | Online Random Feature Forests for Learning in Varying Feature Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new online learning algorithm tailored for data streams described by varying feature spaces (VFS), wherein new features constantly emerge and old features may stop to be observed over various time spans. |
Christian Schreckenberger; Yi He; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt; |
| 513 | Scaling Law for Recommendation Models: Towards General-Purpose User Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we explore the possibility of general-purpose user representation learning by training a universal user encoder at large scales. |
Kyuyong Shin; Hanock Kwak; Su Young Kim; Max Nihlén Ramström; Jisu Jeong; Jung-Woo Ha; Kyung-Min Kim; |
| 514 | Cross-Domain Adaptative Learning for Online Advertisement Customer Lifetime Value Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, predicting LTV in real-world applications is not an easy task since the user consumption data is usually insufficient within a specific domain. To tackle this problem, we propose a novel cross-domain adaptative framework (CDAF) to leverage consumption data from different domains. |
Hongzu Su; Zhekai Du; Jingjing Li; Lei Zhu; Ke Lu; |
| 515 | Self-Supervised Interest Transfer Network Via Prototypical Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning. |
Guoqiang Sun; Yibin Shen; Sijin Zhou; Xiang Chen; Hongyan Liu; Chunming Wu; Chenyi Lei; Xianhui Wei; Fei Fang; |
| 516 | Opinion Optimization in Directed Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a problem of opinion optimization based on the popular Friedkin-Johnsen (FJ) model for opinion dynamics in an unweighted directed social network with n nodes and m edges. |
Haoxin Sun; Zhongzhi Zhang; |
| 517 | Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). |
Li Sun; Junda Ye; Hao Peng; Feiyang Wang; Philip S. Yu; |
| 518 | Self-Organization Preserved Graph Structure Learning with Principle of Relevant Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. |
Qingyun Sun; Jianxin Li; Beining Yang; Xingcheng Fu; Hao Peng; Philip S. Yu; |
| 519 | Efficient Embeddings of Logical Variables for Query Answering Over Incomplete Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach, however, can be computationally expensive during inference, and cannot deal with queries involving negation. In this paper, we propose a novel approach that addresses all of these limitations. |
Dingmin Wang; Yeyuan Chen; Bernardo Cuenca Grau; |
| 520 | Human-Instructed Deep Hierarchical Generative Learning for Automated Urban Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We rethink the urban planning generative task from a unique functionality perspective, where we summarize planning requirements into different functionality projections for better urban plan generation. To this end, we develop a three-stage generation process from a target area to zones to grids. |
Dongjie Wang; Lingfei Wu; Denghui Zhang; Jingbo Zhou; Leilei Sun; Yanjie Fu; |
| 521 | Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. |
Hongjun Wang; Jiyuan Chen; Tong Pan; Zipei Fan; Xuan Song; Renhe Jiang; Lingyu Zhang; Yi Xie; Zhongyi Wang; Boyuan Zhang; |
| 522 | Cross-Domain Graph Anomaly Detection Via Anomaly-Aware Contrastive Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. |
Qizhou Wang; Guansong Pang; Mahsa Salehi; Wray Buntine; Christopher Leckie; |
| 523 | WSiP: Wave Superposition Inspired Pooling for Dynamic Interactions-Aware Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a wave superposition inspired social pooling (Wave-pooling for short) method for dynamically aggregating the high-order interactions from both local and global neighbor vehicles. |
Renzhi Wang; Senzhang Wang; Hao Yan; Xiang Wang; |
| 524 | Beyond Graph Convolutional Network: An Interpretable Regularizer-Centered Optimization Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. |
Shiping Wang; Zhihao Wu; Yuhong Chen; Yong Chen; |
| 525 | Augmenting Affective Dependency Graph Via Iterative Incongruity Graph Learning for Sarcasm Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Errors produced during the graph construction step cannot be remedied and may accrue to the following stages, resulting in poor performance. To surmount the above limitations, we explore a novel Iterative Augmenting Affective Graph and Dependency Graph (IAAD) framework to jointly and iteratively learn the incongruity graph structure. |
Xiaobao Wang; Yiqi Dong; Di Jin; Yawen Li; Longbiao Wang; Jianwu Dang; |
| 526 | Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel training strategy that adaptively learns personalized imitation weights for each user to balance the contribution from the recent data and the amount of knowledge to be distilled from previous time periods. |
Yuening Wang; Yingxue Zhang; Antonios Valkanas; Ruiming Tang; Chen Ma; Jianye Hao; Mark Coates; |
| 527 | Online Semi-supervised Learning with Mix-Typed Streaming Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our key idea to solve the new problem is to leverage copula model to align the data instances with different feature spaces so as to make their distance measurable. |
Di Wu; Shengda Zhuo; Yu Wang; Zhong Chen; Yi He; |
| 528 | Few-Shot Composition Learning for Image Retrieval with Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of composition learning for image retrieval, for which we learn to retrieve target images with search queries in the form of a composition of a reference image and a modification text that describes desired modifications of the image. |
Junda Wu; Rui Wang; Handong Zhao; Ruiyi Zhang; Chaochao Lu; Shuai Li; Ricardo Henao; |
| 529 | ConTextual Masked Auto-Encoder for Dense Passage Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes CoT-MAE (ConTextual Masked Auto-Encoder), a simple yet effective generative pre-training method for dense passage retrieval. |
Xing Wu; Guangyuan Ma; Meng Lin; Zijia Lin; Zhongyuan Wang; Songlin Hu; |
| 530 | Jointly Imputing Multi-View Data with Optimal Transport Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a generative imputation model named Git with optimal transport theory to jointly impute the missing features/values, conditional on all observed values from the multi-view data. |
Yangyang Wu; Xiaoye Miao; Xinyu Huang; Jianwei Yin; |
| 531 | Knowledge Graph Embedding By Normalizing Flows Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory. |
Changyi Xiao; Xiangnan He; Yixin Cao; |
| 532 | Temporal Knowledge Graph Reasoning with Historical Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a new event forecasting model called Contrastive Event Network (CENET), based on a novel training framework of historical contrastive learning. |
Yi Xu; Junjie Ou; Hui Xu; Luoyi Fu; |
| 533 | SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Further, we propose a Spectrum Concentrated Implicit neural compression (SCI) which adaptively partitions the complex biomedical data into blocks matching INR’s concentrated spectrum envelop, and design a funnel shaped neural network capable of representing each block with a small number of parameters. Based on this design, we conduct compression via optimization under given budget and allocate the available parameters with high representation accuracy. |
Runzhao Yang; Tingxiong Xiao; Yuxiao Cheng; Qianni Cao; Jinyuan Qu; Jinli Suo; Qionghai Dai; |
| 534 | Unsupervised Legal Evidence Retrieval Via Contrastive Learning with Approximate Aggregated Positive Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To build a practical Legal AI application and free the judges from the manually searching work, we introduce the task of Legal Evidence Retrieval, which aims at automatically retrieving the precise fact-related verbal evidence within a single case. |
Feng Yao; Jingyuan Zhang; Yating Zhang; Xiaozhong Liu; Changlong Sun; Yun Liu; Weixing Shen; |
| 535 | One-for-All: Proposal Masked Cross-Class Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One of the most challenges for anomaly detection (AD) is how to learn one unified and generalizable model to adapt to multi-class especially cross-class settings: the model is trained with normal samples from seen classes with the objective to detect anomalies from both seen and unseen classes. In this work, we propose a novel Proposal Masked Anomaly Detection (PMAD) approach for such challenging multi- and cross-class anomaly detection. |
Xincheng Yao; Chongyang Zhang; Ruoqi Li; Jun Sun; Zhenyu Liu; |
| 536 | Analogical Inference Enhanced Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. |
Zhen Yao; Wen Zhang; Mingyang Chen; Yufeng Huang; Yi Yang; Huajun Chen; |
| 537 | A Noise-Tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleaving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of the previous studies only learn restricted SOIREs and are not robust on noisy data. To tackle these issues, we propose a noise-tolerant differentiable learning approach SOIREDL for SOIRE. |
Rongzhen Ye; Tianqu Zhuang; Hai Wan; Jianfeng Du; Weilin Luo; Pingjia Liang; |
| 538 | Learning from The Wisdom of Crowds: Exploiting Similar Sessions for Session Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel Similar Session-enhanced Ranking (SSR) model to improve the session search performance using historical sessions with similar intents. |
Yuhang Ye; Zhonghua Li; Zhicheng Dou; Yutao Zhu; Changwang Zhang; Shangquan Wu; Zhao Cao; |
| 539 | Next POI Recommendation with Dynamic Graph and Explicit Dependency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose the Sequence-based Neighbour search and Prediction Model (SNPM) for next POI recommendation. |
Feiyu Yin; Yong Liu; Zhiqi Shen; Lisi Chen; Shuo Shang; Peng Han; |
| 540 | Predicting Temporal Sets with Simplified Fully Connected Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a succinct architecture that is solely built on the Simplified Fully Connected Networks (SFCNs) for temporal sets prediction to bring both effectiveness and efficiency together. |
Le Yu; Zihang Liu; Tongyu Zhu; Leilei Sun; Bowen Du; Weifeng Lv; |
| 541 | Learning to Count Isomorphisms with Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. |
Xingtong Yu; Zemin Liu; Yuan Fang; Xinming Zhang; |
| 542 | Untargeted Attack Against Federated Recommendation Systems Via Poisonous Item Embeddings and The Defense Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. |
Yang Yu; Qi Liu; Likang Wu; Runlong Yu; Sanshi Lei Yu; Zaixi Zhang; |
| 543 | Practical Cross-System Shilling Attacks with Limited Access to Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze the properties a practical shilling attack method should have and propose a new concept of Cross-system Attack. |
Meifang Zeng; Ke Li; Bingchuan Jiang; Liujuan Cao; Hui Li; |
| 544 | Query-Aware Quantization for Maximum Inner Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a quantization method based on the distribution of queries combined with sampled softmax. |
Jin Zhang; Defu Lian; Haodi Zhang; Baoyun Wang; Enhong Chen; |
| 545 | TOT：Topology-Aware Optimal Transport for Multimodal Hate Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The leveraged cross-modal attention mechanisms also suffer from the distributional modality gap and lack logical interpretability. To address these semantic gap issues, we propose TOT: a topology-aware optimal transport framework to decipher the implicit harm in memes scenario, which formulates the cross-modal aligning problem as solutions for optimal transportation plans. |
Linhao Zhang; Li Jin; Xian Sun; Guangluan Xu; Zequn Zhang; Xiaoyu Li; Nayu Liu; Qing Liu; Shiyao Yan; |
| 546 | Cross-Domain Few-Shot Graph Classification with A Reinforced Task Coordinator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The combat with the domain shift issue is hindered due to the coarse utilization of source domains and the ignorance of accessible prompts. To address these challenges, in this paper, we design a novel Cross-domain Task Coordinator to leverage a small set of labeled target domain data as prompt tasks, then model the association and discover the relevance between meta-tasks from the source domain and the prompt tasks. |
Qiannan Zhang; Shichao Pei; Qiang Yang; Chuxu Zhang; Nitesh V. Chawla; Xiangliang Zhang; |
| 547 | AutoSTL: Automated Spatio-Temporal Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. |
Zijian Zhang; Xiangyu Zhao; Hao Miao; Chunxu Zhang; Hongwei Zhao; Junbo Zhang; |
| 548 | Fair Representation Learning for Recommendation: A Mutual Information Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we re-define recommendation fairness with a novel two-fold mutual information objective. |
Chen Zhao; Le Wu; Pengyang Shao; Kun Zhang; Richang Hong; Meng Wang; |
| 549 | Deep Graph Structural Infomax Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an effective model called Deep Graph Structural Infomax (DGSI) to learn node representation. |
Wenting Zhao; Gongping Xu; Zhen Cui; Siqiang Luo; Cheng Long; Tong Zhang; |
| 550 | Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze the physical concepts affecting the generation of multimode traffic flow from the perspective of the observation generation principle and propose a Causal Conditional Hidden Markov Model (CCHMM) to predict multimodal traffic flow. |
Yu Zhao; Pan Deng; Junting Liu; Xiaofeng Jia; Mulan Wang; |
| 551 | ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a method to leverage weak/noisy labels (e.g., risk scores generated by machine rules for detecting malware) that are cheaper to obtain for anomaly detection. |
Yue Zhao; Guoqing Zheng; Subhabrata Mukherjee; Robert McCann; Ahmed Awadallah; |
| 552 | A Provable Framework of Learning Graph Embeddings Via Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose GELSUMM, a well-formulated graph embedding learning framework based on graph sum-marization, in which we show the theoretical ground of learn-ing from summary graphs and the restoration with the three well-known graph embedding approaches in a closed form.Through extensive experiments on real-world datasets, we demonstrate that our methods can learn graph embeddings with matching or better performance on downstream tasks.This work provides theoretical analysis for learning node em-beddings via summarization and helps explain and under-stand the mechanism of the existing works. |
Houquan Zhou; Shenghua Liu; Danai Koutra; Huawei Shen; Xueqi Cheng; |
| 553 | GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To resolve the problem, in this paper we seek to automatically augment the minority classes from the massive unlabelled nodes of the graph. |
Mengting Zhou; Zhiguo Gong; |
| 554 | Detecting Multivariate Time Series Anomalies with Zero Known Label Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose MTGFlow, an unsupervised anomaly detection approach forMultivariate Time series anomaly detection via dynamic Graph and entityaware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. |
Qihang Zhou; Jiming Chen; Haoyu Liu; Shibo He; Wenchao Meng; |
| 555 | GRLSTM: Trajectory Similarity Computation with Graph-Based Residual LSTM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods have been designed primarily for trajectories in Euclidean space, which overlooks the fact that real-world trajectories are often generated on road networks. This paper addresses this gap by proposing a novel framework, called GRLSTM (Graph-based Residual LSTM). |
Silin Zhou; Jing Li; Hao Wang; Shuo Shang; Peng Han; |
| 556 | Heterogeneous Region Embedding with Prompt Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework, HREP (Heterogeneous Region Embedding with Prompt learning), which addresses both intra-region and inter-region correlations through two key modules: Heterogeneous Region Embedding (HRE) and prompt learning for different downstream tasks. |
Silin Zhou; Dan He; Lisi Chen; Shuo Shang; Peng Han; |
| 557 | Show Me The Way! Bilevel Search for Synthesizing Programmatic Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce a bilevel search algorithm that searches concurrently in the space of programs and in a space of state features. |
David S. Aleixo; Levi H.S. Lelis; |
| 558 | Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on split population multi-variate survival processes, we develop a discriminative approach that, unlike prior works, leads to a single model for predicting whether individual users of an information network will engage a given cascade for arbitrary forecast horizons and observation periods. |
Akshay Aravamudan; Xi Zhang; Georgios C. Anagnostopoulos; |
| 559 | Principled Data-Driven Decision Support for Cyber-Forensic Investigations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this data-driven approach, called DISCLOSE, is based on a heuristic that utilizes only a subset of the available information and does not approximate optimal decisions. To improve upon this heuristic, we introduce a principled approach for data-driven decision support for cyber-forensic investigations. |
Soodeh Atefi; Sakshyam Panda; Emmanouil Panaousis; Aron Laszka; |
| 560 | BETA-CD: A Bayesian Meta-Learned Cognitive Diagnosis Framework for Personalized Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general Bayesian mETA-learned Cognitive Diagnosis framework (BETA-CD), which addresses the two challenges by prior knowledge exploitation and model uncertainty quantification, respectively. |
Haoyang Bi; Enhong Chen; Weidong He; Han Wu; Weihao Zhao; Shijin Wang; Jinze Wu; |
| 561 | Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. |
Xianyu Chen; Jian Shen; Wei Xia; Jiarui Jin; Yakun Song; Weinan Zhang; Weiwen Liu; Menghui Zhu; Ruiming Tang; Kai Dong; Dingyin Xia; Yong Yu; |
| 562 | Unsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose a Single-Cell Deep Embedding Fusion Representation (scDEFR) model, which develop a deep embedded fusion representation to learn fused heterogeneous latent embedding that contains both the transcriptome gene-level information and the cell topology information. |
Yue Cheng; Yanchi Su; Zhuohan Yu; Yanchun Liang; Ka-Chun Wong; Xiangtao Li; |
| 563 | Constrained Submodular Optimization for Vaccine Design Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, the genetic variability of the human immune system makes it difficult to design peptide vaccines that provide widespread immunity in vaccinated populations. We introduce a framework for evaluating and designing peptide vaccines that uses probabilistic machine learning models, and demonstrate its ability to produce designs for a SARS-CoV-2 vaccine that outperform previous designs. |
Zheng Dai; David K. Gifford; |
| 564 | Flow-Based Robust Watermarking with Invertible Noise Layer for Black-Box Distortions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, one potential drawback of such a framework is that the encoder and the decoder may not be well coupled, resulting in the fact that the encoder may embed some redundant features into the host image thus influencing the invisibility and robustness of the whole algorithm. To address this limitation, this paper proposes a flow-based robust watermarking framework. |
Han Fang; Yupeng Qiu; Kejiang Chen; Jiyi Zhang; Weiming Zhang; Ee-Chien Chang; |
| 565 | Identifying and Eliminating Majority Illusion in Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From a system engineering point of view, this motivates the search for algorithms to detect and, where possible, correct this undesirable phenomenon. In this paper we initiate the computational study of majority illusion in social networks, providing NP-hardness and parametrised complexity results for its occurrence and elimination. |
Umberto Grandi; Lawqueen Kanesh; Grzegorz Lisowski; Ramanujan Sridharan; Paolo Turrini; |
| 566 | A Domain-Knowledge-Inspired Music Embedding Space and A Novel Attention Mechanism for Symbolic Music Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. |
Zixun Guo; Jaeyong Kang; Dorien Herremans; |
| 567 | MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring Based on A Dual-CNN Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Leveraging recent progress on deep learning techniques, we design a new neural NILM model {\em Multi-State Dual CNN} (MSDC). |
Jialing He; Jiamou Liu; Zijian Zhang; Yang Chen; Yiwei Liu; Bakh Khoussainov; Liehuang Zhu; |
| 568 | Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new setting, optimize-and-estimate structured bandits. |
Peter Henderson; Ben Chugg; Brandon Anderson; Kristen Altenburger; Alex Turk; John Guyton; Jacob Goldin; Daniel E. Ho; |
| 569 | MGTCF: Multi-Generator Tropical Cyclone Forecasting with Heterogeneous Meteorological Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods lack a generic framework for adapting heterogeneous meteorological data and do not focus on the importance of the environment. Therefore, we propose a Multi-Generator Tropical Cyclone Forecasting model (MGTCF), a generic, extensible, multi-modal TC prediction model with the key modules of Generator Chooser Network (GC-Net) and Environment Net (Env-Net). |
Cheng Huang; Cong Bai; Sixian Chan; Jinglin Zhang; YuQuan Wu; |
| 570 | MDM: Molecular Diffusion Model for 3D Molecule Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel diffusion model to address those two challenges. |
Lei Huang; Hengtong Zhang; Tingyang Xu; Ka-Chun Wong; |
| 571 | Learning Chemical Rules of Retrosynthesis with Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on template-free methods which are known to be less bothered by the template generalization issue and the atom mapping challenge. |
Yinjie Jiang; Ying WEI; Fei Wu; Zhengxing Huang; Kun Kuang; Zhihua Wang; |
| 572 | Online Symbolic Regression with Informative Query Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose QUOSR, a query-based framework for online symbolic regression that can automatically obtain informative data in an iterative manner. |
Pengwei Jin; Di Huang; Rui Zhang; Xing Hu; Ziyuan Nan; Zidong Du; Qi Guo; Yunji Chen; |
| 573 | Repair Is Nearly Generation: Multilingual Program Repair with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce RING, a multilingual repair engine powered by a large language model trained on code (LLMC) such as Codex. |
Harshit Joshi; José Cambronero Sanchez; Sumit Gulwani; Vu Le; Gust Verbruggen; Ivan Radiček; |
| 574 | Heterogeneous Graph Learning for Multi-Modal Medical Data Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data. |
Sein Kim; Namkyeong Lee; Junseok Lee; Dongmin Hyun; Chanyoung Park; |
| 575 | Rolling Horizon Based Temporal Decomposition for The Offline Pickup and Delivery Problem with Time Windows Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. |
Youngseo Kim; Danushka Edirimanna; Michael Wilbur; Philip Pugliese; Aron Laszka; Abhishek Dubey; Samitha Samaranayake; |
| 576 | GRIP: Graph Representation of Immune Repertoire Using Graph Neural Network and Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present our novel method graph representation of immune repertoire (GRIP), which analyzes the immune repertoire as a hier-archical graph structure and utilize the collection of graph neural network followed by graph pooling and transformer to efficiently represents the immune reper-toire as an embedding vector. |
Yongju Lee; Hyunho Lee; Kyoungseob Shin; Sunghoon Kwon; |
| 577 | LagNet: Deep Lagrangian Mechanics for Plug-and-Play Molecular Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel plug-and-play architecture LagNet by simulating molecular force field only with parameterized position coordinates, which implements Lagrangian mechanics to learn molecular representation by preserving 3D conformation without obeying any additional restrictions. |
Chunyan Li; Junfeng Yao; Jinsong Su; Zhaoyang Liu; Xiangxiang Zeng; Chenxi Huang; |
| 578 | Steganography of Steganographic Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It raises concerns on how to covertly transmit the steganographic network in public channels, which is a crucial stage in the pipeline of steganography in real world applications. To address such an issue, we propose a novel scheme for steganography of steganographic networks in this paper. |
Guobiao Li; Sheng Li; Meiling Li; Xinpeng Zhang; Zhenxing Qian; |
| 579 | PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework of Prediction-Explanation Network (PEN) jointly modeling text streams and price streams with alignment. |
Shuqi Li; Weiheng Liao; Yuhan Chen; Rui Yan; |
| 580 | Decision-Making Context Interaction Network for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Decision-Making Context Interaction Network (DCIN), which deploys a carefully designed Context Interaction Unit (CIU) to learn decision-making contexts and thus benefits CTR prediction. |
Xiang Li; Shuwei Chen; Jian Dong; Jin Zhang; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 581 | Fine-Grained Position Helps Memorizing More, A Novel Music Compound Transformer Model with Feature Interaction Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, in this work, we propose an improved compound Transformer model for music understanding. Specifically, we propose an attribute embedding fusion module and a novel position encoding scheme with absolute-relative consideration. |
Zuchao Li; Ruhan Gong; Yineng Chen; Kehua Su; |
| 582 | Zero-Shot Rumor Detection with Propagation Structure Via Prompt Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages. |
Hongzhan Lin; Pengyao Yi; Jing Ma; Haiyun Jiang; Ziyang Luo; Shuming Shi; Ruifang Liu; |
| 583 | On Manipulating Weight Predictions in Signed Weighted Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on the problem of evading FGA—an edge weight prediction method for signed weighted networks by Kumar et al. 2016. |
Tomasz Lizurej; Tomasz Michalak; Stefan Dziembowski; |
| 584 | Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing code completion benchmarks also lack such context. To resolve these restrictions we curate a new dataset of permissively licensed Python packages that includes full projects and their dependencies and provide tools to extract non-local information with the help of program analyzers. We then focus on the task of function call argument completion which requires predicting the arguments to function calls. We show that existing code completion models do not yield good results on our completion task. |
Hengzhi Pei; Jinman Zhao; Leonard Lausen; Sheng Zha; George Karypis; |
| 585 | MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing multilingual training overlooks the language-specific information which is crucial for modeling source code across different programming languages, while only focusing on learning a unified model with shared parameters among different languages for language-agnostic information modeling. To address this problem, we propose MetaTPTrans, a meta learning approach for multilingual code representation learning. |
Weiguo Pian; Hanyu Peng; Xunzhu Tang; Tiezhu Sun; Haoye Tian; Andrew Habib; Jacques Klein; Tegawendé F. Bissyandé; |
| 586 | HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, which is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identities, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. |
Ling Sun; Yuan Rao; Yuqian Lan; Bingcan Xia; Yangyang Li; |
| 587 | Defending Against Backdoor Attacks in Natural Language Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, little effort has been invested to how backdoor attacks can affect current NLG models and how to defend against these attacks. In this work, by giving a formal definition of backdoor attack and defense, we investigate this problem on two important NLG tasks, machine translation and dialog generation. |
Xiaofei Sun; Xiaoya Li; Yuxian Meng; Xiang Ao; Lingjuan Lyu; Jiwei Li; Tianwei Zhang; |
| 588 | GenéLive! Generating Rhythm Actions in Love Live! Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article presents our generative model for rhythm action games together with applications in business operation. |
Atsushi Takada; Daichi Yamazaki; Yudai Yoshida; Nyamkhuu Ganbat; Takayuki Shimotomai; Naoki Hamada; Likun Liu; Taiga Yamamoto; Daisuke Sakurai; |
| 589 | Deepfake Video Detection Via Facial Action Dependencies Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the deepfake video detection problem into a graph classification task, and propose a novel paradigm named Facial Action Dependencies Estimation (FADE) for deepfake video detection. |
Lingfeng Tan; Yunhong Wang; Junfu Wang; Liang Yang; Xunxun Chen; Yuanfang Guo; |
| 590 | Contrastive Attention Networks for Attribution of Early Modern Print Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop machine learning techniques to identify unknown printers in early modern (c.~1500–1800) English printed books. |
Nikolai Vogler; Kartik Goyal; Kishore PV Reddy; Elizaveta Pertseva; Samuel V. Lemley; Christopher N. Warren; Max G’Sell; Taylor Berg-Kirkpatrick; |
| 591 | AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this is the first work that proposes an Adaptive and Safe-Certified DRL (AdapSafe) algorithm for frequency control to simultaneously address the aforementioned challenges. |
Xu Wan; Mingyang Sun; Boli Chen; Zhongda Chu; Fei Teng; |
| 592 | Don’t Predict Counterfactual Values, Predict Expected Values Instead Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple yet powerful modification to the CFVs estimation process, which consists in utilizing a deep neural network to estimate only the EV factor of CFV. |
Jeremiasz Wołosiuk; Maciej Świechowski; Jacek Mańdziuk; |
| 593 | Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it has been widely accepted that substructures play a dominant role in identifying and determining molecular properties. To address such issues, we formulate heterogeneous molecular graphs (HMGs) and introduce a novel architecture to exploit both molecular motifs and 3D geometry. |
Fang Wu; Dragomir Radev; Stan Z. Li; |
| 594 | DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. |
Fang Wu; Stan Z. Li; |
| 595 | Retrosynthesis Prediction with Local Template Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce RetroKNN, a local reaction template retrieval method to further boost the performance of template-based systems with non-parametric retrieval. |
Shufang Xie; Rui Yan; Junliang Guo; Yingce Xia; Lijun Wu; Tao Qin; |
| 596 | Multi-Relational Contrastive Learning Graph Neural Network for Drug-Drug Interaction Event Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new method, Multi-Relational Contrastive learning Graph Neural Network, MRCGNN for brevity, to predict DDI events. |
Zhankun Xiong; Shichao Liu; Feng Huang; Ziyan Wang; Xuan Liu; Zhongfei Zhang; Wen Zhang; |
| 597 | Tighter Robust Upper Bounds for Options Via No-Regret Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we give new robust upper bounds for option prices based on a novel η-momentum trading strategy. |
Shan Xue; Ye Du; Liang Xu; |
| 598 | KerPrint: Local-Global Knowledge Graph Enhanced Diagnosis Prediction for Retrospective and Prospective Interpretations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a time-aware KG attention method to solve the problem of knowledge decay over time for trustworthy retrospective interpretation. |
Kai Yang; Yongxin Xu; Peinie Zou; Hongxin Ding; Junfeng Zhao; Yasha Wang; Bing Xie; |
| 599 | Multi-Label Few-Shot ICD Coding As Autoregressive Generation with Prompt Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge – Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically. This study addresses the long-tail challenge by transforming this multi-label classification task into an autoregressive generation task. |
Zhichao Yang; Sunjae Kwon; Zonghai Yao; Hong Yu; |
| 600 | DMIS: Dynamic Mesh-Based Importance Sampling for Training Physics-Informed Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For the training of PINNs, existing methods suffer from the problems of inefficiency and unstable convergence, since the PDE residuals require calculating automatic differentiation. In this paper, we propose Dynamic Mesh-based Importance Sampling (DMIS) to tackle these problems. |
Zijiang Yang; Zhongwei Qiu; Dongmei Fu; |
| 601 | Bootstrapping Multi-View Representations for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel scheme of Bootstrapping Multi-view Representations (BMR) for fake news detection. |
Qichao Ying; Xiaoxiao Hu; Yangming Zhou; Zhenxing Qian; Dan Zeng; Shiming Ge; |
| 602 | Overcoming Forgetting in Fine-Grained Urban Flow Inference Via Adaptive Knowledge Replay Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make the first step in FUFI and present CUFAR — Continual Urban Flow inference with Adaptive knowledge Replay — a novel framework for inferring the fine-grained citywide traffic flows. |
Haoyang Yu; Xovee Xu; Ting Zhong; Fan Zhou; |
| 603 | Generalized Cell Type Annotation and Discovery for Single-Cell RNA-Seq Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this task, cells of seen cell types are given class labels, while cells of novel cell types are given cluster labels instead of a unified “unassigned” label. To address this problem, we carefully design a comprehensive evaluation benchmark and propose a novel end-to-end algorithm framework called scGAD. |
Yuyao Zhai; Liang Chen; Minghua Deng; |
| 604 | Mining and Applying Composition Knowledge of Dance Moves for Style-Concentrated Dance Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Without the stylized prior knowledge, these approaches are not promising to generate controllable style or diverse moves for each dance style, nor new dances complying with stylized knowledge. To address this issue, we propose a novel music-to-dance generation framework guided by style embedding, considering both input music and stylized dancing knowledge. |
Xinjian Zhang; Su Yang; Yi Xu; Weishan Zhang; Longwen Gao; |
| 605 | Yet Another Traffic Classifier: A Masked Autoencoder Based Traffic Transformer with Multi-Level Flow Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the following limitations remain: (1) the traffic representation is simply generated from raw packet bytes, resulting in the absence of important information; (2) the model structure of directly applying deep learning algorithms does not take traffic characteristics into account; and (3) scenario-specific classifier training usually requires a labor-intensive and time-consuming process to label data. In this paper, we introduce a masked autoencoder (MAE) based traffic transformer with multi-level flow representation to tackle these problems. |
Ruijie Zhao; Mingwei Zhan; Xianwen Deng; Yanhao Wang; Yijun Wang; Guan Gui; Zhi Xue; |
| 606 | Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we attempt to detect loan fraud users from cross domain heterogeneous data views, including user attributes, installed app lists, app installation behaviors, and app-in logs, which compensate for the lack of historical loan records. |
Sha Zhao; Yongrui Huang; Ling Chen; Chunping Wang; Shijian Li; Lei Chen; Gang Pan; |
| 607 | Sparse Maximum Margin Learning from Multimodal Human Behavioral Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a multimodal data fusion framework to systematically analyze human behavioral data from specialized domains that are inherently dynamic, sparse, and heterogeneous. |
Ervine Zheng; Qi Yu; Zhi Zheng; |
| 608 | Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach for solving resource allocation problems to mitigate the side-effects. |
Hao Zhou; Shaoming Li; Guibin Jiang; Jiaqi Zheng; Dong Wang; |
| 609 | Mediated Cheap Talk Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study an information design problem with two informed senders and a receiver in which, in contrast to traditional Bayesian persuasion settings, senders do not have commitment power. |
Itai Arieli; Ivan Geffner; Moshe Tennenholtz; |
| 610 | Bidding Graph Games with Partially-Observable Budgets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we initiate the study of partial-information bidding games: we study bidding games in which a player’s initial budget is drawn from a known probability distribution. |
Guy Avni; Ismael Jecker; Đorđe Žikelić; |
| 611 | Fairness Concepts for Indivisible Items with Externalities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This allows us to capture scenarios in which agents benefit from or compete against one another. We extend the well-studied properties of envy-freeness up to one item (EF1) and envy-freeness up to any item (EFX) to this setting, and we propose a new fairness concept called general fair share (GFS), which applies to a more general public decision making model. |
Haris Aziz; Warut Suksompong; Zhaohong Sun; Toby Walsh; |
| 612 | Finding Fair Allocations Under Budget Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the computation (or even existence) of exact EFk allocations remained an intriguing open problem. We make notable progress towards this by proposing a simple, greedy, polynomial-time algorithm that computes EF2 allocations under budget constraints. |
Siddharth Barman; Arindam Khan; Sudarshan Shyam; K. V. N. Sreenivas; |
| 613 | Now We’re Talking: Better Deliberation Groups Through Submodular Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Assembly members form their recommendations through a sequence of discussions in small groups (deliberation), in which group members exchange arguments and experiences. We seek to support this process through optimization, by studying how to assign participants to discussion groups over multiple sessions, in a way that maximizes interaction between participants and satisfies diversity constraints within each group. |
Jake Barrett; Kobi Gal; Paul Gölz; Rose M. Hong; Ariel D. Procaccia; |
| 614 | Causes of Stability in Dynamic Coalition Formation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the formation of stable outcomes via simple dynamics in cardinal hedonic games, where the utilities of agents change over time depending on the history of the coalition formation process. |
Niclas Boehmer; Martin Bullinger; Anna Maria Kerkmann; |
| 615 | Properties of Position Matrices and Their Elections Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We show that counting elections that generate a given position matrix is #P-complete. Consequently, sampling such elections uniformly at random seems challenging and we propose a simpler algorithm, without hard guarantees. |
Niclas Boehmer; Jin-Yi Cai; Piotr Faliszewski; Austen Z. Fan; Łukasz Janeczko; Andrzej Kaczmarczyk; Tomasz Wąs; |
| 616 | Rank Aggregation Using Scoring Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To aggregate rankings into a social ranking, one can use scoring systems such as Plurality, Veto, and Borda. We distinguish three types of methods: ranking by score, ranking by repeatedly choosing a winner that we delete and rank at the top, and ranking by repeatedly choosing a loser that we delete and rank at the bottom. |
Niclas Boehmer; Robert Bredereck; Dominik Peters; |
| 617 | Proportionality in Approval-Based Participatory Budgeting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study proportionality axioms with respect to large classes of approval-based satisfaction functions. |
Markus Brill; Stefan Forster; Martin Lackner; Jan Maly; Jannik Peters; |
| 618 | Multiwinner Voting with Possibly Unavailable Candidates Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to parallelize the invitation process, we investigate the computation of safe parallel queries, and show that it is often hard. |
Markus Brill; Hayrullah Dindar; Jonas Israel; Jérôme Lang; Jannik Peters; Ulrike Schmidt-Kraepelin; |
| 619 | Fair Division with Prioritized Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the fair division problem of indivisible items. |
Xiaolin Bu; Zihao Li; Shengxin Liu; Jiaxin Song; Biaoshuai Tao; |
| 620 | Topological Distance Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a class of strategic games in which agents are assigned to nodes of a topology graph and the utility of an agent depends on both the agent’s inherent utilities for other agents as well as her distance from these agents on the topology graph. |
Martin Bullinger; Warut Suksompong; |
| 621 | Game Implementation: What Are The Obstructions? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In many applications, we want to influence the decisions of independent agents by designing incentives for their actions. We revisit a fundamental problem in this area, called GAME IMPLEMENTATION: Given a game in standard form and a set of desired strategies, can we design a set of payment promises such that if the players take the payment promises into account, then all undominated strategies are desired? |
Jiehua Chen; Seyedeh Negar Layegh Khavidaki; Sebastian Vincent Haydn; Sofia Simola; Manuel Sorge; |
| 622 | A Pair-Approximation Method for Modelling The Dynamics of Multi-Agent Stochastic Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With a novel use of pair-approximation method, we develop a formal model for myopic Q-learning in stochastic games with symmetric state transition. |
Chen Chu; Zheng Yuan; Shuyue Hu; Chunjiang Mu; Zhen Wang; |
| 623 | Complexity of Probabilistic Inference in Random Dichotomous Hedonic Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Focusing on various classes of dichotomous hedonic games, where each agent either approves or disapproves a given coalition, we propose the random extension, where players have an independent participation probability. |
Saar Cohen; Noa Agmon; |
| 624 | Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on a combinatorial setting, where multiple projects are available for CC and agents have a limited budget. |
Sankarshan Damle; Manisha Padala; Sujit Gujar; |
| 625 | Strategyproofness and Proportionality in Party-Approval Multiwinner Elections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Strategyproofness demands that no voter can benefit by misreporting her true preferences. We show that these two axioms are incompatible for anonymous party-approval multiwinner voting rules, thus proving a far-reaching impossibility theorem. |
Théo Delemazure; Tom Demeulemeester; Manuel Eberl; Jonas Israel; Patrick Lederer; |
| 626 | Tight Inapproximability for Graphical Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide a complete characterization for the computational complexity of finding approximate equilibria in two-action graphical games. |
Argyrios Deligkas; John Fearnley; Alexandros Hollender; Themistoklis Melissourgos; |
| 627 | From Monopoly to Competition: Optimal Contests Prevail Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design and symmetric contestants. |
Xiaotie Deng; Yotam Gafni; Ron Lavi; Tao Lin; Hongyi Ling; |
| 628 | Commitment Games with Conditional Information Disclosure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a framework for commitment games with a new kind of conditional commitment device, which agents can use to conditionally disclose private information. |
Anthony DiGiovanni; Jesse Clifton; |
| 629 | Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we generalize the existing work to offer fair treatment guarantees to both sides of the market simultaneously, at a calculated worst case drop to operator profit. |
Seyed Esmaeili; Sharmila Duppala; Davidson Cheng; Vedant Nanda; Aravind Srinivasan; John P. Dickerson; |
| 630 | Participatory Budgeting Designs for The Real World Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Participatory budgeting engages the public in the process of allocating public money to different types of projects. PB designs differ in how voters are asked to express their preferences over candidate projects and how these preferences are aggregated to determine which projects to fund. This paper studies two fundamental questions in PB design |
Roy Fairstein; Gerdus Benadè; Kobi Gal; |
| 631 | PAC Learning and Stabilizing Hedonic Games: Towards A Unifying Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. |
Simone Fioravanti; Michele Flammini; Bojana Kodric; Giovanna Varricchio; |
| 632 | Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. |
Mingyu Guo; Max Ward; Aneta Neumann; Frank Neumann; Hung Nguyen; |
| 633 | Representation with Incomplete Votes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove that this complication renders non-adaptive algorithms impractical in terms of the amount of information they must gather. Therefore, we develop an adaptive algorithm that uses information more efficiently by presenting incoming participants with statements that appear promising based on votes by previous participants. |
Daniel Halpern; Gregory Kehne; Ariel D. Procaccia; Jamie Tucker-Foltz; Manuel Wüthrich; |
| 634 | Optimizing Multiple Simultaneous Objectives for Voting and Facility Location Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More specifically, we consider the l-centrum family of objectives, which includes the total distance, max distance, and many others. We present tight bounds on how well any pair of such objectives (e.g., max and sum) can be simultaneously approximated compared to their optimum outcomes. |
Yue Han; Christopher Jerrett; Elliot Anshelevich; |
| 635 | Class Fairness in Online Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For matching indivisible items, we propose an adaptive-priority-based algorithm, MATCH-AND-SHIFT, prove that it achieves (1/2)-approximation of both class envy-freeness up to one item and class maximin share fairness, and show that each guarantee is tight. |
Hadi Hosseini; Zhiyi Huang; Ayumi Igarashi; Nisarg Shah; |
| 636 | How to Cut A Discrete Cake Fairly Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we consider the problem of dividing indivisible goods fairly under the connectivity constraints of a path. |
Ayumi Igarashi; |
| 637 | Competition, Alignment, and Equilibria in Digital Marketplaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But to what extent is alignment exhibited in data-driven marketplaces? To study this question from a theoretical perspective, we introduce a duopoly market where platform actions are bandit algorithms and the two platforms compete for user participation. |
Meena Jagadeesan; Michael I. Jordan; Nika Haghtalab; |
| 638 | Voting with Preference Intensities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Occasionally, she may also wish to report the intensity of her preferences by indicating adjacent pairs of alternatives in her ranking between which her preference is acutely decisive; for instance, she may suggest that she likes alternative a more than b, but b much more than c. We design near-optimal voting rules which aggregate such preference rankings with intensities using the recently-popular distortion framework. |
Anson Kahng; Mohamad Latifian; Nisarg Shah; |
| 639 | Approximations for Indivisible Concave Allocations with Applications to Nash Welfare Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we obtain both multiplicative and additive approximations in the offline setting for indivisible items. |
Nathaniel Kell; Kevin Sun; |
| 640 | Strategic Facility Location with Clients That Minimize Total Waiting Time Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the positive side, we provide a simple and efficient algorithm to compute 3-approximate subgame perfect equilibria. |
Simon Krogmann; Pascal Lenzner; Alexander Skopalik; |
| 641 | Proportional Decisions in Perpetual Voting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we look at perpetual voting rules from an axiomatic perspective. |
Martin Lackner; Jan Maly; |
| 642 | Multiagent MST Cover: Pleasing All Optimally Via A Simple Voting Rule Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our task is to build a minimum number of roads so that every agent has a spanning tree in the built subgraph whose weight is the same as a minimum spanning tree in the original graph. |
Bo Li; Xiaowei Wu; Chenyang Xu; Ruilong Zhang; |
| 643 | When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By considering a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for atomic users, we prove that the myopic routing policy’s price of anarchy (PoA) can be arbitrarily large as the discount factor approaches 1. To remedy such huge efficiency loss, we propose a selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore the stochastic paths, while hiding such information when they want to under-explore. |
Hongbo Li; Lingjie Duan; |
| 644 | Partitioning Friends Fairly Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of partitioning n agents in an undirected social network into k almost equal in size (differing by at most one) groups, where the utility of an agent for a group is the number of her neighbors in the group. |
Lily Li; Evi Micha; Aleksandar Nikolov; Nisarg Shah; |
| 645 | Differentially Private Condorcet Voting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy of randomized voting rules based on the well-known Condorcet method, and focus on three classes of voting rules in this family: Laplacian Condorcet method (CMLAP), exponential Condorcet method (CMEXP), and randomized response Condorcet method (CMRR), where λ represents the level of noise. |
Zhechen Li; Ao Liu; Lirong Xia; Yongzhi Cao; Hanpin Wang; |
| 646 | Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose learning the Enforceable Payoff Frontier (EPF)—a generalization of the state value function for general-sum games. |
Chun Kai Ling; J. Zico Kolter; Fei Fang; |
| 647 | Optimal Pricing Schemes for Identical Items with Time-Sensitive Buyers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Time or money? That is a question! In this paper, we consider this dilemma in the pricing regime, in which we try to find the optimal pricing scheme for identical items with heterogenous time-sensitive buyers. |
Zhengyang Liu; Liang Shan; Zihe Wang; |
| 648 | Approval-Based Voting with Mixed Goods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under approval votes, we propose two variants of the extended justified representation (EJR) notion from multiwinner voting, a stronger one called EJR for mixed goods (EJR-M) and a weaker one called EJR up to 1 (EJR-1). |
Xinhang Lu; Jannik Peters; Haris Aziz; Xiaohui Bei; Warut Suksompong; |
| 649 | Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a mixed environment where utility maximizers and value maximizers coexist, the truthful ad auction design would be challenging since bidders could manipulate both their values and affiliated classes, leading to a multi-parameter mechanism design problem. In this work, we address this issue by proposing a payment rule which combines the corresponding ones in classical VCG and GSP mechanisms in a novel way. |
Hongtao Lv; Zhilin Zhang; Zhenzhe Zheng; Jinghan Liu; Chuan Yu; Lei Liu; Lizhen Cui; Fan Wu; |
| 650 | Facility Location Games with Entrance Fees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider a novel model where each facility charges an entrance fee, which is a function of the facility’s location. |
Mengfan Ma; Mingyu Xiao; Tian Bai; Bakh Khoussainov; |
| 651 | Securing Lifelines: Safe Delivery of Critical Services in Areas with Volatile Security Situation Via A Stackelberg Game Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The COVID pandemic and the need to vaccinate added even more urgency to this issue. Motivated by this problem, we propose a general framework to set-up limited temporary (vaccination) centers that balance physical security and desired (vaccine) service coverage with limited resources. |
Tien Mai; Arunesh Sinha; |
| 652 | Differentially Private Fair Division Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study privacy in the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent if they differ only on the utility of a single agent for a single item. |
Pasin Manurangsi; Warut Suksompong; |
| 653 | An Efficient Deep Reinforcement Learning Algorithm for Solving Imperfect Information Extensive-Form Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such accumulated approximation error causes poor performance. To address this accumulated approximation error, we propose a novel FTRL algorithm called FTRL-ORW, which does not utilize the agent’s past strategies to pick the next iteration strategy. |
Linjian Meng; Zhenxing Ge; Pinzhuo Tian; Bo An; Yang Gao; |
| 654 | Fast and Interpretable Dynamics for Fisher Markets Via Block-Coordinate Updates Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Highly performant deterministic full-gradient first-order methods have been developed for these programs. In this paper, we develop new block-coordinate first-order methods for computing Fisher market equilibria, and show that these methods have interpretations as tâtonnement-style or proportional response-style dynamics where either buyers or items show up one at a time. |
Tianlong Nan; Yuan Gao; Christian Kroer; |
| 655 | Ballot Length in Instant Runoff Voting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We theoretically and empirically analyze how ballot length can influence the outcome of an election, given fixed voter preferences. |
Kiran Tomlinson; Johan Ugander; Jon Kleinberg; |
| 656 | Multi-Stage Facility Location Problems with Transient Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study various models for the one-dimensional multi-stage facility location problems with transient agents, where a transient agent arrives in some stage and stays for a number of consecutive stages. |
Xuezhen Wang; Vincent Chau; Hau Chan; Ken C.K. Fong; Minming Li; |
| 657 | Bayesian Optimization-Based Combinatorial Assignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the main shortcoming of this prior work is that it does not model a mechanism’s uncertainty over values for not yet elicited bundles. In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism. |
Jakob Weissteiner; Jakob Heiss; Julien Siems; Sven Seuken; |
| 658 | Semi-random Impossibilities of Condorcet Criterion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We strengthen previous work by proving the first set of semi-random impossibilities for voting rules to satisfy CC and the more general, group versions of the four desiderata: for any sufficiently large number of voters n, any size of the group 1<= B<= \sqrt n, any voting rule r, and under a large class of semi-random models that include Impartial Culture, the likelihood for r to satisfy CC and PAR, CC and HM, CC and MM, or CC and SP is 1-\Omega(B/\sqrt n). |
Lirong Xia; |
| 659 | Tournament Fixing Parameterized By Feedback Vertex Set Number Is FPT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A sequence of papers has studied the parameterized complexity of Tournament Fixing with respect to the feedback arc set number (fas) of D Given that this parameter yielded tractability, it has been asked explicitly and repeatedly whether Tournament Fixing is FPT also with respect to the feedback vertex set number (fvs) of D. We answer this question positively. |
Meirav Zehavi; |
| 660 | Truthful Mechanisms for Steiner Tree Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Note that each edge agent may misreport her own cost for the use of the edge for her own benefit. In such a non-cooperative setting, we aim at designing an approximately truthful mechanism for establishing a Steiner tree, a minimum cost tree spanning over all the terminals. |
Jinshan Zhang; Zhengyang Liu; Xiaotie Deng; Jianwei Yin; |
| 661 | Collusion-Proof and Sybil-Proof Reward Mechanisms for Query Incentive Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, our impossibility results demonstrate that a reward mechanism cannot simultaneously achieve Sybil-proof (agents benefit from manipulating multiple fake identities), collusion-proof (multiple agents pretend as a single agent to improve the reward), and other essential properties. In order to address these issues, we propose two novel reward mechanisms. |
Youjia Zhang; Pingzhong Tang; |
| 662 | Fisher Markets with Social Influence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a generalization of Fisher markets, namely influence Fisher markets, which captures the impact of social influence on buyers’ utilities. |
Jiayi Zhao; Denizalp Goktas; Amy Greenwald; |
| 663 | Probably Approximate Shapley Fairness with Applications in Machine Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we generalise Shapley fairness to probably approximate Shapley fairness and propose fidelity score, a metric to measure the variation of SV estimates, that determines how probable the fairness guarantees hold. |
Zijian Zhou; Xinyi Xu; Rachael Hwee Ling Sim; Chuan Sheng Foo; Bryan Kian Hsiang Low; |
| 664 | The Perils of Trial-and-Error Reward Design: Misdesign Through Overfitting and Invalid Task Specifications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This process raises the question of whether the same reward function is optimal for all algorithms, i.e., whether the reward function can be overfit to a particular algorithm. In this paper, we study the consequences of this wide yet unexamined practice of trial-and-error reward design. |
Serena Booth; W. Bradley Knox; Julie Shah; Scott Niekum; Peter Stone; Alessandro Allievi; |
| 665 | The Value of AI Guidance in Human Examination of Synthetically-Generated Faces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently developed synthetic face image detectors boast “better-than-human” discriminative ability, especially those guided by human perceptual intelligence during the model’s training process. In this paper, we investigate whether these human-guided synthetic face detectors can assist non-expert human operators in the task of synthetic image detection when compared to models trained without human-guidance. |
Aidan Boyd; Patrick Tinsley; Kevin Bowyer; Adam Czajka; |
| 666 | Teaching to Learn: Sequential Teaching of Learners with Internal States Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend the machine teaching framework to learners that can improve their inductive biases, represented as latent internal states, in order to generalize to new datasets. |
Mustafa Mert Çelikok; Pierre-Alexandre Murena; Samuel Kaski; |
| 667 | Interactive Concept Bottleneck Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. |
Kushal Chauhan; Rishabh Tiwari; Jan Freyberg; Pradeep Shenoy; Krishnamurthy Dvijotham; |
| 668 | Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences Toward Allocations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we evaluate the sensitivity of moral preferences to the history of allocations and their perceived future impact on various socially salient groups. We propose a mathematical model to capture and infer such dynamic moral preferences. |
Violet (Xinying) Chen; Joshua Williams; Derek Leben; Hoda Heidari; |
| 669 | Extracting Semantic-Dynamic Features for Long-Term Stable Brain Computer Interface Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the observation from biologists that low-dimensional dynamics could describe high-dimensional neural signals, we model the underlying neural dynamics and propose a semantic-dynamic feature that represents the semantics and dynamics in a shared feature space facilitating the BCI recalibration. |
Tao Fang; Qian Zheng; Yu Qi; Gang Pan; |
| 670 | Moral Machine or Tyranny of The Majority? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the Moral Machine project, researchers crowdsourced answers to Trolley Problems concerning autonomous vehicles. Subsequently, Noothigattu et al. (2018) proposed inferring linear functions that approximate each individual’s preferences and aggregating these linear models by averaging parameters across the population. In this paper, we examine this averaging mechanism, focusing on fairness concerns and strategic effects. |
Michael Feffer; Hoda Heidari; Zachary C. Lipton; |
| 671 | The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we advocate that grounding the rationality coefficient in real data for each feedback type, rather than assuming a default value, has a significant positive effect on reward learning. |
Gaurav R. Ghosal; Matthew Zurek; Daniel S. Brown; Anca D. Dragan; |
| 672 | The Role of Heuristics and Biases During Complex Choices with An AI Teammate Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. |
Nikolos Gurney; John H. Miller; David V. Pynadath; |
| 673 | Learning to Defer with Limited Expert Predictions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a three-step approach to reduce the number of expert predictions required to train learning to defer algorithms. |
Patrick Hemmer; Lukas Thede; Michael Vössing; Johannes Jakubik; Niklas Kühl; |
| 674 | SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unsupervised domain adaptation model with sample weight learning (SWL-Adapt) for cross-user WHAR. |
Rong Hu; Ling Chen; Shenghuan Miao; Xing Tang; |
| 675 | Incentive-Boosted Federated Crowdsourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, generic crowdsourcing systems may lead to privacy-leakage through the sharing of worker data. To tackle this problem, we propose a novel approach, called iFedCrowd (incentive-boosted Federated Crowdsourcing), to manage the privacy and quality of crowdsourcing projects. |
Xiangping Kang; Guoxian Yu; Jun Wang; Wei Guo; Carlotta Domeniconi; Jinglin Zhang; |
| 676 | Towards Voice Reconstruction from EEG During Imagined Speech Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose NeuroTalk, which converts non-invasive brain signals of imagined speech into the user’s own voice. |
Young-Eun Lee; Seo-Hyun Lee; Sang-Ho Kim; Seong-Whan Lee; |
| 677 | Evaluating and Improving Interactions with Hazy Oracles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We instead introduce and formalize a general notion of deferred inference. Using this formulation, we then propose a novel evaluation centered around the Deferred Error Volume (DEV) metric, which explicitly considers the tradeoff between error reduction and the additional human effort required to achieve it. |
Stephan J. Lemmer; Jason J. Corso; |
| 678 | Human-in-the-Loop Vehicle ReID Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we solve the problem from a new perspective and present an interesting variant called human-in-the-loop vehicle ReID to leverage interactive (and possibly wrong) human feedback signal for performance enhancement. |
Zepeng Li; Dongxiang Zhang; Yanyan Shen; Gang Chen; |
| 679 | Modeling Human Trust and Reliance in AI-Assisted Decision Making: A Markovian Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a hidden Markov model to capture the affective process underlying the human-AI interaction in AI-assisted decision making, by characterizing how decision makers adjust their trust in AI over time and make reliance decisions based on their trust. |
Zhuoyan Li; Zhuoran Lu; Ming Yin; |
| 680 | Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Class Facial Expression Recognition Network (HS-OCFER) which can construct the decision boundary of a given expression class (called normal class) by training on only one-class data. |
Bingjun Luo; Junjie Zhu; Tianyu Yang; Sicheng Zhao; Chao Hu; Xibin Zhao; Yue Gao; |
| 681 | Frustratingly Easy Truth Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider an extremely simple heuristic for estimating workers’ competence using average proximity to other workers. |
Reshef Meir; Ofra Amir; Omer Ben-Porat; Tsviel Ben Shabat; Gal Cohensius; Lirong Xia; |
| 682 | Beam Search Optimized Batch Bayesian Active Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, when applied to a batch acquisition mode, like batch construction with greedy search, BALD suffers from poor performance, especially with noises of near-duplicate data. To address this shortcoming, we propose a diverse beam search optimized batch active learning method, which explores a graph for every batch construction by expanding the highest-scored samples of a predetermined number. |
Jingyu Sun; Hongjie Zhai; Osamu Saisho; Susumu Takeuchi; |
| 683 | Multi-Scale Control Signal-Aware Transformer for Motion Synthesis Without Phase Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As past poses often contain useful auxiliary hints, in this paper, we propose a task-agnostic deep learning method, namely Multi-scale Control Signal-aware Transformer (MCS-T), with an attention based encoder-decoder architecture to discover the auxiliary information implicitly for synthesizing controllable motion without explicitly requiring auxiliary information such as phase. |
Lintao Wang; Kun Hu; Lei Bai; Yu Ding; Wanli Ouyang; Zhiyong Wang; |
| 684 | SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works. |
Shizun Wang; Weihong Zeng; Xu Wang; Hao Yang; Li Chen; Chuang Zhang; Ming Wu; Yi Yuan; Yunzhao Zeng; Min Zheng; Jing Liu; |
| 685 | Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Alternatively, inspired by the diffusion process in nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic model to treat the kinematics of human joints as heated particles, which will diffuse from original states to a noise distribution. |
Dong Wei; Huaijiang Sun; Bin Li; Jianfeng Lu; Weiqing Li; Xiaoning Sun; Shengxiang Hu; |
| 686 | Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We develop a network of Bayesian agents that collectively model the mental states of teammates from the observed communication. |
Samuel Westby; Christoph Riedl; |
| 687 | Learning to Select Pivotal Samples for Meta Re-weighting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, all of them assume that a perfect meta sample set is already provided while we observe that the selections of meta sample set is performance-critical. In this paper, we study how to learn to identify such a meta sample set from a large, imperfect training set, that is subsequently cleaned and used to optimize performance in the meta re-weighting setting. |
Yinjun Wu; Adam Stein; Jacob Gardner; Mayur Naik; |
| 688 | Better Peer Grading Through Bayesian Inference Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper extends the state of the art in the latter approach in three key ways: (1) recognizing that students can behave strategically (e.g., reporting grades close to the class average without doing the work); (2) appropriately handling censored data that arises from discrete-valued grading rubrics; and (3) using mixed integer programming to improve the interpretability of the grades assigned to students. |
Hedayat Zarkoob; Greg d’Eon; Lena Podina; Kevin Leyton-Brown; |
| 689 | Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although such agents can be obtained through self-play training, they can suffer significantly from the distributional shift when paired with unencountered partners, such as humans. In this paper, we propose Maximum Entropy Population-based training (MEP) to mitigate such distributional shift. |
Rui Zhao; Jinming Song; Yufeng Yuan; Haifeng Hu; Yang Gao; Yi Wu; Zhongqian Sun; Wei Yang; |
| 690 | A Set of Control Points Conditioned Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a graph convolutional network-based trajectory prediction. |
Inhwan Bae; Hae-Gon Jeon; |
| 691 | Meta-Auxiliary Learning for Adaptive Human Pose Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More generally, once encountering out-of-distributions, the predicted poses tend to be unreliable. Motivated by this observation, we propose a novel test-time adaptation framework that leverages two self-supervised auxiliary tasks to help the primary forecasting network adapt to the test sequence. |
Qiongjie Cui; Huaijiang Sun; Jianfeng Lu; Bin Li; Weiqing Li; |
| 692 | Moving-Landmark Assisted Distributed Learning Based Decentralized Cooperative Localization (DL-DCL) with Fault Tolerance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper considers the problem of cooperative localization of multiple robots under uncertainty, communicating over a partially connected, dynamic communication network and assisted by an agile landmark. |
Shubhankar Gupta; Suresh Sundaram; |
| 693 | Periodic Multi-Agent Path Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To meet this objective, we propose a solution method that is based on constraint relaxation and optimization. |
Kazumi Kasaura; Ryo Yonetani; Mai Nishimura; |
| 694 | Improving Robotic Tactile Localization Super-resolution Via Spatiotemporal Continuity Learning and Overlapping Air Chambers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Human hand has amazing super-resolution ability in sensing the force and position of contact and this ability can be strengthened by practice. Inspired by this, we propose a method for robotic tactile super-resolution enhancement by learning spatiotemporal continuity of contact position and a tactile sensor composed of overlapping air chambers. |
Xuyang Li; Yipu Zhang; Xuemei Xie; Jiawei Li; Guangming Shi; |
| 695 | Co-imitation: Learning Design and Behaviour By Imitation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new viewpoint on the co-adaptation problem, which we call co-imitation: finding a morphology and a policy that allow an imitator to closely match the behaviour of a demonstrator. |
Chang Rajani; Karol Arndt; David Blanco-Mulero; Kevin Sebastian Luck; Ville Kyrki; |
| 696 | RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. |
Sijie Wang; Qiyu Kang; Rui She; Wee Peng Tay; Andreas Hartmannsgruber; Diego Navarro Navarro; |
| 697 | Abstract Argumentation Framework with Conditional Preferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we generalize PAF by introducing conditional preferences of the form a > b \leftarrow body that informally state that a is better than b whenever the condition expressed by body is true. |
Gianvincenzo Alfano; Sergio Greco; Francesco Parisi; Irina Trubitsyna; |
| 698 | Reactive Synthesis of Dominant Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the synthesis under environment specifications problem for LTL/LTLf which, in particular, generalizes FOND (strong) planning with these temporal goals. |
Benjamin Aminof; Giuseppe De Giacomo; Sasha Rubin; |
| 699 | Complexity of Safety and CoSafety Fragments of Linear Temporal Logic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the complexity of the problems of satisfiability, validity, and realizability over infinite and finite traces for the safety and cosafety fragments of LTL. |
Alessandro Artale; Luca Geatti; Nicola Gigante; Andrea Mazzullo; Angelo Montanari; |
| 700 | Automatically Verifying Expressive Epistemic Properties of Programs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new approach to the verification of epistemic properties of programmes. |
Francesco Belardinelli; Ioana Boureanu; Vadim Malvone; Fortunat Rajaona; |
| 701 | The Effect of Preferences in Abstract Argumentation Under A Claim-Centric View Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. |
Michael Bernreiter; Wolfgang Dvorak; Anna Rapberger; Stefan Woltran; |
| 702 | The Parameterized Complexity of Network Microaggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Microaggregation is a classical statistical disclosure control technique which requires the input data to be partitioned into clusters while adhering to specified size constraints. We provide novel exact algorithms and lower bounds for the task of microaggregating a given network while considering both unrestricted and connected clusterings, and analyze these from the perspective of the parameterized complexity paradigm. |
Václav Blažej; Robert Ganian; Dušan Knop; Jan Pokorný; Šimon Schierreich; Kirill Simonov; |
| 703 | SMT Safety Verification of Ontology-Based Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach requires a combination of model-theoretic notions and algorithmic techniques based on backward reachability. We introduce here Ontology-Based Processes, which are a variant of one of the most investigated models in this spectrum, namely simple artifact systems (SASs), where, instead of managing a database, we operate over a description logic (DL) ontology. |
Diego Calvanese; Alessandro Gianola; Andrea Mazzullo; Marco Montali; |
| 704 | Epistemic Disjunctive Datalog for Querying Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is indeed well-known that answering Datalog queries is undecidable already over lightweight knowledge bases (KBs) of the DL-Lite family. To overcome this issue, we propose a new query language based on Disjunctive Datalog rules combined with a modal epistemic operator. |
Gianluca Cima; Marco Console; Maurizio Lenzerini; Antonella Poggi; |
| 705 | Learning Logic Programs By Discovering Where Not to Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers "where not to search". |
Andrew Cropper; Céline Hocquette; |
| 706 | From Width-Based Model Checking to Width-Based Automated Theorem Proving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a general framework to convert a large class of width-based model-checking algorithms into algorithms that can be used to test the validity of graph-theoretic conjectures on classes of graphs of bounded width. |
Mateus de Oliveira Oliveira; Farhad Vadiee; |
| 707 | Model-Checking for Ability-Based Logics with Constrained Plans Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the complexity of the model-checking problem for a family of modal logics capturing the notion of “knowing how”. |
Stéphane Demri; Raul Fervari; |
| 708 | A Structural Complexity Analysis of Synchronous Dynamical Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the three most notable problems in synchronous dynamical systems: whether the system will transition to a target configuration from a starting configuration, whether the system will reach convergence from a starting configuration, and whether the system is guaranteed to converge from every possible starting configuration. |
Eduard Eiben; Robert Ganian; Thekla Hamm; Viktoriia Korchemna; |
| 709 | Evaluating Epistemic Logic Programs Via Answer Set Programming with Quantifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce a simple way to evaluate epistemic logic programs by means of answer set programming with quantifiers, a recently proposed extension of answer set programming. |
Wolfgang Faber; Michael Morak; |
| 710 | Reachability Games Modulo Theories with A Bounded Safety Player Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate that GMTs can model various relevant real-world games, and that our approach can effectively solve several problems from different domains, using Z3 as the backend CHC solver. |
Marco Faella; Gennaro Parlato; |
| 711 | Splitting Answer Set Programs with Respect to Intensionality Statements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We generalize the conditions under which this technique is applicable, by considering not only dependencies between predicates but also their arguments and context. |
Jorge Fandinno; Yuliya Lierler; |
| 712 | Monitoring Arithmetic Temporal Properties on Finite Traces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study monitoring of linear-time arithmetic properties against finite traces generated by an unknown dynamic system. |
Paolo Felli; Marco Montali; Fabio Patrizi; Sarah Winkler; |
| 713 | Untangled: A Complete Dynamic Topological Logic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider dynamic topological logic restricted to the class of scattered spaces. |
David Fernández-Duque; Yoàv Montacute; |
| 714 | Inconsistent Cores for ASP: The Perks and Perils of Non-monotonicity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study fnding minimal ICs of ASP programs and key fragments from a complexity-theoretic perspective. |
Johannes K. Fichte; Markus Hecher; Stefan Szeider; |
| 715 | General Acyclicity and Cyclicity Notions for The Disjunctive Skolem Chase Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The disjunctive skolem chase is a sound, complete, and potentially non-terminating procedure for solving boolean conjunctive query entailment over knowledge bases of disjunctive existential rules. We develop novel acyclicity and cyclicity notions for this procedure; that is, we develop sufficient conditions to determine chase termination and non-termination. |
Lukas Gerlach; David Carral; |
| 716 | GANTEE: Generative Adversarial Network for Taxonomy Enterance Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. |
Zhouhong Gu; Sihang Jiang; Jingping Liu; Yanghua Xiao; Hongwei Feng; Zhixu Li; Jiaqing Liang; Zhong Jian; |
| 717 | Finite Based Contraction and Expansion Via Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new paradigm for Belief Change in which the new information is represented as sets of models, while the agent’s body of knowledge is represented as a finite set of formulae, that is, a finite base. |
Ricardo Guimarães; Ana Ozaki; Jandson S. Ribeiro; |
| 718 | MAPS-KB: A Million-Scale Probabilistic Simile Knowledge Base Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. |
Qianyu He; Xintao Wang; Jiaqing Liang; Yanghua Xiao; |
| 719 | Characterizing Structural Hardness of Logic Programs: What Makes Cycles and Reachability Hard for Treewidth? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper deals with a novel reduction from SAT to normal ASP that goes beyond well-known encodings: We explicitly utilize the structural power of ASP, whereby we sublinearly decrease the treewidth, which probably cannot be significantly improved. |
Markus Hecher; |
| 720 | Conditional Syntax Splitting for Non-monotonic Inference Operators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce the concept of conditional syntax splitting, inspired by the notion of conditional independence as known from probability theory. |
Jesse Heyninck; Gabriele Kern-Isberner; Thomas Meyer; Jonas Philipp Haldimann; Christoph Beierle; |
| 721 | Relational Program Synthesis with Numerical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: An especially difficult problem is learning continuous values from multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reasoning. |
Céline Hocquette; Andrew Cropper; |
| 722 | Common Knowledge of Abstract Groups Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Often, however, one wishes to express knowledge of groups of agents specified by a given property, as in ‘it is common knowledge among economists’. We introduce such a logic of common knowledge, which we term abstract-group epistemic logic (AGEL). |
Merlin Humml; Lutz Schröder; |
| 723 | FASTDIAGP: An Algorithm for Parallelized Direct Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. |
Viet-Man Le; Cristian Vidal Silva; Alexander Felfernig; David Benavides; José Galindo; Thi Ngoc Trang Tran; |
| 724 | Two Views of Constrained Differential Privacy: Belief Revision and Update Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide two views of constrained differential private (DP) mechanisms. |
Likang Liu; Keke Sun; Chunlai Zhou; Yuan Feng; |
| 725 | Copyright-Certified Distillation Dataset: Distilling One Million Coins Into One Bitcoin with Your Private Key Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, copyright protection for dataset distillation has not been proposed yet, so we propose the first method to protect intellectual property by embedding watermarks in the dataset distillation process. |
Tengjun Liu; Ying Chen; Wanxuan Gu; |
| 726 | DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. |
Haoran Luo; Haihong E; Ling Tan; Gengxian Zhou; Tianyu Yao; Kaiyang Wan; |
| 727 | Automated Verification of Propositional Agent Abstraction for Classical Planning Via CTLK Model Checking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on a propositional version of agent abstraction designed for finite-state systems. |
Kailun Luo; |
| 728 | Efficient Answer Enumeration in Description Logics with Functional Roles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the enumeration of answers to ontology-mediated queries when the ontology is formulated in a description logic that supports functional roles and the query is a CQ. |
Carsten Lutz; Marcin Przybyłko; |
| 729 | Distributed Spectrum-Based Fault Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. |
Avraham Natan; Roni Stern; Meir Kalech; |
| 730 | Multi-Level Wavelet Mapping Correlation for Statistical Dependence Measurement: Methodology and Performance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new criterion for measuring dependence between two real variables, namely, Multi-level Wavelet Mapping Correlation (MWMC). |
Yixin Ren; Hao Zhang; Yewei Xia; Jihong Guan; Shuigeng Zhou; |
| 731 | Learning Interpretable Temporal Properties from Positive Examples Only Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. |
Rajarshi Roy; Jean-Raphaël Gaglione; Nasim Baharisangari; Daniel Neider; Zhe Xu; Ufuk Topcu; |
| 732 | Editing Boolean Classifiers: A Belief Change Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper is about editing Boolean classifiers, i.e., determining how a Boolean classifier should be modified when new pieces of evidence must be incorporated. Our main goal is to delineate what are the rational ways of making such edits. |
Nicolas Schwind; Katsumi Inoue; Pierre Marquis; |
| 733 | Implementing Bounded Revision Via Lexicographic Revision and C-revision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that Bounded Revision can be characterized by three simple, yet elegant postulates and corresponds to a special case of a lexicographic revision, which inherits all relevant features of BR. |
Meliha Sezgin; Gabriele Kern-Isberner; |
| 734 | Multi-Aspect Explainable Inductive Relation Prediction By Sentence Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce the concepts of relation path coverage and relation path confidence to filter out unreliable paths prior to model training to elevate the model performance. |
Zhixiang Su; Di Wang; Chunyan Miao; Lizhen Cui; |
| 735 | Learning to Break Symmetries for Efficient Optimization in Answer Set Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel approach using Inductive Logic Programming (ILP) to lift symmetry-breaking constraints for optimization problems modeled in Answer Set Programming (ASP). |
Alice Tarzariol; Martin Gebser; Konstantin Schekotihin; Mark Law; |
| 736 | On Undisputed Sets in Abstract Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the notion of an undisputed set for abstract argumentation frameworks, which is a conflict-free set of arguments, such that its reduct contains no non-empty admissible set. |
Matthias Thimm; |
| 737 | Neurosymbolic Reasoning and Learning with Restricted Boltzmann Machines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a neurosymbolic system is introduced that can represent any propositional logic formula. |
Son N. Tran; Artur d’Avila Garcez; |
| 738 | Materialisation-Based Reasoning in DatalogMTL with Bounded Intervals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate materialisation-based reasoning (a.k.a. forward chaining) in the context of DatalogMTL programs and datasets with bounded intervals, where partial representations of the canonical model are obtained through successive rounds of rule applications. |
Przemysław A. Wałęga; Michał Zawidzki; Dingmin Wang; Bernardo Cuenca Grau; |
| 739 | Efficient Extraction of EL-Ontology Deductive Modules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome computation cost and lack of quality, we propose to compute two kinds of deductive modules called pseudo-minimal modules and complete modules for EL-ontology. |
Hui Yang; Yue Ma; Nicole Bidoit; |
| 740 | Visually Grounded Commonsense Knowledge Acquisition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances. |
Yuan Yao; Tianyu Yu; Ao Zhang; Mengdi Li; Ruobing Xie; Cornelius Weber; Zhiyuan Liu; Hai-Tao Zheng; Stefan Wermter; Tat-Seng Chua; Maosong Sun; |
| 741 | DNG: Taxonomy Expansion By Exploring The Intrinsic Directed Structure on Non-gaussian Space Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches are deficient in their mining of structural information in two ways: poor modeling of the hierarchical semantics and failure to capture directionality of the is-a relation. This paper seeks to address these issues by explicitly denoting each node as the combination of inherited feature (i.e., structural part) and incremental feature (i.e., supplementary part). |
Songlin Zhai; Weiqing Wang; Yuanfang Li; Yuan Meng; |
| 742 | Quality-Aware Self-Training on Differentiable Synthesis of Rare Relational Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the latent associations between the attribute set and the class labels in a relational data cannot be easily captured by a vanilla GAN. In light of this, we introduce an end-to-end self-training scheme (namely, Quality-Aware Self-Training) for rare relational data synthesis, which generates labeled synthetic data via pseudo labeling on GAN-based synthesis. |
Chongsheng Zhang; Yaxin Hou; Ke Chen; Shuang Cao; Gaojuan Fan; Ji Liu; |
| 743 | Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, due to highly recursive functions, there is usually a non-negligible disparity between the prototype-based explanations and the original input. In this work, we propose a Self-Explaining Selective Model (SESM) that uses a linear combination of prototypical concepts to explain its own predictions. |
Yifei Zhang; Neng Gao; Cunqing Ma; |
| 744 | McOmet: Multimodal Fusion Transformer for Physical Audiovisual Commonsense Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a unified framework, named Multimodal Commonsense Transformer (MCOMET), for physical audiovisual commonsense reasoning. |
Daoming Zong; Shiliang Sun; |
| 745 | Approximating Full Conformal Prediction at Scale Via Influence Functions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we use influence functions to efficiently approximate full CP. |
Javier Abad Martinez; Umang Bhatt; Adrian Weller; Giovanni Cherubin; |
| 746 | Efficient Distributed Inference of Deep Neural Networks Via Restructuring and Pruning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the parallel implementation of an already-trained deep model on multiple processing nodes (a.k.a. workers). |
Afshin Abdi; Saeed Rashidi; Faramarz Fekri; Tushar Krishna; |
| 747 | Symbolic Metamodels for Interpreting Black-Boxes Using Primitive Functions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new method for finding interpretable metamodels. |
Mahed Abroshan; Saumitra Mishra; Mohammad Mahdi Khalili; |
| 748 | Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a general framework for reward shaping and task composition in entropy-regularized RL. |
Jacob Adamczyk; Argenis Arriojas; Stas Tiomkin; Rahul V. Kulkarni; |
| 749 | Clustering What Matters: Optimal Approximation for Clustering with Outliers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given a set X of n points and two numbers k and m, the clustering with outliers aims to exclude m points from X, and partition the remaining points into k clusters that minimizes a certain cost function. In this paper, we give a general approach for solving clustering with outliers, which results in a fixed-parameter tractable (FPT) algorithm in k and m (i.e., an algorithm with running time of the form f(k, m) * poly(n) for some function f), that almost matches the approximation ratio for its outlier-free counterpart. |
Akanksha Agrawal; Tanmay Inamdar; Saket Saurabh; Jie Xue; |
| 750 | Contrastive Classification and Representation Learning with Probabilistic Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss. |
Rahaf Aljundi; Yash Patel; Milan Sulc; Nikolay Chumerin; Daniel Olmeda Reino; |
| 751 | Simulating Network Paths with Recurrent Buffering Units Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style model called Recurrent Buffering Unit, providing the interpretability of standard network simulator tools, the power of neural models, the efficiency of SGD-based techniques for learning, and yielding promising results on synthetic and real-world network traces. |
Divyam Anshumaan; Sriram Balasubramanian; Shubham Tiwari; Nagarajan Natarajan; Sundararajan Sellamanickam; Venkat N. Padmanabhan; |
| 752 | Fully Dynamic Online Selection Through Online Contention Resolution Schemes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main contribution is providing a general method for constructing an OCRS for fully dynamic online selection problems. |
Vashist Avadhanula; Andrea Celli; Riccardo Colini-Baldeschi; Stefano Leonardi; Matteo Russo; |
| 753 | Tree Learning: Optimal Sample Complexity and Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The smallest number of such tuples required in order to be able to accurately label subsequent tuples is of interest for data collection in machine learning. We present optimal sample complexity bounds for this problem in several learning settings, including (agnostic) PAC learning and online learning. |
Dmitrii Avdiukhin; Grigory Yaroslavtsev; Danny Vainstein; Orr Fischer; Sauman Das; Faraz Mirza; |
| 754 | Meta-Learning for Simple Regret Minimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this framework, a learning agent interacts with a sequence of bandit tasks, which are sampled i.i.d. from an unknown prior distribution, and learns its meta-parameters to perform better on future tasks. We propose the first Bayesian and frequentist meta-learning algorithms for this setting. |
Javad Azizi; Branislav Kveton; Mohammad Ghavamzadeh; Sumeet Katariya; |
| 755 | Generalizing Downsampling from Regular Data to Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Indeed reductions in graph data are still needed for the goals described above, but reduction mechanisms do not have the same focus on preserving topological structures and properties, while allowing for resolution-tuning, as is the case in regular data downsampling. In this paper, we take a step in this direction, introducing a unifying interpretation of downsampling in regular and graph data. |
Davide Bacciu; Alessio Conte; Francesco Landolfi; |
| 756 | PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose PiCor, an efficient multi-task DRL framework that splits learning into policy optimization and policy correction phases. |
Fengshuo Bai; Hongming Zhang; Tianyang Tao; Zhiheng Wu; Yanna Wang; Bo Xu; |
| 757 | Achieving Zero Constraint Violation for Constrained Reinforcement Learning Via Conservative Natural Policy Gradient Primal-Dual Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel Conservative Natural Policy Gradient Primal Dual Algorithm (CNPGPD) to achieve zero constraint violation while achieving state of the art convergence results for the objective value function. |
Qinbo Bai; Amrit Singh Bedi; Vaneet Aggarwal; |
| 758 | Optimal Sparse Recovery with Decision Stumps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We here obtain a tight finite sample bound for the feature selection problem in linear regression using single-depth decision trees. |
Kiarash Banihashem; Mohammad Hajiaghayi; Max Springer; |
| 759 | Towards Efficient and Domain-Agnostic Evasion Attack with High-Dimensional Categorical Inputs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our objective is to achieve highly efficient and effective attack using an Orthogonal Matching Pursuit (OMP)-enhanced Upper Confidence Bound (UCB) exploration strategy. |
Hongyan Bao; Yufei Han; Yujun Zhou; Xin Gao; Xiangliang Zhang; |
| 760 | Fairness and Welfare Quantification for Regret in Multi-Armed Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work develops an algorithm that, given the horizon of play T, achieves a Nash regret of O ( sqrt{(k log T)/T} ), here k denotes the number of arms in the MAB instance. |
Siddharth Barman; Arindam Khan; Arnab Maiti; Ayush Sawarni; |
| 761 | Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Indeed, shallow alternating layered VQAs are easy to implement and have been shown to be both trainable and expressive. In this work, we introduce a training algorithm with an exponential reduction in training cost of such VQAs. |
Afrad Basheer; Yuan Feng; Christopher Ferrie; Sanjiang Li; |
| 762 | Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we go beyond current approaches to incorporate global features for effectively learning representations of a dynamically evolving graph. |
Anson Bastos; Abhishek Nadgeri; Kuldeep Singh; Toyotaro Suzumura; Manish Singh; |
| 763 | Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models while incurring minimum L_2 loss between the feature representations of the pretrained and the equivariant models. |
Sourya Basu; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Vijil Chenthamarakshan; Kush R. Varshney; Lav R. Varshney; Payel Das; |
| 764 | Sustaining Fairness Via Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To ensure fairness in the wild, it is important for a system to adapt to such changes as it accesses new data in an incremental fashion. In this work, we propose to address this issue by introducing the problem of learning fair representations in an incremental learning setting. |
Somnath Basu Roy Chowdhury; Snigdha Chaturvedi; |
| 765 | Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. |
Lucas Berry; David Meger; |
| 766 | An Improved Algorithm for Online Min-Sum Set Cover Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike previous work that mostly studied the performance of an online algorithm ALG in comparison to the static optimal solution (a single optimal list ordering), in this paper, we study an arguably harder variant where the benchmark is the provably stronger optimal dynamic solution OPT (that may also modify the list ordering). |
Marcin Bienkowski; Marcin Mucha; |
| 767 | AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces AutoInit, a weight initialization algorithm that automatically adapts to different neural network architectures. |
Garrett Bingham; Risto Miikkulainen; |
| 768 | A Parameterized Theory of PAC Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But while the nascent theory of parameterized complexity has allowed us to push beyond the P-NP "dichotomy" in classical computational complexity and identify the exact boundaries of tractability for numerous problems, there is no analogue in the domain of sample complexity that could push beyond efficient PAC learnability. As our core contribution, we fill this gap by developing a theory of parameterized PAC learning which allows us to shed new light on several recent PAC learning results that incorporated elements of parameterized complexity. |
Cornelius Brand; Robert Ganian; Kirill Simonov; |
| 769 | Fully-Dynamic Decision Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop the first fully dynamic algorithm that maintains a decision tree over an arbitrary sequence of insertions and deletions of labeled examples. |
Marco Bressan; Gabriel Damay; Mauro Sozio; |
| 770 | Scalable Theory-Driven Regularization of Scene Graph Generation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art. |
Davide Buffelli; Efthymia Tsamoura; |
| 771 | Toward A Perspectivist Turn in Ground Truthing for Predictive Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The annotation process is often performed in terms of a majority vote, however this has been proved to be often problematic by recent evaluation studies. In this article, we describe and advocate for a different paradigm, which we call perspectivism: this counters the removal of disagreement and, consequently, the assumption of correctness of traditionally aggregated gold-standard datasets, and proposes the adoption of methods that preserve divergence of opinions and integrate multiple perspectives in the ground truthing process of ML development. |
Federico Cabitza; Andrea Campagner; Valerio Basile; |
| 772 | Semantic-Enhanced Image Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to investigate the task of image clustering with the help of visual-language pre-training model. |
Shaotian Cai; Liping Qiu; Xiaojun Chen; Qin Zhang; Longteng Chen; |
| 773 | RePreM: Representation Pre-training with Masked Model for Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Representation Pre-training with Masked Model), which trains the encoder combined with transformer blocks to predict the masked states or actions in a trajectory. |
Yuanying Cai; Chuheng Zhang; Wei Shen; Xuyun Zhang; Wenjie Ruan; Longbo Huang; |
| 774 | FTM: A Frame-Level Timeline Modeling Method for Temporal Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing neighborhood aggregation strategies fail to capture either the short-term features or the long-term features of temporal graph attributes, leading to unsatisfactory model performance and even poor robustness and domain generality of the representation learning method. To address this problem, we propose a Frame-level Timeline Modeling (FTM) method that helps to capture both short-term and long-term features and thus learns more informative representations on temporal graphs. |
Bowen Cao; Qichen Ye; Weiyuan Xu; Yuexian Zou; |
| 775 | Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, in continuous time settings with irregular samples, it is challenging to directly handle the dynamics of causality. In this paper, we leverage recent advances in Lipschitz regularization and neural controlled differential equations (CDE) to develop an effective and scalable solution, namely LipCDE, to address the above challenges. |
Defu Cao; James Enouen; Yujing Wang; Xiangchen Song; Chuizheng Meng; Hao Niu; Yan Liu; |
| 776 | InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Time steps with temporal redundancy are weakly semantic, and only leveraging time-domain tokens is hard to depict the overall properties of time series (e.g., the overall trend and periodic variations). To address these problems, we propose a novel Transformer-based forecasting model named InParformer with an Interactive Parallel Attention (InPar Attention) mechanism. |
Haizhou Cao; Zhenhao Huang; Tiechui Yao; Jue Wang; Hui He; Yangang Wang; |
| 777 | Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Starting from the premise, we envision a neural data structure, which we term the meta-sketch, to go beyond the basic structure of conventional sketches. |
Yukun Cao; Yuan Feng; Xike Xie; |
| 778 | Unfooling Perturbation-Based Post Hoc Explainers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: And how can we ascertain that the auditee is complying with the audit in good faith? In this work, we rigorously formalize this problem and devise a defense against adversarial attacks on perturbation-based explainers. |
Zachariah Carmichael; Walter J. Scheirer; |
| 779 | Very Fast, Approximate Counterfactual Explanations for Decision Forests Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a simple but very effective approach: we constrain the optimization to input space regions populated by actual data points. |
Miguel Á. Carreira-Perpinan; Suryabhan Singh Hada; |
| 780 | An Equivalence Analysis of Binary Quantification Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by this study, we propose a new method that extends one of the approaches analyzed. |
Alberto Castaño; Jaime Alonso; Pablo González; Juan José del Coz; |
| 781 | Soft Action Priors: Towards Robust Policy Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we use the action prior from the Reinforcement Learning as Inference framework – that is, a distribution over actions at each state which resembles a teacher policy, rather than a Bayesian prior – to recover state-of-the-art policy distillation techniques. |
Matheus Centa; Philippe Preux; |
| 782 | Invariant Representations with Stochastically Quantized Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a methodology for direct computation of the mutual information between neurons in a layer and a sensitive attribute. |
Mattia Cerrato; Marius Köppel; Roberto Esposito; Stefan Kramer; |
| 783 | Learning Pessimism for Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected target returns. In this work, we propose Generalized Pessimism Learning (GPL), a strategy employing a novel learnable penalty to enact such pessimism. |
Edoardo Cetin; Oya Celiktutan; |
| 784 | Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time. In this work, we develop a novel MBRL method (i) which relaxes the assumptions on the target transition model to belong to a generic family of mixture models; (ii) is applicable to large-scale training by incorporating a compression step such that the posterior estimate consists of a Bayesian coreset of only statistically significant past state-action pairs; and (iii) exhibits a sublinear Bayesian regret. |
Souradip Chakraborty; Amrit Singh Bedi; Pratap Tokekar; Alec Koppel; Brian Sadler; Furong Huang; Dinesh Manocha; |
| 785 | NHITS: Neural Hierarchical Interpolation for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce NHITS, a model which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. |
Cristian Challu; Kin G. Olivares; Boris N. Oreshkin; Federico Garza Ramirez; Max Mergenthaler Canseco; Artur Dubrawski; |
| 786 | Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the PM graph, we propose a novel Dynamic Graphs and Hierarchical Fusion for Movement Forecasting model (DyMF) with interaction style extractors to capture the mutual interactions of players themselves and between both players within a rally, and dynamic players’ tactics across time. |
Kai-Shiang Chang; Wei-Yao Wang; Wen-Chih Peng; |
| 787 | Graph Ordering Attention Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Standard well-examined choices such as the mean or sum aggregation functions have limited capabilities, as they are not able to capture interactions among neighbors. In this work, we formalize these interactions using an information-theoretic framework that notably includes synergistic information. |
Michail Chatzianastasis; Johannes Lutzeyer; George Dasoulas; Michalis Vazirgiannis; |
| 788 | Scalable and Globally Optimal Generalized L₁ K-center Clustering Via Constraint Generation in Mixed Integer Linear Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L_1 k-center clustering that uses L_1 distance and allows the selection of arbitrary vectors as cluster centers. |
Aravinth Chembu; Scott Sanner; Hassan Khurram; Akshat Kumar; |
| 789 | Attribute and Structure Preserving Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to design a more robust mechanism, we develop a novel attribute and structure preserving graph contrastive learning framework, named ASP, which comprehensively and efficiently preserves node attributes while exploiting graph structure. |
Jialu Chen; Gang Kou; |
| 790 | On The Stability and Generalization of Triplet Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. |
Jun Chen; Hong Chen; Xue Jiang; Bin Gu; Weifu Li; Tieliang Gong; Feng Zheng; |
| 791 | CF-ViT: A General Coarse-to-Fine Method for Vision Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, considerable redundancy arises in the spatial dimension of an input image, leading to massive computational costs. Therefore, We propose a coarse-to-fine vision transformer (CF-ViT) to relieve computational burden while retaining performance in this paper. |
Mengzhao Chen; Mingbao Lin; Ke Li; Yunhang Shen; Yongjian Wu; Fei Chao; Rongrong Ji; |
| 792 | Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though promising performance has been achieved by leveraging Graph Neural Network (GNN) models to encode the molecular structures of medications or/and DDI, we observe that existing models are still defective: 1) to differentiate medications with similar molecules but different functionality; or/and 2) to properly capture the unintended reactions between drugs in the embedding space. To alleviate this limitation, we propose Carmen, a cautiously designed graph embedding-based MR framework. |
Qianyu Chen; Xin Li; Kunnan Geng; Mingzhong Wang; |
| 793 | Min-Max Submodular Ranking for Multiple Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents—thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents. |
Qingyun Chen; Sungjin Im; Benjamin Moseley; Chenyang Xu; Ruilong Zhang; |
| 794 | Supervised Contrastive Few-Shot Learning for High-Frequency Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we consider a realistic scenario in industry with limited annotation information available. |
Xi Chen; Cheng Ge; Ming Wang; Jin Wang; |
| 795 | The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient According to An Off-Policy Measure Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is YES, and the better policies are in fact located very far from the clipped space. We show that PPO is insufficient in off-policyness, according to an off-policy metric called DEON. |
Xing Chen; Dongcui Diao; Hechang Chen; Hengshuai Yao; Haiyin Piao; Zhixiao Sun; Zhiwei Yang; Randy Goebel; Bei Jiang; Yi Chang; |
| 796 | Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a new analysis framework that allows establishing the global convergence to an epsilon-optimal solution with at most an order of epsilon to -2.5 sample complexity. |
Xuyang Chen; Jingliang Duan; Yingbin Liang; Lin Zhao; |
| 797 | Topological Pooling on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By invoking the machinery of persistent homology and the concept of landmarks, we propose a novel topological pooling layer and witness complex-based topological embedding mechanism that allow us to systematically integrate hidden topological information at both local and global levels. |
Yuzhou Chen; Yulia R. Gel; |
| 798 | Riemannian Local Mechanism for SPD Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on this analysis, we define the local information in the SPD manifold and design a multi-scale submanifold block for mining local geometry. |
Ziheng Chen; Tianyang Xu; Xiao-Jun Wu; Rui Wang; Zhiwu Huang; Josef Kittler; |
| 799 | TC-DWA:Text Clustering with Dual Word-Level Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we propose a novel BERT-based method, namely Text Clustering with Dual Word-level Augmentation (TCDWA). |
Bo Cheng; Ximing Li; Yi Chang; |
| 800 | Causal Inference with Conditional Instruments Using Deep Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. |
Debo Cheng; Ziqi Xu; Jiuyong Li; Lin Liu; Jixue Liu; Thuc Duy Le; |
| 801 | Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Wiener Graph Deconvolutional Network (WGDN), an augmentation-adaptive decoder empowered by graph wiener filter to perform information reconstruction. |
Jiashun Cheng; Man Li; Jia Li; Fugee Tsung; |
| 802 | Partial-Label Regression Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The drawback of this method lies in that the loss incurred by the true label may be overwhelmed by other false labels. To overcome this drawback, we propose an identification method that takes the least loss incurred by candidate labels as the predictive loss. |
Xin Cheng; Deng-Bao Wang; Lei Feng; Min-Ling Zhang; Bo An; |
| 803 | Offline Quantum Reinforcement Learning in A Conservative Manner Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current QRL algorithms employ an online learning scheme, i.e., the policy that is run on a quantum computer needs to interact with the environment to collect experiences, which could be expensive and dangerous for practical applications. In this paper, we aim to solve this problem in an offline learning manner. |
Zhihao Cheng; Kaining Zhang; Li Shen; Dacheng Tao; |
| 804 | Variational Wasserstein Barycenters with C-cyclical Monotonicity Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Upon those ideas, we propose a novel end-to-end continuous approximation method, namely Variational Wasserstein Barycenters with c-Cyclical Monotonicity Regularization (VWB-CMR), given sample access to the input distributions. |
Jinjin Chi; Zhiyao Yang; Ximing Li; Jihong Ouyang; Renchu Guan; |
| 805 | MobileTL: On-Device Transfer Learning with Inverted Residual Blocks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, their high memory cost prohibits training IRBs on resource-limited edge devices, and making them unsuitable in the context of transfer learning. To address this issue, we present MobileTL, a memory and computationally efficient on-device transfer learning method for models built with IRBs. |
Hung-Yueh Chiang; Natalia Frumkin; Feng Liang; Diana Marculescu; |
| 806 | Learning Optimal Features Via Partial Invariance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The success of IRM requires an important assumption: the underlying causal mechanisms/features remain invariant across environments. When not satisfied, we show that IRM can over-constrain the predictor and to remedy this, we propose a relaxation via partial invariance. |
Moulik Choraria; Ibtihal Ferwana; Ankur Mani; Lav R. Varshney; |
| 807 | PrimeNet: Pre-training for Irregular Multivariate Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time-series. |
Ranak Roy Chowdhury; Jiacheng Li; Xiyuan Zhang; Dezhi Hong; Rajesh K. Gupta; Jingbo Shang; |
| 808 | Structured BFGS Method for Optimal Doubly Stochastic Matrix Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we consider the optimal approximation of a square matrix in the set of doubly stochastic matrices. |
Dejun Chu; Changshui Zhang; Shiliang Sun; Qing Tao; |
| 809 | On The Complexity of PAC Learning in Hilbert Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The problem of learning convex polyhedra in finite-dimensional spaces is sufficiently well studied in the literature. We generalize this problem to that in a Hilbert space and propose an algorithm for learning a polyhedron which correctly classifies at least 1 − ε of the distribution, with a probability of at least 1 − δ, where ε and δ are given parameters. |
Sergei Chubanov; |
| 810 | Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks Using An Incompetent Teacher Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel machine unlearning method by exploring the utility of competent and incompetent teachers in a student-teacher framework to induce forgetfulness. |
Vikram S Chundawat; Ayush K Tarun; Murari Mandal; Mohan Kankanhalli; |
| 811 | Scalable Spatiotemporal Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While methods to improve scalability have been proposed in the context of static graphs, few research efforts have been devoted to the spatiotemporal case. To fill this gap, we propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics. |
Andrea Cini; Ivan Marisca; Filippo Maria Bianchi; Cesare Alippi; |
| 812 | Exploiting Multiple Abstractions in Episodic RL Via Reward Shaping Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Each layer is an MDP representing a coarser model of the one immediately below in the hierarchy. In this work, we propose a novel form of Reward Shaping where the solution obtained at the abstract level is used to offer rewards to the more concrete MDP, in such a way that the abstract solution guides the learning in the more complex domain. |
Roberto Cipollone; Giuseppe De Giacomo; Marco Favorito; Luca Iocchi; Fabio Patrizi; |
| 813 | Tricking The Hashing Trick: A Tight Lower Bound on The Robustness of CountSketch to Adaptive Inputs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When inputs are adaptive, however, an adversarial input can be constructed after O(l) queries with the classic estimator and the best known robust estimator only supports ~O(l^2) queries. In this work we show that this quadratic dependence is in a sense inherent: We design an attack that after O(l^2) queries produces an adversarial input vector whose sketch is highly biased. |
Edith Cohen; Jelani Nelson; Tamas Sarlos; Uri Stemmer; |
| 814 | Continuous Mixtures of Tractable Probabilistic Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate a hybrid approach, namely continuous mixtures of tractable models with a small latent dimension. |
Alvaro H.C. Correia; Gennaro Gala; Erik Quaeghebeur; Cassio de Campos; Robert Peharz; |
| 815 | End-to-End Learning for Optimization Via Constraint-Enforcing Approximators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The difficulty in end-to-end learning lies in differentiating through the optimization problem. Therefore, we propose a neural network architecture that can learn to approximately solve these optimization problems, particularly ensuring its output satisfies the feasibility constraints via alternate projections. |
Rares Cristian; Pavithra Harsha; Georgia Perakis; Brian L Quanz; Ioannis Spantidakis; |
| 816 | Practical Parallel Algorithms for Submodular Maximization Subject to A Knapsack Constraint with Nearly Optimal Adaptivity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose the first combinatorial algorithm achieving an (8+epsilon)-approximation under O(log n) adaptive complexity, which is optimal up to a factor of O(loglog n). |
Shuang Cui; Kai Han; Jing Tang; He Huang; Xueying Li; Aakas Zhiyuli; |
| 817 | Opposite Online Learning Via Sequentially Integrated Stochastic Gradient Descent Estimators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article proposes a novel and general framework of one-sided testing for streaming data based on SGD, which determines whether the unknown parameter is greater than a certain positive constant. |
Wenhai Cui; Xiaoting Ji; Linglong Kong; Xiaodong Yan; |
| 818 | Contrastive Learning with The Feature Reconstruction Amplifier Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, when low-dimensional features cannot provide sufficient discriminative information to the model (e.g., the samples are very similar to each other), the existing contrastive learning method will be limited to a great extent. Therefore, in this paper, we propose a general module called the Feature Reconstruction Amplifier (FRA) for adding additional high-dimensional feature information to the model. |
Wentao Cui; Liang Bai; |
| 819 | Augmented Proximal Policy Optimization for Safe Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we propose Augmented Proximal Policy Optimization (APPO), which augments the Lagrangian function of the primal constrained problem via attaching a quadratic deviation term. |
Juntao Dai; Jiaming Ji; Long Yang; Qian Zheng; Gang Pan; |
| 820 | GradPU: Positive-Unlabeled Learning Via Gradient Penalty and Positive Upweighting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on that, we develop a simple yet effective positive-unlabeled learning method, GradPU, which consists of two key ingredients: A gradient-based regularizer that penalizes the gradient norms in the interpolated data region, which improves the generalization of positive class; An unnormalized upweighting mechanism that assigns larger weights to those positive samples that are hard, not-well-fitted and less frequently labeled. |
Songmin Dai; Xiaoqiang Li; Yue Zhou; Xichen Ye; Tong Liu; |
| 821 | Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling Via Bayesian Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression. |
Weihang Dai; Xiaomeng Li; Kwang-Ting Cheng; |
| 822 | Tackling Data Heterogeneity in Federated Learning with Class Prototypes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we consider class imbalance, an overlooked type of data heterogeneity, in the classification setting. |
Yutong Dai; Zeyuan Chen; Junnan Li; Shelby Heinecke; Lichao Sun; Ran Xu; |
| 823 | CrysGNN: Distilling Pre-trained Knowledge to Enhance Property Prediction for Crystalline Materials Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is an availability of a huge amount of crystal data with its chemical composition and structural bonds. To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unla- belled material data. |
Kishalay Das; Bidisha Samanta; Pawan Goyal; Seung-Cheol Lee; Satadeep Bhattacharjee; Niloy Ganguly; |
| 824 | Non-reversible Parallel Tempering for Deep Posterior Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such an innovation largely disappears in big data due to the limited chains and few bias-corrected swaps. To handle this issue, we generalize the DEO scheme to promote non-reversibility and propose a few solutions to tackle the underlying bias caused by the geometric stopping time. |
Wei Deng; Qian Zhang; Qi Feng; Faming Liang; Guang Lin; |
| 825 | Stability-Based Generalization Analysis of The Asynchronous Decentralized SGD Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide the first generalization results of the popular stochastic gradient descent (SGD) algorithm in the distributed asynchronous decentralized setting. |
Xiaoge Deng; Tao Sun; Shengwei Li; Dongsheng Li; |
| 826 | Integer Subspace Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose new differential privacy solutions for when external invariants and integer constraints are simultaneously enforced on the data product. |
Prathamesh Dharangutte; Jie Gao; Ruobin Gong; Fang-Yi Yu; |
| 827 | Black-Box Adversarial Attack on Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Theoretical analyses reveal that the key lies in: estimating black-box gradient with diversity and non-convexity of TSC models resolved, and restricting the l0 norm of the perturbation to construct adversarial samples. Towards this end, we propose a new framework named BlackTreeS, which solves the hard optimization issue for adversarial example construction with two simple yet effective modules. |
Daizong Ding; Mi Zhang; Fuli Feng; Yuanmin Huang; Erling Jiang; Min Yang; |
| 828 | C-NTPP: Learning Cluster-Aware Neural Temporal Point Process Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a c-NTPP (Cluster-Aware Neural Temporal Point Process) model, which leverages a sequential variational autoencoder framework to infer the latent cluster each event belongs to in the sequence. |
Fangyu Ding; Junchi Yan; Haiyang Wang; |
| 829 | Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we address their inherent limitations by proposing a simple yet effective framework — Simple Neural Networks with Structural and Semantic Contrastive Learning} (S^3-CL). |
Kaize Ding; Yancheng Wang; Yingzhen Yang; Huan Liu; |
| 830 | Incremental Reinforcement Learning with Dual-Adaptive Ε-Greedy Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address a new challenge with a more realistic setting, Incremental Reinforcement Learning, where the search space of the Markov Decision Process continually expands. |
Wei Ding; Siyang Jiang; Hsi-Wen Chen; Ming-Syan Chen; |
| 831 | Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper provides the first provably efficient algorithm for non-stationary CMDPs with safe exploration. |
Yuhao Ding; Javad Lavaei; |
| 832 | Non-stationary Risk-Sensitive Reinforcement Learning: Near-Optimal Dynamic Regret, Adaptive Detection, and Separation Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Both the reward functions and the state transition kernels are unknown and allowed to vary arbitrarily over time with a budget on their cumulative variations. When this variation budget is known a prior, we propose two restart-based algorithms, namely Restart-RSMB and Restart-RSQ, and establish their dynamic regrets. |
Yuhao Ding; Ming Jin; Javad Lavaei; |
| 833 | SKDBERT: Compressing BERT Via Stochastic Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Stochastic Knowledge Distillation (SKD) to obtain compact BERT-style language model dubbed SKDBERT. |
Zixiang Ding; Guoqing Jiang; Shuai Zhang; Lin Guo; Wei Lin; |
| 834 | Model-Based Offline Reinforcement Learning with Local Misspecification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we propose an empirical algorithm for optimal offline policy selection. |
Kefan Dong; Yannis Flet-Berliac; Allen Nie; Emma Brunskill; |
| 835 | Can Label-Specific Features Help Partial-Label Learning? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From a different perspective, we propose to enrich the feature space and raise the question “Can label-specific features help PLL?” |
Ruo-Jing Dong; Jun-Yi Hang; Tong Wei; Min-Ling Zhang; |
| 836 | Interpreting Unfairness in Graph Neural Networks Via Training Node Attribution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes. |
Yushun Dong; Song Wang; Jing Ma; Ninghao Liu; Jundong Li; |
| 837 | Robust and Fast Measure of Information Via Low-Rank Representation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. |
Yuxin Dong; Tieliang Gong; Shujian Yu; Hong Chen; Chen Li; |
| 838 | Graph Anomaly Detection Via Multi-Scale Contrastive Learning Networks with Augmented View Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. |
Jingcan Duan; Siwei Wang; Pei Zhang; En Zhu; Jingtao Hu; Hu Jin; Yue Liu; Zhibin Dong; |
| 839 | Diffeomorphic Information Neural Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we introduce DINE (Diffeomorphic Information Neural Estimator)–a novel approach for estimating CMI of continuous random variables, inspired by the invariance of CMI over diffeomorphic maps. |
Bao Duong; Thin Nguyen; |
| 840 | Combining Slow and Fast: Complementary Filtering for Dynamics Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose two methods, one being purely learning-based and the other one being a hybrid model that requires an additional physics-based simulator. |
Katharina Ensinger; Sebastian Ziesche; Barbara Rakitsch; Michael Tiemann; Sebastian Trimpe; |
| 841 | Popularizing Fairness: Group Fairness and Individual Welfare Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose that a potential mitigation to this concern is to ensure that a group-fair model is also popular, in the sense that, for a majority of the target population, it yields a preferred distribution over outcomes compared with the conventional model. In this paper, we show that state of the art fair learning approaches are often unpopular in this sense. |
Andrew Estornell; Sanmay Das; Brendan Juba; Yevgeniy Vorobeychik; |
| 842 | FairFed: Enabling Group Fairness in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the importance and challenges of group fairness in federated learning, in this work, we propose FairFed, a novel algorithm for fairness-aware aggregation to enhance group fairness in federated learning. |
Yahya H. Ezzeldin; Shen Yan; Chaoyang He; Emilio Ferrara; A. Salman Avestimehr; |
| 843 | Goal-Conditioned Generators of Deep Policies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we study goal-conditioned neural nets (NNs) that learn to generate deep NN policies in form of context-specific weight matrices, similar to Fast Weight Programmers and other methods from the 1990s. |
Francesco Faccio; Vincent Herrmann; Aditya Ramesh; Louis Kirsch; Jürgen Schmidhuber; |
| 844 | Directed Acyclic Graph Structure Learning from Dynamic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. |
Shaohua Fan; Shuyang Zhang; Xiao Wang; Chuan Shi; |
| 845 | Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, for better distribution estimation, we propose the coefficient net (Conet), which can be any neural architectures, to map input sequences into learnable distribution coefficients. |
Wei Fan; Pengyang Wang; Dongkun Wang; Dongjie Wang; Yuanchun Zhou; Yanjie Fu; |
| 846 | Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, as spatial relations may differ substantially across samples, building one static graph for all the samples inherently limits flexibility and severely degrades the performance in practice. To address this issue, we propose a framework for fine-grained modeling and utilization of spatial correlation between variables. |
Yuchen Fang; Kan Ren; Caihua Shan; Yifei Shen; You Li; Weinan Zhang; Yong Yu; Dongsheng Li; |
| 847 | Wasserstein Graph Distance Based on L1–Approximated Tree Edit Distance Between Weisfeiler–Lehman Subtrees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limitation is particularly severe for traditional metrics defined by the WL test, which cannot precisely capture slight structural differences. In this paper, we propose a novel graph metric called the Wasserstein WL Subtree (WWLS) distance to address this problem. |
Zhongxi Fang; Jianming Huang; Xun Su; Hiroyuki Kasai; |
| 848 | Combinatorial Causal Bandits Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present the algorithm BGLM-OFU for Markovian BGLMs (i.e., no hidden variables) based on the maximum likelihood estimation method and give regret analysis for it. |
Shi Feng; Wei Chen; |
| 849 | Scalable Attributed-Graph Subspace Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Over recent years, graph convolutional networks emerged as powerful node clustering methods and have set state of the art results for this task. In this paper, we argue that some of these methods are unnecessarily complex and propose a node clustering model that is more scalable while being more effective. |
Chakib Fettal; Lazhar Labiod; Mohamed Nadif; |
| 850 | SigMaNet: One Laplacian to Rule Them All Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The cornerstone of SigMaNet is the Sign-Magnetic Laplacian (LSM), a new Laplacian matrix that we introduce ex novo in this work. |
Stefano Fiorini; Stefano Coniglio; Michele Ciavotta; Enza Messina; |
| 851 | Optimal Decision Diagrams for Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this context, we study the training of optimal decision diagrams (ODDs) from a mathematical programming perspective. We introduce a novel mixed-integer linear programming model for training and demonstrate its applicability for many datasets of practical importance. |
Alexandre M. Florio; Pedro Martins; Maximilian Schiffer; Thiago Serra; Thibaut Vidal; |
| 852 | Estimating Average Causal Effects from Patient Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim at estimating the average causal effect (ACE) from observational data (patient trajectories) that are collected over time. |
Dennis Frauen; Tobias Hatt; Valentyn Melnychuk; Stefan Feuerriegel; |
| 853 | Decorate The Newcomers: Visual Domain Prompt for Continual Test Time Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-layer visual domain prompt for target domains while having the source model parameters frozen. |
Yulu Gan; Yan Bai; Yihang Lou; Xianzheng Ma; Renrui Zhang; Nian Shi; Lin Luo; |
| 854 | EffConv: Efficient Learning of Kernel Sizes for Convolution Layers of CNNs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous methods cannot achieve satisfactory results or are inefficient for large-scale datasets. To fill this gap, we design a novel efficient kernel size learning method in which a size predictor model learns to predict optimal kernel sizes for a classifier given a desired number of parameters. |
Alireza Ganjdanesh; Shangqian Gao; Heng Huang; |
| 855 | Fast Counterfactual Inference for History-Based Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes to perform CI on observation sub-spaces instead of single observations and develop a coarse-to-fine CI algorithm, called Tree-based History Counterfactual Inference (T-HCI), to reduce the number of interventions exponentially. |
Haichuan Gao; Tianren Zhang; Zhile Yang; Yuqing Guo; Jinsheng Ren; Shangqi Guo; Feng Chen; |
| 856 | Robust Causal Graph Representation Learning Against Confounding Effects Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. |
Hang Gao; Jiangmeng Li; Wenwen Qiang; Lingyu Si; Bing Xu; Changwen Zheng; Fuchun Sun; |
| 857 | Towards Decision-Friendly AUC: Learning Multi-Classifier with AUCµ Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that simply optimizing MAUC is far from enough for imbalanced multi-classification. |
Peifeng Gao; Qianqian Xu; Peisong Wen; Huiyang Shao; Yuan He; Qingming Huang; |
| 858 | Long-Tail Cross Modal Hashing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose LtCMH (Long-tail CMH) to handle imbalanced multi-modal data. |
Zijun Gao; Jun Wang; Guoxian Yu; Zhongmin Yan; Carlotta Domeniconi; Jinglin Zhang; |
| 859 | Handling Missing Data Via Max-Entropy Regularized Graph Autoencoder Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy. |
Ziqi Gao; Yifan Niu; Jiashun Cheng; Jianheng Tang; Lanqing Li; Tingyang Xu; Peilin Zhao; Fugee Tsung; Jia Li; |
| 860 | Reinforced Approximate Exploratory Data Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Deep Reinforcement Learning (DRL) based framework which can optimize the sample selection in order to keep the analysis and insight generation flow intact. |
Shaddy Garg; Subrata Mitra; Tong Yu; Yash Gadhia; Arjun Kashettiwar; |
| 861 | Learning Program Synthesis for Integer Sequences from Scratch Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a self-learning approach for synthesizing programs from integer sequences. |
Thibault Gauthier; Josef Urban; |
| 862 | Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study generalized ZS-SBIR (GZS-SBIR) and propose a novel semi-transductive learning paradigm. |
Ce Ge; Jingyu Wang; Qi Qi; Haifeng Sun; Tong Xu; Jianxin Liao; |
| 863 | Multi-Classifier Adversarial Optimization for Active Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a novel adversarial AL method, namely multi-classifier adversarial optimization for active learning (MAOAL). |
Lin Geng; Ningzhong Liu; Jie Qin; |
| 864 | Differentially Private Heatmaps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the task of producing heatmaps from users’ aggregated data while protecting their privacy. We give a differentially private (DP) algorithm for this task and demonstrate its advantages over previous algorithms on real-world datasets. |
Badih Ghazi; Junfeng He; Kai Kohlhoff; Ravi Kumar; Pasin Manurangsi; Vidhya Navalpakkam; Nachiappan Valliappan; |
| 865 | DiFA: Differentiable Feature Acquisition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to acquire a small subset of features that maximize prediction performance. |
Aritra Ghosh; Andrew Lan; |
| 866 | Local Intrinsic Dimensional Entropy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we question the role of cardinality and distribution spread in defining entropy measures for continuous spaces, which can undergo multiple rounds of transformations and distortions, e.g., in neural networks. |
Rohan Ghosh; Mehul Motani; |
| 867 | Improving Uncertainty Quantification of Deep Classifiers Via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). |
Subhankar Ghosh; Taha Belkhouja; Yan Yan; Janardhan Rao Doppa; |
| 868 | Adaptive Hierarchy-Branch Fusion for Online Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel Adaptive Hierarchy-Branch Fusion framework for Online Knowledge Distillation, termed AHBF-OKD, which designs hierarchical branches and adaptive hierarchy-branch fusion module to boost the model diversity and aggregate complementary knowledge. |
Linrui Gong; Shaohui Lin; Baochang Zhang; Yunhang Shen; Ke Li; Ruizhi Qiao; Bo Ren; Muqing Li; Zhou Yu; Lizhuang Ma; |
| 869 | Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To deal with the problems, in this work, we design a deep neural network called \emph{Deep Latent Regularity Net} (DLR-Net). |
Shiqi Gong; Peiyan Hu; Qi Meng; Yue Wang; Rongchan Zhu; Bingguang Chen; Zhiming Ma; Hao Ni; Tie-Yan Liu; |
| 870 | Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing related methods cannot answer temporal queries like what type of interaction will occur next and when it will occur. This paper proposes a temporal point process model for hyperedge prediction to address these problems. |
Tony Gracious; Ambedkar Dukkipati; |
| 871 | An Adaptive Layer to Leverage Both Domain and Task Specific Information from Scarce Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given very small subsets of labeled textual conversations and unlabeled ones, we propose a semi-supervised framework dedicated to customer service data leveraging speaker role information to adapt the model to the domain and the task using a two-step process. |
Gaël Guibon; Matthieu Labeau; Luce Lefeuvre; Chloé Clavel; |
| 872 | Interpolating Graph Pair to Regularize Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a simple and yet effective interpolation-based regularization technique, aiming to improve the generalization of Graph Neural Networks (GNNs) on supervised graph classification. |
Hongyu Guo; Yongyi Mao; |
| 873 | Graph Knows Unknowns: Reformulate Zero-Shot Learning As Sample-Level Graph Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Taking advantage of recently developed graph neural networks (GNNs), we formulate the ZSL problem to a graph-to-semantics mapping task, which can better exploit element-semantics correlation and local sub-structural information in samples. |
Jingcai Guo; Song Guo; Qihua Zhou; Ziming Liu; Xiaocheng Lu; Fushuo Huo; |
| 874 | Self-Supervised Bidirectional Learning for Graph Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a robust self-supervised bidirectional learning method (IA-SSGM) to tackle GM in an unsupervised manner. |
Wenqi Guo; Lin Zhang; Shikui Tu; Lei Xu; |
| 875 | Boosting Graph Neural Networks Via Adaptive Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN. |
Zhichun Guo; Chunhui Zhang; Yujie Fan; Yijun Tian; Chuxu Zhang; Nitesh V. Chawla; |
| 876 | Dream to Generalize: Zero-Shot Model-Based Reinforcement Learning for Unseen Visual Distractions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a novel self-supervised method, Dream to Generalize (Dr. G), for zero-shot MBRL. |
Jeongsoo Ha; Kyungsoo Kim; Yusung Kim; |
| 877 | Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we shed new Bayesian light on the fact that the posterior probability of source importance connects to discriminability and transferability. |
Zhongyi Han; Zhiyan Zhang; Fan Wang; Rundong He; Wan Su; Xiaoming Xi; Yilong Yin; |
| 878 | Astromorphic Self-Repair of Neuromorphic Hardware Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. |
Zhuangyu Han; A N M Nafiul Islam; Abhronil Sengupta; |
| 879 | Estimating Regression Predictive Distributions with Sample Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose SampleNet, a flexible and scalable architecture for modeling uncertainty that avoids specifying a parametric form on the output distribution. |
Ali Harakeh; Jordan Sir Kwang Hu; Naiqing Guan; Steven Waslander; Liam Paull; |
| 880 | NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a lightweight and effective local intrinsic dimension (LID)-based method NAS-LID. |
Xin He; Jiangchao Yao; Yuxin Wang; Zhenheng Tang; Ka Chun Cheung; Simon See; Bo Han; Xiaowen Chu; |
| 881 | Safeguarded Learned Convex Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, unfortunately, many L2O algorithms lack converge guarantees. To fuse the advantages of these approaches, we present a Safe-L2O framework. |
Howard Heaton; Xiaohan Chen; Zhangyang Wang; Wotao Yin; |
| 882 | Improving Long-Horizon Imitation Through Instruction Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore the use of an often unused source of auxiliary supervision: language. |
Joey Hejna; Pieter Abbeel; Lerrel Pinto; |
| 883 | Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. |
Thi Kieu Khanh Ho; Narges Armanfard; |
| 884 | Improving Pareto Front Learning Via Multi-Sample Hypernetworks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. |
Long P. Hoang; Dung D. Le; Tran Anh Tuan; Tran Ngoc Thang; |
| 885 | Towards Better Visualizing The Decision Basis of Networks Via Unfold and Conquer Attribution Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel post-hoc framework, Unfold and Conquer Attribution Guidance (UCAG), which enhances the explainability of the network decision by spatially scrutinizing the input features with respect to the model confidence. |
Jung-Ho Hong; Woo-Jeoung Nam; Kyu-Sung Jeon; Seong-Whan Lee; |
| 886 | Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a novel FL strategy: propagating adversarial robustness from rich-resource users that can afford AT, to those with poor resources that cannot afford it, during federated learning. |
Junyuan Hong; Haotao Wang; Zhangyang Wang; Jiayu Zhou; |
| 887 | Compressed Decentralized Learning of Conditional Mean Embedding Operators in Reproducing Kernel Hilbert Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a decentralized algorithm for a collection of agents to cooperatively approximate CME over a network. |
Boya Hou; Sina Sanjari; Nathan Dahlin; Subhonmesh Bose; |
| 888 | RLEKF: An Optimizer for Deep Potential with Ab Initio Accuracy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel optimizer named reorganized layer extended Kalman filtering (RLEKF), an optimized version of global extended Kalman filtering (GEKF) with a strategy of splitting big and gathering small layers to overcome the O(N^2) computational cost of GEKF. |
Siyu Hu; Wentao Zhang; Qiuchen Sha; Feng Pan; Lin-Wang Wang; Weile Jia; Guangming Tan; Tong Zhao; |
| 889 | Background-Mixed Augmentation for Weakly Supervised Change Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. |
Rui Huang; Ruofei Wang; Qing Guo; Jieda Wei; Yuxiang Zhang; Wei Fan; Yang Liu; |
| 890 | Enabling Knowledge Refinement Upon New Concepts in Abductive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Ignoring those new concepts can lead to significant performance decay, whereas it is challenging to identify new concepts and add them to the existing knowledge base with potential conflicts resolved. We propose the ABL_nc approach which exploits machine learning in ABL to identify new concepts from data, exploits knowledge graph to match them with entities, and refines existing knowledge base to resolve conflicts. |
Yu-Xuan Huang; Wang-Zhou Dai; Yuan Jiang; Zhi-Hua Zhou; |
| 891 | Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper we propose Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering (SGDMC), which promotes the performance of GNN-based deep MVC models by making full use of the self-supervised information and graph information. |
Zongmo Huang; Yazhou Ren; Xiaorong Pu; Shudong Huang; Zenglin Xu; Lifang He; |
| 892 | Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits: A Distributional Learning Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper studies the neural contextual bandit problem from a distributional perspective and proposes NeuralRBMLE, which leverages the likelihood of surrogate parametric distributions to learn the unknown reward distributions and thereafter adapts the RBMLE principle to achieve efficient exploration by properly adding a reward-bias term. |
Yu-Heng Hung; Ping-Chun Hsieh; |
| 893 | Learning Noise-Induced Reward Functions for Surpassing Demonstrations in Imitation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a method to learn rewards from suboptimal demonstrations via a weighted preference learning technique (LERP). |
Liangyu Huo; Zulin Wang; Mai Xu; |
| 894 | XClusters: Explainability-First Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. |
Hyunseung Hwang; Steven Euijong Whang; |
| 895 | Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a provably efficient RL algorithm for the MDP whose state transition is given by a multinomial logistic model. |
Taehyun Hwang; Min-hwan Oh; |
| 896 | Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes fast discrete OT with group-sparse regularizers. |
Yasutoshi Ida; Sekitoshi Kanai; Kazuki Adachi; Atsutoshi Kumagai; Yasuhiro Fujiwara; |
| 897 | Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We leverage probabilistic models of neural representations to investigate how residual networks fit classes. |
Michał Jamroż; Marcin Kurdziel; |
| 898 | Audio-Visual Contrastive Learning with Temporal Self-Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human supervision. |
Simon Jenni; Alexander Black; John Collomosse; |
| 899 | Confidence-Aware Training of Smoothed Classifiers for Certified Robustness Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. |
Jongheon Jeong; Seojin Kim; Jinwoo Shin; |
| 900 | Learnable Path in Neural Controlled Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. |
Sheo Yon Jhin; Minju Jo; Seungji Kook; Noseong Park; |
| 901 | DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – A Focus on Affinity Prediction Problems with Noise Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present DrugOOD, a systematic OOD dataset curator and benchmark for AIDD. |
Yuanfeng Ji; Lu Zhang; Jiaxiang Wu; Bingzhe Wu; Lanqing Li; Long-Kai Huang; Tingyang Xu; Yu Rong; Jie Ren; Ding Xue; Houtim Lai; Wei Liu; Junzhou Huang; Shuigeng Zhou; Ping Luo; Peilin Zhao; Yatao Bian; |
| 902 | MNER-QG: An End-to-End MRC Framework for Multimodal Named Entity Recognition with Query Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. |
Meihuizi Jia; Lei Shen; Xin Shen; Lejian Liao; Meng Chen; Xiaodong He; Zhendong Chen; Jiaqi Li; |
| 903 | Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, beyond manually designed features, we introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network, which learns to predict whether a sample was mislabeled using the raw training dynamics as input. |
Qingrui Jia; Xuhong Li; Lei Yu; Jiang Bian; Penghao Zhao; Shupeng Li; Haoyi Xiong; Dejing Dou; |
| 904 | Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To utilize both offline and online experiences to tune the policies of agents, we introduce online transition correction (OTC) to implicitly correct the offline transition dynamics by modifying sampling probabilities. |
Jiechuan Jiang; Zongqing Lu; |
| 905 | Robust Domain Adaptation for Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Undoubtedly, the noisy correspondence will degenerate the performance of MRC, which however is neglected by existing works. To solve such an untouched problem, we propose to construct QA pairs by additionally using the dialogue related to the documents, as well as a new domain adaptation method for MRC. |
Liang Jiang; Zhenyu Huang; Jia Liu; Zujie Wen; Xi Peng; |
| 906 | Multi-View MOOC Quality Evaluation Via Information-Aware Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of MOOC quality evaluation that is essential for improving the course materials, promoting students’ learning efficiency, and benefiting user services. |
Lu Jiang; Yibin Wang; Jianan Wang; Pengyang Wang; Minghao Yin; |
| 907 | Spatio-Temporal Meta-Graph Learning for Traffic Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. |
Renhe Jiang; Zhaonan Wang; Jiawei Yong; Puneet Jeph; Quanjun Chen; Yasumasa Kobayashi; Xuan Song; Shintaro Fukushima; Toyotaro Suzumura; |
| 908 | Complement Sparsification: Low-Overhead Model Pruning for Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Model pruning/sparsification develops sparse models that could solve this problem, but existing sparsification solutions cannot satisfy at the same time the requirements for low bidirectional communication overhead between the server and the clients, low computation overhead at the clients, and good model accuracy, under the FL assumption that the server does not have access to raw data to fine-tune the pruned models. We propose Complement Sparsification (CS), a pruning mechanism that satisfies all these requirements through a complementary and collaborative pruning done at the server and the clients. |
Xiaopeng Jiang; Cristian Borcea; |
| 909 | Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we tackle 3D molecular pretraining in a complete and novel sense. |
Rui Jiao; Jiaqi Han; Wenbing Huang; Yu Rong; Yang Liu; |
| 910 | Local-Global Defense Against Unsupervised Adversarial Attacks on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Prior research attempts to improve representation robustness by maximizing mutual information between the representation and the perturbed graph, which is sub-optimal because it does not adapt its defense techniques to the severity of the attack. To address this issue, we propose an unsupervised defense method that combines local and global defense to improve the robustness of representation. |
Di Jin; Bingdao Feng; Siqi Guo; Xiaobao Wang; Jianguo Wei; Zhen Wang; |
| 911 | Trafformer: Unify Time and Space in Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the spatial and temporal information is highly correlated in a traffic network, so existing methods may not learn the complex spatial-temporal dependencies hidden in the traffic network due to the decomposed model design. To overcome this limitation, we propose a new model named Trafformer, which unifies spatial and temporal information in one transformer-style model. |
Di Jin; Jiayi Shi; Rui Wang; Yawen Li; Yuxiao Huang; Yu-Bin Yang; |
| 912 | On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the expressibility and learnability of solution functions of convex optimization and their multi-layer architectural extension. |
Ming Jin; Vanshaj Khattar; Harshal Kaushik; Bilgehan Sel; Ruoxi Jia; |
| 913 | Pointerformer: Deep Reinforced Multi-Pointer Transformer for The Traveling Salesman Problem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel end-to-end DRL approach, referred to as Pointerformer, based on multi-pointer Transformer. |
Yan Jin; Yuandong Ding; Xuanhao Pan; Kun He; Li Zhao; Tao Qin; Lei Song; Jiang Bian; |
| 914 | Knowledge-Constrained Answer Generation for Open-Ended Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Knowledge-constrained Generative VideoQA Algorithm (KcGA) with an encoder-decoder pipeline, which enables out-of-domain answer generation through an adaptive external knowledge module and a multi-stream information control mechanism. |
Yao Jin; Guocheng Niu; Xinyan Xiao; Jian Zhang; Xi Peng; Jun Yu; |
| 915 | POEM: Polarization of Embeddings for Domain-Invariant Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most existing DG approaches share the same philosophy to minimize the discrepancy between domains by finding the domain-invariant representations. On the contrary, our proposed method called POEM acquires a strong DG capability by learning domain-invariant and domain-specific representations and polarizing them. |
Sang-Yeong Jo; Sung Whan Yoon; |
| 916 | An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new efficient algorithm with lower regret than even previous inefficient ones. |
Matthew Jones; Huy Nguyen; Thy Nguyen; |
| 917 | Towards More Robust Interpretation Via Local Gradient Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods. In this paper, we provide new insights by taking such normalization into account. |
Sunghwan Joo; SeokHyeon Jeong; Juyeon Heo; Adrian Weller; Taesup Moon; |
| 918 | Identifying Selection Bias from Observational Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study under which conditions we can identify selection bias and give results for both parametric and non-parametric families of distributions. Based on these results we develop two practical methods to determine whether or not an observed sample comes from a distribution subject to selection bias. |
David Kaltenpoth; Jilles Vreeken; |
| 919 | PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a new kind of data-driven PDEs solver, physics-informed cell representations (PIXEL), elegantly combining classical numerical methods and learning-based approaches. |
Namgyu Kang; Byeonghyeon Lee; Youngjoon Hong; Seok-Bae Yun; Eunbyung Park; |
| 920 | On The Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under no assumption of independent samples, we provide a high-probability, polynomial sample complexity bound for vanilla model-based off-policy evaluation that requires partial or uniform coverage. |
Mustafa O. Karabag; Ufuk Topcu; |
| 921 | Communication-Efficient Collaborative Best Arm Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate top-m arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function. |
Nikolai Karpov; Qin Zhang; |
| 922 | Variable-Based Calibration for Machine Learning Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we introduce the notion of variable-based calibration to characterize calibration properties of a model with respect to a variable of interest, generalizing traditional score-based metrics such as expected calibration error (ECE). |
Markelle Kelly; Padhraic Smyth; |
| 923 | Design Amortization for Bayesian Optimal Experimental Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we build off of successful variational approaches, which optimize a parameterized variational model with respect to bounds on the EIG. |
Noble Kennamer; Steven Walton; Alexander Ihler; |
| 924 | On Error and Compression Rates for Prototype Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the close interplay between error and compression in the non-parametric multiclass classification setting in terms of prototype learning rules. |
Omer Kerem; Roi Weiss; |
| 925 | CertiFair: A Framework for Certified Global Fairness of Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. |
Haitham Khedr; Yasser Shoukry; |
| 926 | Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence. We then propose a simple yet effective Key In-distribution feature Replacement BY inpainting (KIRBY) procedure that constructs a surrogate OOD dataset by replacing class-discriminative features of in-distribution samples with marginal background features. |
Jaeyoung Kim; Seo Taek Kong; Dongbin Na; Kyu-Hwan Jung; |
| 927 | FLAME: Free-Form Language-Based Motion Synthesis & Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a diffusion-based motion synthesis and editing model named FLAME. |
Jihoon Kim; Jiseob Kim; Sungjoon Choi; |
| 928 | Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they cannot be used directly for Bayesian neural networks (BNNs) because the variable parameters of BNNs make it difficult to calculate the FIM. To address this problem, we achieve regularization of the FIM of BNNs by specifying a new suitable prior distribution called the inverse-reference (IR) prior. |
Keunseo Kim; Eun-Yeol Ma; Jeongman Choi; Heeyoung Kim; |
| 929 | Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, different from audio forced alignment, it is challenging to develop a reliable visual forced alignment technology for the following two reasons: 1) Visual Speech Recognition (VSR) has a much lower performance compared to audio-based Automatic Speech Recognition (ASR), and 2) the translation from text to video is not reliable, so the method typically used for building audio forced alignment cannot be utilized in developing visual forced alignment. In order to alleviate these challenges, in this paper, we propose a new method that is appropriate for visual forced alignment, namely Deep Visual Forced Alignment (DVFA). |
Minsu Kim; Chae Won Kim; Yong Man Ro; |
| 930 | Better Generalized Few-Shot Learning Even Without Base Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The existing GFSL methods attempt to make the weight norms balanced, which we find help only the variance part, but discard the importance of mean of weights particularly for novel classes, leading to the limited performance in the GFSL problem even with base data. In this paper, we overcome this limitation by proposing a simple yet effective normalization method that can effectively control both mean and variance of the weight distribution of novel classes without using any base samples and thereby achieve a satisfactory performance on both novel and base classes. |
Seong-Woong Kim; Dong-Wan Choi; |
| 931 | Learning Topology-Specific Experts for Molecular Property Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. |
Suyeon Kim; Dongha Lee; SeongKu Kang; Seonghyeon Lee; Hwanjo Yu; |
| 932 | Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel algorithm for generalized linear contextual bandits (GLBs) with a regret bound sublinear to the time horizon, the minimum eigenvalue of the covariance of contexts and a lower bound of the variance of rewards. |
Wonyoung Kim; Kyungbok Lee; Myunghee Cho Paik; |
| 933 | Exploring Temporal Information Dynamics in Spiking Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we ask several important questions for providing a fundamental understanding of SNNs: What are temporal information dynamics inside SNNs? |
Youngeun Kim; Yuhang Li; Hyoungseob Park; Yeshwanth Venkatesha; Anna Hambitzer; Priyadarshini Panda; |
| 934 | FastAMI – A Monte Carlo Approach to The Adjustment for Chance in Clustering Comparison Metrics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose FastAMI, a Monte Carlo-based method to efficiently approximate the Adjusted Mutual Information (AMI) and extend it to the Standardized Mutual Information (SMI). |
Kai Klede; Leo Schwinn; Dario Zanca; Björn Eskofier; |
| 935 | A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing Via Auxiliary Classifier Under Label Noise Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Lipschitz regularization successfully alleviates this problem by training a robust feature extractor, but it requires longer training time and expensive computations. Motivated by this, we propose a simple yet effective method, called ALASCA, which efficiently provides a robust feature extractor under label noise. |
Jongwoo Ko; Bongsoo Yi; Se-Young Yun; |
| 936 | Grouping Matrix Based Graph Pooling with Adaptive Number of Clusters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. |
Sung Moon Ko; Sungjun Cho; Dae-Woong Jeong; Sehui Han; Moontae Lee; Honglak Lee; |
| 937 | The Influence of Dimensions on The Complexity of Computing Decision Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, little is known about the complexity of the underlying computational problem of computing a minimum-size tree for the given training data. We study this problem with respect to the number d of dimensions of the feature space \mathbb{R}^d, which contains n training examples. |
Stephen G. Kobourov; Maarten Löffler; Fabrizio Montecchiani; Marcin Pilipczuk; Ignaz Rutter; Raimund Seidel; Manuel Sorge; Jules Wulms; |
| 938 | Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a similarity model based on entropy, which allows for the creation of physically meaningful ground truth distances for the similarity assessment of scalar and vectorial data, produced from transport and motion-based simulations. |
Georg Kohl; Li-Wei Chen; Nils Thuerey; |
| 939 | Peeling The Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the issue, this paper aims to introduce sparsity into data and proposes an end-to-end efficient training framework from three sparse perspectives, dubbed Tri-Level E-ViT. |
Zhenglun Kong; Haoyu Ma; Geng Yuan; Mengshu Sun; Yanyue Xie; Peiyan Dong; Xin Meng; Xuan Shen; Hao Tang; Minghai Qin; Tianlong Chen; Xiaolong Ma; Xiaohui Xie; Zhangyang Wang; Yanzhi Wang; |
| 940 | Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet the observed degradation in policy performance caused by imperceptible worst-case policy dependent translations along high sensitivity directions (i.e. adversarial perturbations) raises concerns on the robustness of deep reinforcement learning policies. In our paper, we show that these high sensitivity directions do not lie only along particular worst-case directions, but rather are more abundant in the deep neural policy landscape and can be found via more natural means in a black-box setting. |
Ezgi Korkmaz; |
| 941 | Almost Cost-Free Communication in Federated Best Arm Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The goal is to identify the local best arms and the global best arm with minimal total cost, defined as the sum of the total number of arm selections at all the clients and the total communication cost, subject to an upper bound on the error probability. We propose a novel algorithm FedElim that is based on successive elimination and communicates only in exponential time steps and obtain a high probability instance-dependent upper bound on its total cost. |
Srinivas Reddy Kota; P. N. Karthik; Vincent Y. F. Tan; |
| 942 | UEQMS: UMAP Embedded Quick Mean Shift Algorithm for High Dimensional Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, a new algorithm called MeanShift++ (MS++) for low-dimensional clustering was proposed with a speedup of 4000 times over the vanilla mean shift. In this work, starting with a first-of-its-kind theoretical analysis of MS++, we extend its reach to high-dimensional data clustering by integrating the Uniform Manifold Approximation and Projection (UMAP) based dimensionality reduction in the same framework. |
Abhishek Kumar; Swagatam Das; Rammohan Mallipeddi; |
| 943 | The Effect of Diversity in Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). |
Ramnath Kumar; Tristan Deleu; Yoshua Bengio; |
| 944 | Gradient Estimation for Binary Latent Variables Via Gradient Variance Clipping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, DisARM and other estimators have potentially exploding variance near the boundary of the parameter space, where solutions tend to lie. To ameliorate this issue, we propose a new gradient estimator bitflip-1 that is lower variance at the boundaries of the parameter space. |
Russell Z. Kunes; Mingzhang Yin; Max Land; Doron Haviv; Dana Pe’er; Simon Tavaré; |
| 945 | LoNe Sampler: Graph Node Embeddings By Coordinated Local Neighborhood Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Discrete embeddings offer several advantages compared to continuous word2vec-like node embeddings: ease of computation, scalability, and interpretability. We present LoNe Sampler, a suite of algorithms for generating discrete node embeddings by Local Neighborhood Sampling, and address two shortcomings of previous work. |
Konstantin Kutzkov; |
| 946 | WLD-Reg: A Data-Dependent Within-Layer Diversity Regularizer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional ‘between-layer’ feedback with additional ‘within-layer’ feedback to encourage the diversity of the activations within the same layer. |
Firas Laakom; Jenni Raitoharju; Alexandros Iosifidis; Moncef Gabbouj; |
| 947 | SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it suffers from two problems: CNN structure produces inaccurate attention map based on local features, and mutually similar backgrounds cause distraction. To alleviate these problems, we design a novel SpatialFormer structure to generate more accurate attention regions based on global features. |
Jinxiang Lai; Siqian Yang; Wenlong Wu; Tao Wu; Guannan Jiang; Xi Wang; Jun Liu; Bin-Bin Gao; Wei Zhang; Yuan Xie; Chengjie Wang; |
| 948 | A Data Source for Reasoning Embodied Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent. |
Jack Lanchantin; Sainbayar Sukhbaatar; Gabriel Synnaeve; Yuxuan Sun; Kavya Srinet; Arthur Szlam; |
| 949 | Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Differently from many works in approximate recovery, we present results both for bounded Lipschitz losses and for the absolute loss, with the latter relying on Talagrand-type inequalities. |
Antoine Ledent; Rodrigo Alves; Yunwen Lei; Yann Guermeur; Marius Kloft; |
| 950 | I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose TriCL (Tri-directional Contrastive Learning), a general framework for contrastive learning on hypergraphs. |
Dongjin Lee; Kijung Shin; |
| 951 | Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing works for such a situation still suffer from requiring many GPUs and expensive costs. Motivated by this, we propose a novel neural network optimization framework named Bespoke for low-cost deployment. |
Jong-Ryul Lee; Yong-Hyuk Moon; |
| 952 | Time-Aware Random Walk Diffusion to Improve Dynamic Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose TiaRa (Time-aware Random Walk Diffusion), a novel diffusion-based method for augmenting a dynamic graph represented as a discrete-time sequence of graph snapshots. |
Jong-whi Lee; Jinhong Jung; |
| 953 | Demystifying Randomly Initialized Networks for Evaluating Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we rigorously investigate the feature space of models with random weights in comparison to that of trained models. |
Junghyuk Lee; Jun-Hyuk Kim; Jong-Seok Lee; |
| 954 | Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose FedLAMA, a layer-wise adaptive model aggregation scheme for scalable FL. |
Sunwoo Lee; Tuo Zhang; A. Salman Avestimehr; |
| 955 | Goal-Conditioned Q-learning As Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore a connection between off-policy reinforcement learning in goal-conditioned settings and knowledge distillation. |
Alexander Levine; Soheil Feizi; |
| 956 | Optimism in Face of A Context:Regret Guarantees for Stochastic Contextual MDP Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle. |
Orin Levy; Yishay Mansour; |
| 957 | Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works, on the other hand, show weaknesses in instability and inflexibility. To address these issues, we propose a novel method called Partial Message Meta Multigraph search (PMMM) to automatically optimize the neural architecture design on HINs. |
Chao Li; Hao Xu; Kun He; |
| 958 | Learning Adversarially Robust Sparse Networks Via Weight Reparameterization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the recent robust pruning technologies show promising direction to obtain adversarially robust sparse networks, they perform poorly with high sparsity. In this work, we bridge this performance gap by reparameterizing network parameters to simultaneously learn the sparse structure and the robustness. |
Chenhao Li; Qiang Qiu; Zhibin Zhang; Jiafeng Guo; Xueqi Cheng; |
| 959 | ACE: Cooperative Multi-Agent Q-learning with Bidirectional Action-Dependency Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Starting from first principle, in this paper, we manage to solve the non-stationarity problem by proposing bidirectional action-dependent Q-learning (ACE). |
Chuming Li; Jie Liu; Yinmin Zhang; Yuhong Wei; Yazhe Niu; Yaodong Yang; Yu Liu; Wanli Ouyang; |
| 960 | When Online Learning Meets ODE: Learning Without Forgetting on Variable Feature Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite its importance, most studies for algorithms that are capable of handling online features have no ensurance of stationarity point convergence, while the accuracy guaranteed methods are still limited to some simple cases such as L_1 or L_2 norms with square loss. To address this challenging problem, we develop an efficient Dynamic Feature Learning System (DFLS) to perform online learning on the unfixed feature set for more general statistical models and demonstrate how DFLS opens up many new applications. |
Diyang Li; Bin Gu; |
| 961 | FanoutNet: A Neuralized PCB Fanout Automation Method Using Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the problem, we propose a neuralized PCB fanout method by deep reinforcement learning. |
Haiyun Li; Jixin Zhang; Ning Xu; Mingyu Liu; |
| 962 | Causal Recurrent Variational Autoencoder for Medical Time Series Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process. |
Hongming Li; Shujian Yu; Jose Principe; |
| 963 | Dual Mutual Information Constraints for Discriminative Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In previous studies, most of deep clustering methods follow the idea of self-supervised representation learning by maximizing the consistency of all similar instance pairs while ignoring the effect of feature redundancy on clustering performance. In this paper, to address the above issue, we design a dual mutual information constrained clustering method named DMICC which is based on deep contrastive clustering architecture, in which the dual mutual information constraints are particularly employed with solid theoretical guarantees and experimental validations. |
Hongyu Li; Lefei Zhang; Kehua Su; |
| 964 | AdaBoost.C2: Boosting Classifiers Chains for Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These loss functions generally give a comprehensive evaluation on the label set entirety, and thus the characteristics of different labels are ignored. In this paper, we propose a multi-path AdaBoost framework specific to MLC, where each boosting path is established for distinct label and the combination of them is able to provide a maximum optimization to Hamming Loss. |
Jiaxuan Li; Xiaoyan Zhu; Jiayin Wang; |
| 965 | Scaling Up Dynamic Graph Representation Learning Via Spiking Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a scalable framework, namely SpikeNet, to efficiently capture the temporal and structural patterns of temporal graphs. |
Jintang Li; Zhouxin Yu; Zulun Zhu; Liang Chen; Qi Yu; Zibin Zheng; Sheng Tian; Ruofan Wu; Changhua Meng; |
| 966 | Improved Kernel Alignment Regret Bound for Online Kernel Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we improve the kernel alignment regret bound for online kernel learning in the regime of the Hinge loss function. |
Junfan Li; Shizhong Liao; |
| 967 | VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation Under Adverse Conditions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, most of them mainly focus on individual adverse condition such as nighttime or foggy, weakening the model versatility when encountering other adverse weathers. To overcome the above limitations, we propose a novel framework, Visibility Boosting and Logit-Constraint learning (VBLC), tailored for superior normal-toadverse adaptation. |
Mingjia Li; Binhui Xie; Shuang Li; Chi Harold Liu; Xinjing Cheng; |
| 968 | Understanding The Generalization Performance of Spectral Clustering Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: relaxed RatioCut and relaxed NCut. |
Shaojie Li; Sheng Ouyang; Yong Liu; |
| 969 | Restructuring Graph for Higher Homophily Via Adaptive Spectral Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel graph restructuring method that can be integrated into any type of GNNs, including classical GNNs, to leverage the benefits of existing GNNs while alleviating their limitations. |
Shouheng Li; Dongwoo Kim; Qing Wang; |
| 970 | Nearest-Neighbor Sampling Based Conditional Independence Testing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The aim of this paper is to develop a novel alternative of CRT by using nearest-neighbor sampling without assuming the exact form of the distribution of X given Z. Specifically, we utilize the computationally efficient 1-nearest-neighbor to approximate the conditional distribution that encodes the null hypothesis. |
Shuai Li; Ziqi Chen; Hongtu Zhu; Christina Dan Wang; Wang Wen; |
| 971 | Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given heterogeneous graphs with complex structures and rich semantics, it is imperative that salient objects can be accompanied with their influence paths to the predictions, unveiling the reasoning process of HGNs. In this paper, we develop xPath, a new framework that provides fine-grained explanations for black-box HGNs specifying a cause node with its influence path to the target node. |
Tong Li; Jiale Deng; Yanyan Shen; Luyu Qiu; Huang Yongxiang; Caleb Chen Cao; |
| 972 | Metric Nearness Made Practical Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work designed a practical approach in two stages to tackle the challenge and improve the model’s scalability and applicability. |
Wenye Li; Fangchen Yu; Zichen Ma; |
| 973 | Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By incorporating a low-cost prediction engine, we propose a Predictive Exit framework for computation- and energy-efficient deep learning applications. |
Xiangjie Li; Chenfei Lou; Yuchi Chen; Zhengping Zhu; Yingtao Shen; Yehan Ma; An Zou; |
| 974 | Learning with Partial Labels from Semi-supervised Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). |
Ximing Li; Yuanzhi Jiang; Changchun Li; Yiyuan Wang; Jihong Ouyang; |
| 975 | Learning Compact Features Via In-Training Representation Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although the latter provides an unbiased estimation of the former, they are subject to substantial variances derived from the size and number of sampled mini-batches, leading to noisy and jumpy updates. To stabilize such undesirable variance in estimating the true gradients, we propose In-Training Representation Alignment (ITRA) that explicitly aligns feature distributions of two different mini-batches with a matching loss in the SGD training process. |
Xin Li; Xiangrui Li; Deng Pan; Yao Qiang; Dongxiao Zhu; |
| 976 | An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. |
Yanhong Li; Jack Xu; David C. Anastasiu; |
| 977 | Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to employ implicit stochastic gradient descent (ISGD) method to train PINNs for improving the stability of training process. |
Ye Li; Song-Can Chen; Sheng-Jun Huang; |
| 978 | Provable Pathways: Learning Multiple Tasks Over Multiple Paths Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In conjunction, we formalize the benefits of resulting multipath representation when adapting to new downstream tasks. |
Yingcong Li; Samet Oymak; |
| 979 | Towards Inference Efficient Deep Ensemble Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an inference efficient ensemble learning method, to simultaneously optimize for effectiveness and efficiency in ensemble learning. |
Ziyue Li; Kan Ren; Yifan Yang; Xinyang Jiang; Yuqing Yang; Dongsheng Li; |
| 980 | SplitNet: A Reinforcement Learning Based Sequence Splitting Method for The MinMax Multiple Travelling Salesman Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a learning-based method named SplitNet to transform the single TSP solutions into the MinMax mTSP solutions of the same instances. |
Hebin Liang; Yi Ma; Zilin Cao; Tianyang Liu; Fei Ni; Zhigang Li; Jianye Hao; |
| 981 | Stepdown SLOPE for Controlled Feature Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper goes beyond the previous concern limited to the FDR control by considering the stepdown-based SLOPE in order to control the probability of k or more false rejections (k-FWER) and the false discovery proportion (FDP). |
Jingxuan Liang; Xuelin Zhang; Hong Chen; Weifu Li; Xin Tang; |
| 982 | Positive Distribution Pollution: Rethinking Positive Unlabeled Learning from A Unified Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing studies focus on addressing part of these problems, which fail to provide a unified perspective to understand these problems. In this paper, we first rethink these problems by analyzing a typical PU scenario and come up with an insightful point of view that all these problems are inherently connected to one problem, i.e., positive distribution pollution, which refers to the inaccuracy in estimating positive data distribution under very little labeled data. |
Qianqiao Liang; Mengying Zhu; Yan Wang; Xiuyuan Wang; Wanjia Zhao; Mengyuan Yang; Hua Wei; Bing Han; Xiaolin Zheng; |
| 983 | Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It decouples the learning of RL policy and metric owing to its independence on RL policy. We theoretically justify its scalability to continuous state and action spaces and design a practical way to incorporate it into an RL procedure as a representation learning target. |
Weijian Liao; Zongzhang Zhang; Yang Yu; |
| 984 | Geometry-Aware Network for Domain Adaptive Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel Geometry-Aware Network for Domain Adaptation (GANDA), leveraging more compact 3D geometric point cloud representations to shrink the domain gaps. |
Yinghong Liao; Wending Zhou; Xu Yan; Zhen Li; Yizhou Yu; Shuguang Cui; |
| 985 | Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the robustness of existing (demographic) fairness criteria when the algorithm is trained on biased data. |
Yiqiao Liao; Parinaz Naghizadeh; |
| 986 | On The Expressive Flexibility of Self-Attention Matrices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We perform a theoretical analysis of the core component responsible for signal propagation between elements, i.e. the self-attention matrix. |
Valerii Likhosherstov; Krzysztof Choromanski; Adrian Weller; |
| 987 | Wasserstein Actor-Critic: Directed Exploration Via Optimism for Continuous-Actions Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Wasserstein Actor-Critic (WAC), an actor-critic architecture inspired by the recent Wasserstein Q-Learning (WQL), that employs approximate Q-posteriors to represent the epistemic uncertainty and Wasserstein barycenters for uncertainty propagation across the state-action space. |
Amarildo Likmeta; Matteo Sacco; Alberto Maria Metelli; Marcello Restelli; |
| 988 | Dual Label-Guided Graph Refinement for Multi-View Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose dual label-guided graph refinement for multi-view graph clustering (DuaLGR), to alleviate the vulnerability in facing low homophilous graphs. |
Yawen Ling; Jianpeng Chen; Yazhou Ren; Xiaorong Pu; Jie Xu; Xiaofeng Zhu; Lifang He; |
| 989 | Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a novel neural architecture for GCRL that achieves significantly better sample efficiency than the commonly-used monolithic network architecture. |
Bo Liu; Yihao Feng; Qiang Liu; Peter Stone; |
| 990 | DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet. |
Chengliang Liu; Jie Wen; Xiaoling Luo; Chao Huang; Zhihao Wu; Yong Xu; |
| 991 | Incomplete Multi-View Multi-Label Learning Via Label-Guided Masked View- and Category-Aware Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To cope with these problems, we propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper? |
Chengliang Liu; Jie Wen; Xiaoling Luo; Yong Xu; |
| 992 | Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose learning to dynamically select discretization tightness conditioned on inputs, based on the hypothesis that data naturally contains variations in complexity that call for different levels of representational coarseness which is observed in many heterogeneous data sets. |
Dianbo Liu; Alex Lamb; Xu Ji; Pascal Junior Tikeng Notsawo; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi; |
| 993 | Combating Mode Collapse Via Offline Manifold Entropy Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs. |
Haozhe Liu; Bing Li; Haoqian Wu; Hanbang Liang; Yawen Huang; Yuexiang Li; Bernard Ghanem; Yefeng Zheng; |
| 994 | Robust Representation Learning By Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. |
Qiyuan Liu; Qi Zhou; Rui Yang; Jie Wang; |
| 995 | Reliable Robustness Evaluation Via Automatically Constructed Attack Ensembles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present AutoAE, a conceptually simple approach for automatically constructing AEs. |
Shengcai Liu; Fu Peng; Ke Tang; |
| 996 | Enhancing The Antidote: Improved Pointwise Certifications Against Poisoning Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, by examining worst-case behaviours Certified Defences make it possible to provide guarantees of the robustness of a sample against adversarial attacks modifying a finite number of training samples, known as pointwise certification. We achieve this by exploiting both Differential Privacy and the Sampled Gaussian Mechanism to ensure the invariance of prediction for each testing instance against finite numbers of poisoned examples. |
Shijie Liu; Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein; |
| 997 | Safe Multi-View Deep Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How to avoid worsening classification performance when adding views is crucial for multi-view deep learning but rarely studied. To tackle this limitation, in this paper, we reformulate the multi-view classification problem from the perspective of safe learning and thereby propose a Safe Multi-view Deep Classification (SMDC) method, which can guarantee that the classification performance does not deteriorate when fusing multiple views. |
Wei Liu; Yufei Chen; Xiaodong Yue; Changqing Zhang; Shaorong Xie; |
| 998 | Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the tensor compressive sensing problem based on the tensor correlated total variation, which is a new regularizer used to simultaneously capture both properties existing in the same dataset. |
Xinling Liu; Jingyao Hou; Jiangjun Peng; Hailin Wang; Deyu Meng; Jianjun Wang; |
| 999 | Coupling Artificial Neurons in BERT and Biological Neurons in The Human Brain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Overall, our study introduces a novel, general, and effective framework to link transformer-based NLP models and neural activities in response to language and may provide novel insights for future studies such as brain-inspired evaluation and development of NLP models. |
Xu Liu; Mengyue Zhou; Gaosheng Shi; Yu Du; Lin Zhao; Zihao Wu; David Liu; Tianming Liu; Xintao Hu; |
| 1000 | EASAL: Entity-Aware Subsequence-Based Active Learning for Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the mentioned limitations, in this paper, we allow the active learning algorithm to query subsequences within sentences and propose an Entity-Aware Subsequences-based Active Learning (EASAL) that utilizes an effective Head-Tail pointer to query one entity-aware subsequence for each sentence based on BERT. |
Yang Liu; Jinpeng Hu; Zhihong Chen; Xiang Wan; Tsung-Hui Chang; |
| 1001 | Online Hyperparameter Optimization for Class-Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. |
Yaoyao Liu; Yingying Li; Bernt Schiele; Qianru Sun; |
| 1002 | Hard Sample Aware Network for Contrastive Deep Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. |
Yue Liu; Xihong Yang; Sihang Zhou; Xinwang Liu; Zhen Wang; Ke Liang; Wenxuan Tu; Liang Li; Jingcan Duan; Cancan Chen; |
| 1003 | Temporal-Frequency Co-training for Time Series Semi-supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a Time Series SSL framework via Temporal-Frequency Co-training (TS-TFC), leveraging the complementary information from two distinct views for unlabeled data learning. |
Zhen Liu; Qianli Ma; Peitian Ma; Linghao Wang; |
| 1004 | Q-functionals for Value-Based Continuous Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Q-functionals, an alternative architecture for continuous control deep reinforcement learning. |
Samuel Lobel; Sreehari Rammohan; Bowen He; Shangqun Yu; George Konidaris; |
| 1005 | A Coreset Learning Reality Check Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. |
Fred Lu; Edward Raff; James Holt; |
| 1006 | Centerless Multi-View K-means Based on The Adjacency Matrix Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the affinity matrix, we propose a novel multi-view K-Means based on the adjacency matrix. |
Han Lu; Quanxue Gao; Qianqian Wang; Ming Yang; Wei Xia; |
| 1007 | PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We attribute these problems to the poor abilities of existing predictors to character the sub-models’ structure, specifically the topology information extraction and the node feature representation of the input graph data. To address these problems, we propose a Transformer-like NAS predictor PINAT, consisting of a Permutation INvariance Augmentation module serving as both token embedding layer and self-attention head, as well as a Laplacian matrix to be the positional encoding. |
Shun Lu; Yu Hu; Peihao Wang; Yan Han; Jianchao Tan; Jixiang Li; Sen Yang; Ji Liu; |
| 1008 | Multi-View Domain Adaptive Object Detection on Camera Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. |
Yan Lu; Zhun Zhong; Yuanchao Shu; |
| 1009 | Generative Label Enhancement with Gaussian Mixture and Partial Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a generative label enhancement model to encode the process of generating feature vectors and logical label vectors from label distributions in a principled way. |
Yunan Lu; Liang He; Fan Min; Weiwei Li; Xiuyi Jia; |
| 1010 | Crowd-Level Abnormal Behavior Detection Via Multi-Scale Motion Consistency Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). |
Linbo Luo; Yuanjing Li; Haiyan Yin; Shangwei Xie; Ruimin Hu; Wentong Cai; |
| 1011 | MVCINN: Multi-View Diabetic Retinopathy Detection Using A Deep Cross-Interaction Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main reason is that most of the previous methods only use single-view data, and the single field of view (FOV) only accounts for about 13% of the FOV of the retina, resulting in the loss of most lesion features. To alleviate this problem, we propose a multi-view model for DR detection, which takes full advantage of multi-view images covering almost all of the retinal field. |
Xiaoling Luo; Chengliang Liu; Waikeung Wong; Jie Wen; Xiaopeng Jin; Yong Xu; |
| 1012 | Local Explanations for Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel perspective to understanding RL policies based on identifying important states from automatically learned meta-states. |
Ronny Luss; Amit Dhurandhar; Miao Liu; |
| 1013 | Compositional Prototypical Networks for Few-Shot Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, our motivation is to explicitly learn some fine-grained and transferable meta-knowledge so that feature reusability can be further improved. |
Qiang Lyu; Weiqiang Wang; |
| 1014 | Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack Against Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). |
Xiaoting Lyu; Yufei Han; Wei Wang; Jingkai Liu; Bin Wang; Jiqiang Liu; Xiangliang Zhang; |
| 1015 | OMPQ: Orthogonal Mixed Precision Quantization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches rely heavily on an extremely time-consuming search process and various relaxations when seeking the optimal bit configuration. To address this issue, we propose to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width. |
Yuexiao Ma; Taisong Jin; Xiawu Zheng; Yan Wang; Huixia Li; Yongjian Wu; Guannan Jiang; Wei Zhang; Rongrong Ji; |
| 1016 | Recovering The Graph Underlying Networked Dynamical Systems Under Partial Observability: A Deep Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a new feature-based paradigm: to each pair of nodes, we compute a feature vector from the observed time series. |
Sérgio Machado; Anirudh Sridhar; Paulo Gil; Jorge Henriques; José M. F. Moura; Augusto Santos; |
| 1017 | LIMIP: Lifelong Learning to Solve Mixed Integer Programs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs. |
Sahil Manchanda; Sayan Ranu; |
| 1018 | Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Consider a network of N decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called DEEPSTORM, that achieves optimal sample complexity for this setting. |
Gabriel Mancino-Ball; Shengnan Miao; Yangyang Xu; Jie Chen; |
| 1019 | Online Reinforcement Learning with Uncertain Episode Lengths Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider a general framework of episodic reinforcement learning when the length of each episode is drawn from a distribution. |
Debmalya Mandal; Goran Radanovic; Jiarui Gan; Adish Singla; Rupak Majumdar; |
| 1020 | Tight Performance Guarantees of Imitator Policies with Continuous Actions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study BC with the goal of providing theoretical guarantees on the performance of the imitator policy in the case of continuous actions. |
Davide Maran; Alberto Maria Metelli; Marcello Restelli; |
| 1021 | Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. |
Andrei Margeloiu; Nikola Simidjievski; Pietro Liò; Mateja Jamnik; |
| 1022 | Learning Revenue Maximization Using Posted Prices for Stochastic Strategic Patient Buyers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The seller, using posted prices, would like to maximize her revenue from selling to the buyer. In this paper, we formalize this setting and characterize the resulting Stackelberg equilibrium, where the seller first commits to her strategy, and then the buyers best respond. |
Eitan-Hai Mashiah; Idan Attias; Yishay Mansour; |
| 1023 | Boundary Graph Neural Networks for 3D Simulations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce an effective theory to model particle-boundary interactions, which leads to our new Boundary Graph Neural Networks (BGNNs) that dynamically modify graph structures to obey boundary conditions. |
Andreas Mayr; Sebastian Lehner; Arno Mayrhofer; Christoph Kloss; Sepp Hochreiter; Johannes Brandstetter; |
| 1024 | Diffusion Models Beat GANs on Topology Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, GANs can be challenging to train, have limited generalizability, and often neglect important performance objectives such as mechanical compliance and manufacturability. To address these issues, we propose a new architecture called TopoDiff that uses conditional diffusion models to perform performance-aware and manufacturability-aware topology optimization. |
François Mazé; Faez Ahmed; |
| 1025 | VIDM: Video Implicit Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in an implicit condition manner, i.e. one can sample plausible video motions according to the latent feature of frames. |
Kangfu Mei; Vishal Patel; |
| 1026 | Towards Interpreting and Utilizing Symmetry Property in Adversarial Examples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify symmetry property in adversarial scenario by viewing adversarial attack in a fine-grained manner. |
Shibin Mei; Chenglong Zhao; Bingbing Ni; Shengchao Yuan; |
| 1027 | The Unreasonable Effectiveness of Deep Evidential Regression Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification. |
Nis Meinert; Jakob Gawlikowski; Alexander Lavin; |
| 1028 | HyperJump: Accelerating HyperBand Via Risk Modelling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce HyperJump, a new approach that builds on HyperBand’s robust search strategy and complements it with novel model-based risk analysis techniques that accelerate the search by skipping the evaluation of low risk configurations, i.e., configurations that are likely to be eventually discarded by HyperBand. |
Pedro Mendes; Maria Casimiro; Paolo Romano; David Garlan; |
| 1029 | MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. |
Qianwen Meng; Hangwei Qian; Yong Liu; Lizhen Cui; Yonghui Xu; Zhiqi Shen; |
| 1030 | Off-Policy Proximal Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an Off-Policy Proximal Policy Optimization method (Off-Policy PPO) that improves the sample efficiency of PPO by utilizing off-policy data. |
Wenjia Meng; Qian Zheng; Gang Pan; Yilong Yin; |
| 1031 | Information-Theoretic Causal Discovery and Intervention Detection Over Multiple Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To efficiently discover causal networks and intervention targets in practice, we introduce the ORION algorithm, which through extensive experiments we show outperforms the state of the art in causal inference over multiple environments. |
Osman Mian; Michael Kamp; Jilles Vreeken; |
| 1032 | AIO-P: Expanding Neural Performance Predictors Beyond Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel All-in-One Predictor (AIO-P), which aims to pretrain neural predictors on architecture examples from multiple, separate computer vision (CV) task domains and multiple architecture spaces, and then transfer to unseen downstream CV tasks or neural architectures. |
Keith G. Mills; Di Niu; Mohammad Salameh; Weichen Qiu; Fred X. Han; Puyuan Liu; Jialin Zhang; Wei Lu; Shangling Jui; |
| 1033 | GENNAPE: Towards Generalized Neural Architecture Performance Estimators Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and a fuzzy clustering-based predictor ensemble. |
Keith G. Mills; Fred X. Han; Jialin Zhang; Fabian Chudak; Ali Safari Mamaghani; Mohammad Salameh; Wei Lu; Shangling Jui; Di Niu; |
| 1034 | Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present Adaptive IMLE (AIMLE), the first adaptive gradient estimator for complex discrete distributions: it adaptively identifies the target distribution for IMLE by trading off the density of gradient information with the degree of bias in the gradient estimates. |
Pasquale Minervini; Luca Franceschi; Mathias Niepert; |
| 1035 | Why Capsule Neural Networks Do Not Scale: Challenging The Dynamic Parse-Tree Assumption Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, despite major efforts, no work was able to scale the CapsNet architecture to more reasonable-sized datasets. Here, we provide a reason for this failure and argue that it is most likely not possible to scale CapsNets beyond toy examples. |
Matthias Mitterreiter; Marcel Koch; Joachim Giesen; Sören Laue; |
| 1036 | Multiplex Graph Representation Learning Via Common and Private Information Mining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Self-supervised multiplex graph representation learning (SMGRL) has attracted increasing interest, but previous SMGRL methods still suffer from the following issues: (i) they focus on the common information only (but ignore the private information in graph structures) to lose some essential characteristics related to downstream tasks, and (ii) they ignore the redundant information in node representations of each graph. To solve these issues, this paper proposes a new SMGRL method by jointly mining the common information and the private information in the multiplex graph while minimizing the redundant information within node representations. |
Yujie Mo; Zongqian Wu; Yuhuan Chen; Xiaoshuang Shi; Heng Tao Shen; Xiaofeng Zhu; |
| 1037 | Fundamentals of Task-Agnostic Data Valuation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on task-agnostic data valuation without any validation requirements. |
Mohammad Mohammadi Amiri; Frederic Berdoz; Ramesh Raskar; |
| 1038 | Exploring The Interaction Between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, three major challenges remain unaddressed. First, current models overlook the impact of the cumulative errors induced by the pseudo-supervised embedding clustering task (Feature Randomness). Second, existing methods neglect the effect of the strong competition between embedding clustering and reconstruction (Feature Drift). Third, the previous deep clustering models regularly fail to consider the topological information of the latent data, even though the local and global latent configurations can bring complementary views to the clustering task. To address these challenges, we propose a novel approach that explores the interaction between local and global latent configurations to progressively adjust the reconstruction and embedding clustering tasks. |
Nairouz Mrabah; Mohamed Mahmoud Amar; Mohamed Bouguessa; Abdoulaye Banire Diallo; |
| 1039 | Corruption-Tolerant Algorithms for Generalized Linear Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data. |
Bhaskar Mukhoty; Debojyoti Dey; Purushottam Kar; |
| 1040 | Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Can we design a provably efficient algorithm that achieves this ambitious goal of systematic generalization? In this paper, we give a partially positive answer to this question. |
Mirco Mutti; Riccardo De Santi; Emanuele Rossi; Juan Felipe Calderon; Michael Bronstein; Marcello Restelli; |
| 1041 | Mean Estimation of Truncated Mixtures of Two Gaussians: A Gradient Based Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the problem of estimating the means of a mixture of two balanced d-dimensional Gaussians when the samples are prone to truncation. |
Sai Ganesh Nagarajan; Gerasimos Palaiopanos; Ioannis Panageas; Tushar Vaidya; Samson Yu; |
| 1042 | An Operator Theoretic Approach for Analyzing Sequence Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast, we propose to analyze trained neural networks using an operator theoretic approach which is rooted in Koopman theory, the Koopman Analysis of Neural Networks (KANN). |
Ilan Naiman; Omri Azencot; |
| 1043 | Do Invariances in Deep Neural Networks Align with Human Perception? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an adversarial regularizer on the IRI generation loss that finds IRIs that make any model appear to have very little shared invariance with humans. |
Vedant Nanda; Ayan Majumdar; Camila Kolling; John P. Dickerson; Krishna P. Gummadi; Bradley C. Love; Adrian Weller; |
| 1044 | Counterfactual Learning with General Data-Generating Policies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE method for a class of both full support and deficient support logging policies in contextual-bandit settings. |
Yusuke Narita; Kyohei Okumura; Akihiro Shimizu; Kohei Yata; |
| 1045 | Efficient and Accurate Learning of Mixtures of Plackett-Luce Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the final estimate may deviate from the true maximum likelihood estimate as a consequence. In this paper, we address these known limitations. |
Duc Nguyen; Anderson Y. Zhang; |
| 1046 | Behavioral Learning in Security Games: Threat of Multi-Step Manipulative Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper studies the problem of multi-step manipulative attacks in Stackelberg security games, in which a clever attacker attempts to orchestrate its attacks over multiple time steps to mislead the defender’s learning of the attacker’s behavior. |
Thanh H. Nguyen; Arunesh Sinha; |
| 1047 | On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an algorithm called Bootstrapped and Constrained Pessimistic Value Iteration (BCP-VI), which leverages data bootstrapping and constrained optimization on top of pessimism. |
Thanh Nguyen-Tang; Ming Yin; Sunil Gupta; Svetha Venkatesh; Raman Arora; |
| 1048 | Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the analysis results, we propose a gate function called fast gate that has a doubly exponential convergence rate with respect to inputs by simple function composition. |
Kentaro Ohno; Sekitoshi Kanai; Yasutoshi Ida; |
| 1049 | Backpropagation-Free Deep Learning with Recursive Local Representation Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a backprop-free procedure, recursive local representation alignment, for training large-scale architectures. |
Alexander G. Ororbia; Ankur Mali; Daniel Kifer; C. Lee Giles; |
| 1050 | Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an algorithm, that a) uses penalized maximum likelihood estimators to learn the unknown parameters, b) injects a calibrated Gaussian noise in the parameter of rewards to ensure exploration, and c) leverages linearity of the bilinear exponential family transitions with respect to an underlying RKHS to perform tractable planning. |
Reda Ouhamma; Debabrota Basu; Odalric Maillard; |
| 1051 | H-TSP: Hierarchically Solving The Large-Scale Traveling Salesman Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Traveling Salesman Problem (TSP). |
Xuanhao Pan; Yan Jin; Yuandong Ding; Mingxiao Feng; Li Zhao; Lei Song; Jiang Bian; |
| 1052 | Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion Under Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the challenges in accurate and real-time traffic congestion prediction under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction framework that delivers higher accuracy and lower latency than SOTA solutions. |
Zhenyu Pan; Anshujit Sharma; Jerry Yao-Chieh Hu; Zhuo Liu; Ang Li; Han Liu; Michael Huang; Tony Geng; |
| 1053 | FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we analyze two direct causes of unfairness in FL – an unfair direction and an improper step size when updating the model. |
Zibin Pan; Shuyi Wang; Chi Li; Haijin Wang; Xiaoying Tang; Junhua Zhao; |
| 1054 | Geometric Inductive Biases for Identifiable Unsupervised Learning of Disentangled Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, in additional to the PCA inductive biases, we propose novel geometric inductive biases from the manifold perspective for unsupervised disentangling, which induce the model to capture the global geometric properties of the data manifold with guaranteed model identifiability. |
Ziqi Pan; Li Niu; Liqing Zhang; |
| 1055 | Isometric Manifold Learning Using Hierarchical Flow Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the Hierarchical Flow (HF) model constrained by isometric regularizations for manifold learning that combines manifold learning goals such as dimensionality reduction, inference, sampling, projection and density estimation into one unified framework. |
Ziqi Pan; Jianfu Zhang; Li Niu; Liqing Zhang; |
| 1056 | Evidential Conditional Neural Processes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Evidential Conditional Neural Processes (ECNP), which replace the standard Gaussian distribution used by CNP with a much richer hierarchical Bayesian structure through evidential learning to achieve epistemic-aleatoric uncertainty decomposition. |
Deep Shankar Pandey; Qi Yu; |
| 1057 | Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes balanced column-wise block pruning, named BCBP, to satisfy two conditions: the column-wise minimal size of the pruning unit and balanced workloads. |
Cheonjun Park; Mincheol Park; Hyun Jae Oh; Minkyu Kim; Myung Kuk Yoon; Suhyun Kim; Won Woo Ro; |
| 1058 | Dynamic Structure Pruning for Compressing CNNs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a novel structure pruning method, termed as dynamic structure pruning, to identify optimal pruning granularities for intra-channel pruning. |
Jun-Hyung Park; Yeachan Kim; Junho Kim; Joon-Young Choi; SangKeun Lee; |
| 1059 | Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces Via State Abstraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to improve the accuracy of OPE estimators by projecting the high-dimensional state-space into a low-dimensional state-space using concepts from the state abstraction literature. |
Brahma S. Pavse; Josiah P. Hanna; |
| 1060 | Conceptual Reinforcement Learning for Language-Conditioned Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing studies of language-conditioned RL methods often learn the joint representation as a simple latent layer for the given instances (episode-specific observation and text), which inevitably includes noisy or irrelevant information and cause spurious correlations that are dependent on instances, thus hurting generalization performance and training efficiency. To address the above issue, we propose a conceptual reinforcement learning (CRL) framework to learn the concept-like joint representation for language-conditioned policy. |
Shaohui Peng; Xing Hu; Rui Zhang; Jiaming Guo; Qi Yi; Ruizhi Chen; Zidong Du; Ling Li; Qi Guo; Yunji Chen; |
| 1061 | Weighted Policy Constraints for Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the available dataset usually contains sub-optimal or inferior actions, constraining the policy near all these actions will make the policy inevitably learn inferior behaviors, limiting the performance of the algorithm. Based on this observation, we propose a weighted policy constraints (wPC) method that only constrains the learned policy to desirable behaviors, making room for policy improvement on other parts. |
Zhiyong Peng; Changlin Han; Yadong Liu; Zongtan Zhou; |
| 1062 | Latent Autoregressive Source Separation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, using existing pre-trained models to perform new non-trivial tasks is difficult since it requires additional fine-tuning or extensive training to elicit prompting. This paper introduces LASS as a way to perform vector-quantized Latent Autoregressive Source Separation (i.e., de-mixing an input signal into its constituent sources) without requiring additional gradient-based optimization or modifications of existing models. |
Emilian Postolache; Giorgio Mariani; Michele Mancusi; Andrea Santilli; Luca Cosmo; Emanuele Rodolà; |
| 1063 | Explaining Random Forests Using Bipolar Argumentation and Markov Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As the complexity of the explanation problems is high, we present an efficient approximation algorithm with probabilistic approximation guarantees. |
Nico Potyka; Xiang Yin; Francesca Toni; |
| 1064 | A Model-Agnostic Heuristics for Selective Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. |
Andrea Pugnana; Salvatore Ruggieri; |
| 1065 | Experimental Observations of The Topology of Convolutional Neural Network Activations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we apply cutting edge techniques from TDA with the goal of gaining insight towards interpretability of convolutional neural networks used for image classification. |
Emilie Purvine; Davis Brown; Brett Jefferson; Cliff Joslyn; Brenda Praggastis; Archit Rathore; Madelyn Shapiro; Bei Wang; Youjia Zhou; |
| 1066 | CMVAE: Causal Meta VAE for Unsupervised Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches may be misled by the context-bias (e.g. background) from the training data. In this paper, we abstract the unsupervised meta-learning problem into a Structural Causal Model (SCM) and point out that such bias arises due to hidden confounders. |
Guodong Qi; Huimin Yu; |
| 1067 | Rethinking Data-Free Quantization As A Zero-Sum Game Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: — impel us to revisit DFQ. In this paper, we answer the above questions from a game-theory perspective to specialize DFQ as a zero-sum game between two players — a generator and a quantized network, and further propose an Adaptability-aware Sample Generation (AdaSG) method. |
Biao Qian; Yang Wang; Richang Hong; Meng Wang; |
| 1068 | Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we mathematically model the data distribution and the discrepancy at the incremental stages with mixture uniform distribution (MUD). |
Sunyuan Qiang; Jiayi Hou; Jun Wan; Yanyan Liang; Zhen Lei; Du Zhang; |
| 1069 | Mutual-Enhanced Incongruity Learning Network for Multi-Modal Sarcasm Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing methods have achieved compelling success, they are disturbed by irrelevant information extracted from the whole image and text, or overlooking some important information due to the incomplete input. To address these limitations, we propose a Mutual-enhanced Incongruity Learning Network for multi-modal sarcasm detection, named MILNet. |
Yang Qiao; Liqiang Jing; Xuemeng Song; Xiaolin Chen; Lei Zhu; Liqiang Nie; |
| 1070 | Training Meta-Surrogate Model for Transferable Adversarial Attack Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous works have studied what kind of attacks to the surrogate model can generate more transferable adversarial examples, but their performances are still limited due to the mismatches between surrogate models and the target model. In this paper, we tackle this problem from a novel angle—instead of using the original surrogate models, can we obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easily transferred to other models? |
Yunxiao Qin; Yuanhao Xiong; Jinfeng Yi; Cho-Jui Hsieh; |
| 1071 | Stochastic Contextual Bandits with Long Horizon Rewards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to avoid polynomial dependence on h, we propose new algorithms that leverage sparsity to discover the dependence pattern and arm parameters jointly. |
Yuzhen Qin; Yingcong Li; Fabio Pasqualetti; Maryam Fazel; Samet Oymak; |
| 1072 | Gradient-Variation Bound for Online Convex Optimization with Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide an instance-dependent bound for online convex optimization with complex constraints obtained by a novel online primal-dual mirror-prox algorithm. |
Shuang Qiu; Xiaohan Wei; Mladen Kolar; |
| 1073 | Bellman Meets Hawkes: Model-Based Reinforcement Learning Via Temporal Point Processes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present a novel framework of the model-based reinforcement learning where the agent’s actions and observations are asynchronous stochastic discrete events occurring in continuous-time. |
Chao Qu; Xiaoyu Tan; Siqiao Xue; Xiaoming Shi; James Zhang; Hongyuan Mei; |
| 1074 | GLUECons: A Generic Benchmark for Learning Under Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. |
Hossein Rajaby Faghihi; Aliakbar Nafar; Chen Zheng; Roshanak Mirzaee; Yue Zhang; Andrzej Uszok; Alexander Wan; Tanawan Premsri; Dan Roth; Parisa Kordjamshidi; |
| 1075 | Provable Detection of Propagating Sampling Bias in Prediction Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage. |
Pavan Ravishankar; Qingyu Mo; Edward McFowland III; Daniel B. Neill; |
| 1076 | Diffusing Gaussian Mixtures for Generating Categorical Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a generative model for categorical data based on diffusion models with a focus on high-quality sample generation, and propose sampled-based evaluation methods. |
Florence Regol; Mark Coates; |
| 1077 | Hypernetworks for Zero-Shot Transfer in Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. |
Sahand Rezaei-Shoshtari; Charlotte Morissette; Francois R. Hogan; Gregory Dudek; David Meger; |
| 1078 | Automata Cascades: Expressivity and Sample Complexity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex systems made of many components, each implementing a specific functionality. |
Alessandro Ronca; Nadezda Alexandrovna Knorozova; Giuseppe De Giacomo; |
| 1079 | ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on these, our ESPT objective is defined as maximizing the local spatial relationship consistency between the original episode and the transformed one. |
Yi Rong; Xiongbo Lu; Zhaoyang Sun; Yaxiong Chen; Shengwu Xiong; |
| 1080 | Planning and Learning with Adaptive Lookahead Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we expand beyond the naive fixed horizon and propose a theoretically justified strategy for adaptive selection of the planning horizon as a function of the state-dependent value estimate. |
Aviv Rosenberg; Assaf Hallak; Shie Mannor; Gal Chechik; Gal Dalal; |
| 1081 | DisGUIDE: Disagreement-Guided Data-Free Model Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce a novel generator training scheme that maximizes the disagreement loss between two clone models that attempt to copy the model under attack. |
Jonathan Rosenthal; Eric Enouen; Hung Viet Pham; Lin Tan; |
| 1082 | Overcoming Concept Shift in Domain-Aware Settings Through Consolidated Internal Distributions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We develop an algorithm to improve the predictive performance of a pre-trained model under \textit{concept shift} without retraining the model from scratch when only unannotated samples of initial concepts are accessible. |
Mohammad Rostami; Aram Galstyan; |
| 1083 | Inferring Patient Zero on Temporal Networks Via Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This backtracking task is challenging for two reasons. First, due to the sudden emergence of local epidemics, information recording the spreading process is limited. Second, the spreading process has strong randomness. To address these challenges, we tailor a gnn-based model to establish the inverse statistical association between the current and initial state implicitly. |
Xiaolei Ru; Jack Murdoch Moore; Xin-Ya Zhang; Yeting Zeng; Gang Yan; |
| 1084 | Accommodating Audio Modality in CLIP for Multimodal Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend the state-of-the-art Vision-Language model CLIP to accommodate the audio modality for Vision-Language-Audio multimodal processing. |
Ludan Ruan; Anwen Hu; Yuqing Song; Liang Zhang; Sipeng Zheng; Qin Jin; |
| 1085 | Forecasting with Sparse But Informative Variables: A Case Study in Predicting Blood Glucose Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, when auxiliary signals are generated at a much lower frequency than the target variable (e.g., blood glucose measurements are made every 5 minutes, while meals occur once every few hours), even well-known extrinsic effects (e.g., carbohydrates increase blood glucose) may prove difficult to learn. To better utilize these sparse but informative variables (SIVs), we introduce a novel encoder/decoder forecasting approach that accurately learns the per-timepoint effect of the SIV, by (i) isolating it from intrinsic effects and (ii) restricting its learned effect based on domain knowledge. |
Harry Rubin-Falcone; Joyce Lee; Jenna Wiens; |
| 1086 | On The Sample Complexity of Representation Learning in Multi-Task Bandits with Global and Local Structure Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We consider the best-arm identification problem with fixed confidence, where, in each round, the learner actively selects both a task, and an arm, and observes the corresponding reward. |
Alessio Russo; Alexandre Proutiere; |
| 1087 | Simultaneously Updating All Persistence Values in Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we derive a novel operator, the All-Persistence Bellman Operator, which allows an effective use of both the low-persistence experience, by decomposition into sub-transition, and the high-persistence experience, thanks to the introduction of a suitable bootstrap procedure. |
Luca Sabbioni; Luca Al Daire; Lorenzo Bisi; Alberto Maria Metelli; Marcello Restelli; |
| 1088 | Continual Learning with Scaled Gradient Projection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The degree of gradient scaling along these spaces depends on the importance of the bases spanning them. We propose an efficient method for computing and accumulating importance of these bases using the singular value decomposition of the input representations for each task. |
Gobinda Saha; Kaushik Roy; |
| 1089 | Fast Offline Policy Optimization for Large Scale Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This calculation is untenable in many real world examples such as large catalogue recommender systems, severely limiting the usefulness of this method in practice. In this paper, we derive an approximation of these policy learning algorithms that scale logarithmically with the catalogue size. |
Otmane Sakhi; David Rohde; Alexandre Gilotte; |
| 1090 | Losses Over Labels: Weakly Supervised Learning Via Direct Loss Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we question a foundational premise of the typical weakly supervised learning pipeline: given that the heuristic provides all “label” information, why do we need to generate pseudolabels at all? |
Dylan Sam; J. Zico Kolter; |
| 1091 | Representation Learning By Detecting Incorrect Location Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel self-supervised learning (SSL) loss for image representation learning. |
Sepehr Sameni; Simon Jenni; Paolo Favaro; |
| 1092 | Sparse Coding in A Dual Memory System for Lifelong Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our method maintains an additional long-term semantic memory that aggregates and consolidates information encoded in the synaptic weights of the working model. Our extensive evaluation and characteristics analysis show that equipped with these biologically inspired mechanisms, the model can further mitigate forgetting. |
Fahad Sarfraz; Elahe Arani; Bahram Zonooz; |
| 1093 | Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present CrissCross, a self-supervised framework for learning audio-visual representations. |
Pritam Sarkar; Ali Etemad; |
| 1094 | Dropout Is NOT All You Need to Prevent Gradient Leakage Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. |
Daniel Scheliga; Patrick Maeder; Marco Seeland; |
| 1095 | Exploration Via Epistemic Value Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators. |
Simon Schmitt; John Shawe-Taylor; Hado van Hasselt; |
| 1096 | Multi-Source Survival Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast to previous multi-task setups, we want to investigate how to efficiently adapt to a new survival target domain from multiple survival source domains. For this, we introduce a new survival metric and the corresponding discrepancy measure between survival distributions. |
Ammar Shaker; Carolin Lawrence; |
| 1097 | What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we introduce a novel task – EXCLAIM, generating explanations for visual semantic role labeling in memes. |
Shivam Sharma; Siddhant Agarwal; Tharun Suresh; Preslav Nakov; Md. Shad Akhtar; Tanmoy Chakraborty; |
| 1098 | Post-hoc Uncertainty Learning Using A Dirichlet Meta-Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel Bayesian uncertainty learning approach using the Dirichlet meta-model, which is effective and computationally efficient. |
Maohao Shen; Yuheng Bu; Prasanna Sattigeri; Soumya Ghosh; Subhro Das; Gregory Wornell; |
| 1099 | Neighbor Contrastive Learning on Learnable Graph Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an end-to-end automatic GCL method, named NCLA to apply neighbor contrastive learning on learnable graph augmentation. |
Xiao Shen; Dewang Sun; Shirui Pan; Xi Zhou; Laurence T. Yang; |
| 1100 | ProxyBO: Accelerating Neural Architecture Search Via Bayesian Optimization with Zero-Cost Proxies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the limitations, we present ProxyBO, an efficient Bayesian optimization (BO) framework that utilizes the zero-cost proxies to accelerate neural architecture search. |
Yu Shen; Yang Li; Jian Zheng; Wentao Zhang; Peng Yao; Jixiang Li; Sen Yang; Ji Liu; Bin Cui; |
| 1101 | Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new self-supervised paradigm on point cloud sequence understanding. |
Xiaoxiao Sheng; Zhiqiang Shen; Gang Xiao; |
| 1102 | Fixed-Weight Difference Target Propagation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Learning of the feedforward and feedback networks is sufficient to make TP methods capable of training, but is having these layer-wise autoencoders a necessary condition for TP to work? We answer this question by presenting Fixed-Weight Difference Target Propagation (FW-DTP) that keeps the feedback weights constant during training. |
Tatsukichi Shibuya; Nakamasa Inoue; Rei Kawakami; Ikuro Sato; |
| 1103 | Concurrent Multi-Label Prediction in Event Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many real-world datasets such as e-commerce transactions and electronic health records often involve events where multiple event types co-occur, e.g. multiple items purchased or multiple diseases diagnosed simultaneously. In this paper, we tackle multi-label prediction in such a problem setting, and propose a novel Transformer-based Conditional Mixture of Bernoulli Network (TCMBN) that leverages neural density estimation to capture complex temporal dependence as well as probabilistic dependence between concurrent event types. |
Xiao Shou; Tian Gao; Dharmashankar Subramanian; Debarun Bhattacharjya; Kristin P. Bennett; |
| 1104 | A Generalized Unbiased Risk Estimator for Learning with Augmented Classes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for LAC. |
Senlin Shu; Shuo He; Haobo Wang; Hongxin Wei; Tao Xiang; Lei Feng; |
| 1105 | Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, which focuses on the NLI task, we introduce the methodology of Faithfulness-through-Counterfactuals, which first generates a counterfactual hypothesis based on the logical predicates expressed in the explanation, and then evaluates if the model’s prediction on the counterfactual is consistent with that expressed logic (i.e. if the new formula is \textit{logically satisfiable}). In contrast to existing approaches, this does not require any explanations for training a separate verification model. |
Suzanna Sia; Anton Belyy; Amjad Almahairi; Madian Khabsa; Luke Zettlemoyer; Lambert Mathias; |
| 1106 | SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these ef- forts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum com- puters. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum sim- ilarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms. |
Daniel Silver; Tirthak Patel; Aditya Ranjan; Harshitta Gandhi; William Cutler; Devesh Tiwari; |
| 1107 | Adaptive Mixing of Auxiliary Losses in Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our proposal, AMAL, uses a bi-level optimization criterion on validation data to learn optimal mixing weights, at an instance-level, over the training data. We describe a meta-learning approach towards solving this bi-level objective, and show how it can be applied to different scenarios in supervised learning. |
Durga Sivasubramanian; Ayush Maheshwari; Prathosh AP; Pradeep Shenoy; Ganesh Ramakrishnan; |
| 1108 | Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In fact, we show that the conventional random user selection strategies in FL lead to leaking users’ individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. |
Jinhyun So; Ramy E. Ali; Başak Güler; Jiantao Jiao; A. Salman Avestimehr; |
| 1109 | Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In seeking a faster, high-performing model, we present a new method that leverages multiple manifolds as a mixture of backward (e.g., inverse) models in a forward-backward model architecture. |
Gregory P. Spell; Simiao Ren; Leslie M. Collins; Jordan M. Malof; |
| 1110 | Sharing Pattern Submodels for Prediction with Missing Values Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an alternative approach, called sharing pattern submodels (SPSM), which i) makes predictions that are robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels, and iii) has a short description, enabling improved interpretability. |
Lena Stempfle; Ashkan Panahi; Fredrik D. Johansson; |
| 1111 | Scalable Optimal Multiway-Split Decision Trees with Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel path-based MIP formulation where the number of decision variables is independent of dataset size. |
Shivaram Subramanian; Wei Sun; |
| 1112 | REMIT: Reinforced Multi-Interest Transfer for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework called reinforced multi-interest transfer for CDR (REMIT). |
Caiqi Sun; Jiewei Gu; Binbin Hu; Xin Dong; Hai Li; Lei Cheng; Linjian Mo; |
| 1113 | Cooperative and Adversarial Learning: Co-enhancing Discriminability and Transferability in Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Discriminability and transferability are two goals of feature learning for domain adaptation (DA), as we aim to find the transferable features from the source domain that are helpful for discriminating the class label in the target domain. |
Hui Sun; Zheng Xie; Xin-Ye Li; Ming Li; |
| 1114 | Fair-CDA: Continuous and Directional Augmentation for Group Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Fair-CDA, a fine-grained data augmentation strategy for imposing fairness constraints. |
Rui Sun; Fengwei Zhou; Zhenhua Dong; Chuanlong Xie; Lanqing Hong; Jiawei Li; Rui Zhang; Zhen Li; Zhenguo Li; |
| 1115 | Neural Spline Search for Quantile Probabilistic Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a non-parametric and data-driven approach, Neural Spline Search (NSS), to represent the observed data distribution without parametric assumptions. |
Ruoxi Sun; Chun-Liang Li; Sercan Ö. Arik; Michael W. Dusenberry; Chen-Yu Lee; Tomas Pfister; |
| 1116 | Domain Adaptation with Adversarial Training on Penultimate Activations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore adversarial training on penultimate activations, i.e., input features of the final linear classification layer. |
Tao Sun; Cheng Lu; Haibin Ling; |
| 1117 | Fast Convergence in Learning Two-Layer Neural Networks with Separable Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we go beyond linear models by studying normalized GD on two-layer neural nets. |
Hossein Taheri; Christos Thrampoulidis; |
| 1118 | Federated Learning on Non-IID Graphs Via Structural Knowledge Sharing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: From real-world graph datasets, we observe that some structural properties are shared by various domains, presenting great potential for sharing structural knowledge in FGL. Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks. |
Yue Tan; Yixin Liu; Guodong Long; Jing Jiang; Qinghua Lu; Chengqi Zhang; |
| 1119 | Metric Multi-View Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, existing methods usually adopt the similarity metric by an ad hoc approach, which largely simplifies the relationship among real-world data and results in an inaccurate output. To address these issues, we propose to seamlessly integrates metric learning and graph learning for multi-view clustering. |
Yuze Tan; Yixi Liu; Hongjie Wu; Jiancheng Lv; Shudong Huang; |
| 1120 | DE-net: Dynamic Text-Guided Image Editing Adversarial Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (e.g., color changing, texture changing, content adding and removing), which results in over-editing or insufficient editing. Second, they do not clearly distinguish between text-required and text-irrelevant parts, which leads to inaccurate editing. To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) that composes different editing modules dynamically for various editing requirements. (ii) a Composition Predictor (Comp-Pred), which predicts the composition weights for DEBlock according to the inference on target texts and source images. (iii) a Dynamic text-adaptive Convolution Block (DCBlock) that queries source image features to distinguish text-required parts and text-irrelevant parts. |
Ming Tao; Bing-Kun Bao; Hao Tang; Fei Wu; Longhui Wei; Qi Tian; |
| 1121 | Knowledge Amalgamation for Multi-Label Classification Via Label Dependency Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we acquire vast amounts of new labeled data and retrain a new model from scratch. Here, we propose combining the knowledge from multiple pre-trained models (teachers) to train a new student model that covers the union of the labels predicted by this set of teachers. |
Jidapa Thadajarassiri; Thomas Hartvigsen; Walter Gerych; Xiangnan Kong; Elke Rundensteiner; |
| 1122 | Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We prove that under some mild conditions, the proposed PuriGANs are guaranteed to converge to the distribution of desired instances. |
Bowen Tian; Qinliang Su; Jianxing Yu; |
| 1123 | Heterogeneous Graph Masked Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, we first develop metapath masking and adaptive attribute masking with dynamic mask rate to enable effective and stable learning on heterogeneous graphs. We then design several training strategies including metapath-based edge reconstruction to adopt complex structural information, target attribute restoration to incorporate various node attributes, and positional feature prediction to encode node positional information. |
Yijun Tian; Kaiwen Dong; Chunhui Zhang; Chuxu Zhang; Nitesh V. Chawla; |
| 1124 | Unbalanced CO-optimal Transport Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While this approach leads to better alignments and generalizes both OT and Gromov-Wasserstein distances, we provide a theoretical result showing that it is sensitive to outliers that are omnipresent in real-world data. This prompts us to propose unbalanced COOT for which we provably show its robustness to noise in the compared datasets. |
Quang Huy Tran; Hicham Janati; Nicolas Courty; Rémi Flamary; Ievgen Redko; Pinar Demetci; Ritambhara Singh; |
| 1125 | Linear Regularizers Enforce The Strict Saddle Property Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: First-order methods such as gradient descent may converge to non-strict saddle points of such functions, and there do not currently exist any first-order methods that reliably escape non-strict saddle points. To address this need, we demonstrate that regularizing a function with a linear term enforces the strict saddle property, and we provide justification for only regularizing locally, i.e., when the norm of the gradient falls below a certain threshold. |
Matthew Ubl; Matthew Hale; Kasra Yazdani; |
| 1126 | Policy-Adaptive Estimator Selection for Off-Policy Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, identifying the most accurate estimator using only the logged data is quite challenging because the ground-truth estimation accuracy of estimators is generally unavailable. This paper thus studies this challenging problem of estimator selection for OPE for the first time. |
Takuma Udagawa; Haruka Kiyohara; Yusuke Narita; Yuta Saito; Kei Tateno; |
| 1127 | A Fair Generative Model Using LeCam Divergence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on practically-relevant scenarios wherein demographic labels are not available and therefore the design of a fair generative model is non-straightforward. In this paper, we propose an optimization framework that regulates the unfairness under such practical settings via one statistical measure, LeCam (LC)-divergence. |
Soobin Um; Changho Suh; |
| 1128 | Efficient Distribution Similarity Identification in Clustered Federated Learning Via Principal Angles Between Client Data Subspaces Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. |
Saeed Vahidian; Mahdi Morafah; Weijia Wang; Vyacheslav Kungurtsev; Chen Chen; Mubarak Shah; Bill Lin; |
| 1129 | Training-Time Attacks Against K-nearest Neighbors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove that computing an optimal training-time (a.k.a. poisoning) attack against kNN classification is NP-Hard, even when k = 1 and the attacker can insert only a single data point. We provide an anytime algorithm to perform such an attack, and a greedy algorithm for general k and attacker budget. |
Ara Vartanian; Will Rosenbaum; Scott Alfeld; |
| 1130 | Machines of Finite Depth: Towards A Formalization of Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction—machines of finite depth. |
Pietro Vertechi; Mattia G. Bergomi; |
| 1131 | Kalman Bayesian Neural Networks for Closed-Form Online Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach for BNN learning via closed-form Bayesian inference. |
Philipp Wagner; Xinyang Wu; Marco F. Huber; |
| 1132 | Auto-Weighted Multi-View Clustering for Large-Scale Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. |
Xinhang Wan; Xinwang Liu; Jiyuan Liu; Siwei Wang; Yi Wen; Weixuan Liang; En Zhu; Zhe Liu; Lu Zhou; |
| 1133 | Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon T suffer from the regret of at least the square root of T. In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with the order of the polylog T regrets, exponentially improving the dependence in terms of T. To the best of our knowledge, this is the first provable quantum speedup for regrets of bandit problems and in general exploitation in reinforcement learning. |
Zongqi Wan; Zhijie Zhang; Tongyang Li; Jialin Zhang; Xiaoming Sun; |
| 1134 | FedABC: Targeting Fair Competition in Personalized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. |
Dui Wang; Li Shen; Yong Luo; Han Hu; Kehua Su; Yonggang Wen; Dacheng Tao; |
| 1135 | Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Herein, we propose a Spearman rank correlation-based screening procedure for ultrahigh-dimensional data with censored response cases. |
Hongni Wang; Jingxin Yan; Xiaodong Yan; |
| 1136 | Stability-Based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, to the best of our knowledge, the learning theory foundation of PPL has not been touched in the existing works. In this paper, we try to fill this theoretical gap by investigating the generalization properties of PPL. |
Jiahuan Wang; Jun Chen; Hong Chen; Bin Gu; Weifu Li; Xin Tang; |
| 1137 | Effective Continual Learning for Text Classification with Lightweight Snapshots Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this requires storing all past models, which is very space-consuming for large models, e.g. BERT, thus impractical in real-world applications. To tackle this issue, we propose to construct snapshots of seen tasks whose key knowledge is captured in lightweight adapters. |
Jue Wang; Dajie Dong; Lidan Shou; Ke Chen; Gang Chen; |
| 1138 | Optimistic Whittle Index Policy: Online Learning for Restless Bandits Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To plan in RMAB settings with unknown transitions, we propose the first online learning algorithm based on the Whittle index policy, using an upper confidence bound (UCB) approach to learn transition dynamics. |
Kai Wang; Lily Xu; Aparna Taneja; Milind Tambe; |
| 1139 | AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-Series Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: AEC-GAN contains two main innovations: (1) We develop an error correction module to mitigate the bias. In the training phase, we adversarially perturb the realistic time-series data and then optimize this module to reconstruct the original data. In the generation phase, this module can act as an efficient regulator to detect and mitigate the bias. (2) We propose an augmentation method to facilitate GAN’s training by introducing adversarial examples. |
Lei Wang; Liang Zeng; Jian Li; |
| 1140 | The Implicit Regularization of Momentum Gradient Descent in Overparametrized Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) in the continuous-time view, so-called momentum gradient flow (MGF). |
Li Wang; Zhiguo Fu; Yingcong Zhou; Zili Yan; |
| 1141 | Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. |
Mingyang Wang; Zhenshan Bing; Xiangtong Yao; Shuai Wang; Huang Kai; Hang Su; Chenguang Yang; Alois Knoll; |
| 1142 | Hierarchical Contrastive Learning for Temporal Point Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a novel hierarchical contrastive (HCL) learning method for temporal point processes, which provides a new regularizer of MLE. |
Qingmei Wang; Minjie Cheng; Shen Yuan; Hongteng Xu; |
| 1143 | Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. |
Shuai Wang; Yanqing Xu; Zhiguo Wang; Tsung-Hui Chang; Tony Q. S. Quek; Defeng Sun; |
| 1144 | State-Conditioned Adversarial Subgoal Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel HRL approach for mitigating the non-stationarity by adversarially enforcing the high-level policy to generate subgoals compatible with the current instantiation of the low-level policy. |
Vivienne Huiling Wang; Joni Pajarinen; Tinghuai Wang; Joni-Kristian Kämäräinen; |
| 1145 | Deep Attentive Model for Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this solution, a student and related exercises are mapped into feature vectors based on the student’s performance at the current time step, however, it does not consider the impact of historical behavior sequences, and the relationships between historical sequences and students. In this paper, we develop DAKTN, a novel model which assimilates the historical sequences to tackle this challenge for better knowledge tracing. |
Xinping Wang; Liangyu Chen; Min Zhang; |
| 1146 | Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle the challenge, we present a novel Correspondence-free Domain Alignment (CoDA) method to effectively eliminate the cross-domain gap through In-domain Self-matching Supervision (ISS) and Cross-domain Classifier Alignment (CCA). |
Xu Wang; Dezhong Peng; Ming Yan; Peng Hu; |
| 1147 | Isolation and Impartial Aggregation: A Paradigm of Incremental Learning Without Interference Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To avoid obvious stage learning bottlenecks, we propose a new incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task at each stage, without interference from others. |
Yabin Wang; Zhiheng Ma; Zhiwu Huang; Yaowei Wang; Zhou Su; Xiaopeng Hong; |
| 1148 | Robust Self-Supervised Multi-Instance Learning with Structure Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make the first attempt to propose a robust Self-supervised Multi-Instance LEarning architecture with Structure awareness (SMILEs) that learns unsupervised bag representation. |
Yejiang Wang; Yuhai Zhao; Zhengkui Wang; Meixia Wang; |
| 1149 | Distributed Projection-Free Online Learning for Smooth and Convex Losses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new distributed online projection-free method with a tighter regret bound of O(T^{2/3}) for smooth and convex losses. |
Yibo Wang; Yuanyu Wan; Shimao Zhang; Lijun Zhang; |
| 1150 | USER: Unsupervised Structural Entropy-Based Robust Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose USER, an unsupervised and robust version of GNN based on structural entropy, to alleviate the interference of graph perturbations and learn appropriate representations of nodes without label information. |
Yifei Wang; Yupan Wang; Zeyu Zhang; Song Yang; Kaiqi Zhao; Jiamou Liu; |
| 1151 | AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present AutoNF, the first automated NF architectural optimization framework. |
Yu Wang; Ján Drgoňa; Jiaxin Zhang; Karthik Somayaji Nanjangud Suryanarayana; Malachi Schram; Frank Liu; Peng Li; |
| 1152 | SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA to reduce the domain discrepancy at both the local and global sensor levels. |
Yucheng Wang; Yuecong Xu; Jianfei Yang; Zhenghua Chen; Min Wu; Xiaoli Li; Lihua Xie; |
| 1153 | Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A positive-unlabeled adversarial imitation learning algorithm is developed to dynamically sample expert demonstrations that can well match the trajectories from the constantly optimized agent policy. |
Yunke Wang; Bo Du; Chang Xu; |
| 1154 | FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: There are significant challenges to achieve both stable and bias-free training un- der arbitrary client availability. To address these challenges, we propose a framework named Federated Graph-based Sam- pling (FEDGS), to stabilize the global model update and mitigate the long-term bias given arbitrary client availabil- ity simultaneously. |
Zheng Wang; Xiaoliang Fan; Jianzhong Qi; Haibing Jin; Peizhen Yang; Siqi Shen; Cheng Wang; |
| 1155 | Efficient Exploration in Resource-Restricted Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In tasks with non-replenishable resources, we observe that popular RL methods such as soft actor critic suffer from poor sample efficiency. |
Zhihai Wang; Taoxing Pan; Qi Zhou; Jie Wang; |
| 1156 | Efficient Explorative Key-Term Selection Strategies for Conversational Contextual Bandits Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We prove tighter regret upper bounds of our proposed algorithms. |
Zhiyong Wang; Xutong Liu; Shuai Li; John C. S. Lui; |
| 1157 | Code-Aware Cross-Program Transfer Hyperparameter Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes CaTHPO, a code-aware cross-program transfer hyperparameter optimization framework, which makes three improvements. |
Zijia Wang; Xiangyu He; Kehan Chen; Chen Lin; Jinsong Su; |
| 1158 | Predictive Multiplicity in Probabilistic Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a framework for measuring predictive multiplicity in probabilistic classification (predicting the probability of a positive outcome). |
Jamelle Watson-Daniels; David C. Parkes; Berk Ustun; |
| 1159 | Feature Distribution Fitting with Direction-Driven Weighting for Few-Shot Images Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a direction-driven weighting method to make the feature distributions of few-shot classes precisely fit the ground-truth distributions. |
Xin Wei; Wei Du; Huan Wan; Weidong Min; |
| 1160 | Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, we propose to reconstruct the source label and model it as a Group Instrumental Variable (GIV) to implement IV-based Regression for treatment effect estimation. In this paper, we conceptualize this line of thought and develop a unified framework (Meta-EM) to (1) map the raw data into a representation space to construct Linear Mixed Models for the assigned treatment variable; (2) estimate the distribution differences and model the GIV for the different treatment assignment mechanisms; and (3) adopt an alternating training strategy to iteratively optimize the representations and the joint distribution to model GIV for IV regression. |
Anpeng Wu; Kun Kuang; Ruoxuan Xiong; Minqin Zhu; Yuxuan Liu; Bo Li; Furui Liu; Zhihua Wang; Fei Wu; |
| 1161 | Towards In-Distribution Compatible Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on our new understandings, we propose a new out-of-distribution detection method by adapting both the top-design of deep models and the loss function. |
Boxi Wu; Jie Jiang; Haidong Ren; Zifan Du; Wenxiao Wang; Zhifeng Li; Deng Cai; Xiaofei He; Binbin Lin; Wei Liu; |
| 1162 | Non-IID Transfer Learning on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. |
Jun Wu; Jingrui He; Elizabeth Ainsworth; |
| 1163 | Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting It Into MLPs: An Effective GNN-to-MLP Distillation Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an efficient Full-Frequency GNN-to-MLP (FF-G2M) distillation framework, which extracts both low-frequency and high-frequency knowledge from GNNs and injects it into MLPs. |
Lirong Wu; Haitao Lin; Yufei Huang; Tianyu Fan; Stan Z. Li; |
| 1164 | Symphony in The Latent Space: Provably Integrating High-Dimensional Techniques with Non-linear Machine Learning Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework, which we dub as the additive influence model. |
Qiong Wu; Jian Li; Zhenming Liu; Yanhua Li; Mihai Cucuringu; |
| 1165 | Decentralized Riemannian Algorithm for Nonconvex Minimax Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the distributed nonconvex-strongly-concave minimax optimization problem over the Stiefel manifold and propose both deterministic and stochastic minimax methods. |
Xidong Wu; Zhengmian Hu; Heng Huang; |
| 1166 | Faster Adaptive Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on the momentum-based variance reduced technique in cross-silo FL. |
Xidong Wu; Feihu Huang; Zhengmian Hu; Heng Huang; |
| 1167 | Practical Markov Boundary Learning Without Strong Assumptions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper takes further steps toward opening the door to real-world applications for MB. |
Xingyu Wu; Bingbing Jiang; Tianhao Wu; Huanhuan Chen; |
| 1168 | FedNP: Towards Non-IID Federated Learning Via Federated Neural Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional federated learning (FL) algorithms, such as FedAvg, fail to handle non-i.i.d data because they learn a global model by simply averaging biased local models that are trained on non-i.i.d local data, therefore failing to model the global data distribution. In this paper, we present a novel Bayesian FL algorithm that successfully handles such a non-i.i.d FL setting by enhancing the local training task with an auxiliary task that explicitly estimates the global data distribution. |
Xueyang Wu; Hengguan Huang; Youlong Ding; Hao Wang; Ye Wang; Qian Xu; |
| 1169 | MetaZSCIL: A Meta-Learning Approach for Generalized Zero-Shot Class Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, in this paper, we propose a more practical and challenging setting named Generalized Zero-Shot Class Incremental Learning (CI-GZSL). |
Yanan Wu; Tengfei Liang; Songhe Feng; Yi Jin; Gengyu Lyu; Haojun Fei; Yang Wang; |
| 1170 | Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The benefits of AWP, and more generally the connections between flatness and generalization, have been extensively studied for i.i.d. data such as images. In this paper, we extensively study this phenomenon for graph data. |
Yihan Wu; Aleksandar Bojchevski; Heng Huang; |
| 1171 | Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset. We study reward-poisoning attacks in this setting where an exogenous attacker modifies the rewards in the dataset before the agents see the dataset. |
Young Wu; Jeremy McMahan; Xiaojin Zhu; Qiaomin Xie; |
| 1172 | Models As Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose Models as AGents (MAG), a multi-agent model optimization framework that reversely treats the local models as multi-step decision making agents and the current policies as the dynamics during the model rollout process. |
Zifan Wu; Chao Yu; Chen Chen; Jianye Hao; Hankz Hankui Zhuo; |
| 1173 | Differentially Private Learning with Per-Sample Adaptive Clipping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To achieve DP in the learning process, existing algorithms typically limit the magnitude of gradients with a constant clipping, which requires carefully tuned due to its significant impact on model performance. As a solution to this issue, latest works NSGD and Auto-S innovatively propose to use normalization instead of clipping to avoid hyperparameter tuning. |
Tianyu Xia; Shuheng Shen; Su Yao; Xinyi Fu; Ke Xu; Xiaolong Xu; Xing Fu; |
| 1174 | Zero-Cost Operation Scoring in Differentiable Architecture Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We formalize and analyze a fundamental component of dif- ferentiable neural architecture search (NAS): local “opera- tion scoring” at each operation choice. |
Lichuan Xiang; Lukasz Dudziak; Mohamed S. Abdelfattah; Thomas Chau; Nicholas D. Lane; Hongkai Wen; |
| 1175 | HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, all of the existing works are not designed in a hardware-aware way, limiting the practical performance of the compressed models on real-world hardware platforms. To address these challenges, in this paper we propose HALOC, a hardware-aware automatic low-rank compression framework. |
Jinqi Xiao; Chengming Zhang; Yu Gong; Miao Yin; Yang Sui; Lizhi Xiang; Dingwen Tao; Bo Yuan; |
| 1176 | Bayesian Federated Neural Matching That Completes Full Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But according to our theoretical analysis, the optimization iteration in PFNM omits global information from existing. In this study, we propose a novel approach that overcomes this flaw by introducing a Kullback-Leibler divergence penalty at each iteration. |
Peng Xiao; Samuel Cheng; |
| 1177 | CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop the first practical algorithm named CDMA for general minimax problems in the cross-device FL setting. |
Jiahao Xie; Chao Zhang; Zebang Shen; Weijie Liu; Hui Qian; |
| 1178 | Towards Optimal Randomized Strategies in Adversarial Example Game Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in a fully randomized setting where both the defender and the attacker can use randomized strategies, there are no efficient algorithm for finding such an optimal strategy. To fill the gap, we propose the first algorithm of its kind, called FRAT, which models the problem with a new infinite-dimensional continuous-time flow on probability distribution spaces. |
Jiahao Xie; Chao Zhang; Weijie Liu; Wensong Bai; Hui Qian; |
| 1179 | A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-Run Langevin Flow for Approximate Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to jointly learn the latent space normalizing flow prior model and the top-down generator model by a Markov chain Monte Carlo (MCMC)-based maximum likelihood algorithm, where a short-run Langevin sampling from the intractable posterior distribution is performed to infer the latent variables for each observed example, so that the parameters of the normalizing flow prior and the generator can be updated with the inferred latent variables. |
Jianwen Xie; Yaxuan Zhu; Yifei Xu; Dingcheng Li; Ping Li; |
| 1180 | Semi-supervised Learning with Support Isolation By Small-Paced Self-Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address a special scenario of semi-supervised learning, where the label missing is caused by a preceding filtering mechanism, i.e., an instance can enter a subsequent process in which its label is revealed if and only if it passes the filtering mechanism. |
Zheng Xie; Hui Sun; Ming Li; |
| 1181 | On The Connection Between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates this possibility by exploring the similarity between the IRM and AT objectives. Inspired by this connection, we propose Domain-wise Adversarial Training (DAT), an AT-inspired method for alleviating distribution shift by domain-specific perturbations. |
Shiji Xin; Yifei Wang; Jingtong Su; Yisen Wang; |
| 1182 | Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a multi-player multi-armed walking bandits model, aiming to address aforementioned modeling issues. |
Guojun Xiong; Jian Li; |
| 1183 | Federated Generative Model on Multi-Source Heterogeneous Data in IoT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. |
Zuobin Xiong; Wei Li; Zhipeng Cai; |
| 1184 | Contrastive Open Set Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study how to improve the OSR performance of deep neural networks from the perspective of representation learning. |
Baile Xu; Furao Shen; Jian Zhao; |
| 1185 | Progressive Deep Multi-View Comprehensive Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of synergistic fusion methods inevitably degenerate or even fail when partial views are missing in real-world applications; the aligned based fusion methods usually cannot fully exploit the complementarity of multi-view data. To eliminate all these drawbacks, in this work we present a Progressive Deep Multi-view Fusion (PDMF) method. |
Cai Xu; Wei Zhao; Jinglong Zhao; Ziyu Guan; Yaming Yang; Long Chen; Xiangyu Song; |
| 1186 | A Survey on Model Compression and Acceleration for Pretrained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we focus on the inference stage and review the current state of model compression and acceleration for pretrained language models, including benchmarks, metrics and methodology. |
Canwen Xu; Julian McAuley; |
| 1187 | GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find BCN methods perform weakly on this task for not making full use of graph information. Therefore, we propose GraphPrompt, a prompt-based learning approach that creates prompt templates according to the graphs. |
Hanwen Xu; Jiayou Zhang; Zhirui Wang; Shizhuo Zhang; Megh Bhalerao; Yucong Liu; Dawei Zhu; Sheng Wang; |
| 1188 | Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to train diverse policies under the regularization of the behavior patterns. |
Kang Xu; Yan Ma; Bingsheng Wei; Wei Li; |
| 1189 | Efficient Top-K Feature Selection Using Coordinate Descent Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we focus on the l2,0-norm based feature selection, which is effective for exact top-k feature selection but challenging to optimize. |
Lei Xu; Rong Wang; Feiping Nie; Xuelong Li; |
| 1190 | Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main reason is that the augmented documents for one label may inevitably influence the other co-occurring labels and further exaggerate the long-tailed problem. To mitigate this issue, we propose a new pair-level augmentation framework for MLTC, called Label-Specific Feature Augmentation (LSFA), which merely augments positive feature-label pairs for the tail-labels. |
Pengyu Xu; Lin Xiao; Bing Liu; Sijin Lu; Liping Jing; Jian Yu; |
| 1191 | Neighborhood-Regularized Self-Training for Learning with Few Labels Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. |
Ran Xu; Yue Yu; Hejie Cui; Xuan Kan; Yanqiao Zhu; Joyce Ho; Chao Zhang; Carl Yang; |
| 1192 | Resilient Binary Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs’ training. |
Sheng Xu; Yanjing Li; Teli Ma; Mingbao Lin; Hao Dong; Baochang Zhang; Peng Gao; Jinhu Lu; |
| 1193 | Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a transfer-learning aided DeepONet to enhance the stability. |
Wuzhe Xu; Yulong Lu; Li Wang; |
| 1194 | BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose BridgeTower, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross-modal encoder. |
Xiao Xu; Chenfei Wu; Shachar Rosenman; Vasudev Lal; Wanxiang Che; Nan Duan; |
| 1195 | USDNL: Uncertainty-Based Single Dropout in Noisy Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adopt a perspective to connect label noise with epistemic uncertainty. |
Yuanzhuo Xu; Xiaoguang Niu; Jie Yang; Steve Drew; Jiayu Zhou; Ruizhi Chen; |
| 1196 | Trusted Fine-Grained Image Classification Through Hierarchical Evidence Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adopt the evidence theory to measure uncertainty and confidence in hierarchical classification process and propose a trusted FGIC method through fusing multilayer classification evidence. |
Zhikang Xu; Xiaodong Yue; Ying Lv; Wei Liu; Zihao Li; |
| 1197 | Disentangled Representation for Causal Mediation Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work aims to circumvent the sequential ignorability assumption and applies the piecemeal deconfounding assumption as an alternative. |
Ziqi Xu; Debo Cheng; Jiuyong Li; Jixue Liu; Lin Liu; Ke Wang; |
| 1198 | Global Concept-Based Interpretability for Graph Neural Networks Via Neuron Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel approach for producing global explanations for GNNs using neuron-level concepts to enable practitioners to have a high-level view of the model. |
Han Xuanyuan; Pietro Barbiero; Dobrik Georgiev; Lucie Charlotte Magister; Pietro Liò; |
| 1199 | Fast and Accurate Binary Neural Networks Based on Depth-Width Reshaping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We start from the factors of the performance degradation of BNNs and analyze the problems of directly using full-precision network backbones for BNNs: for a given computational budget, the backbone of a BNN may need to be shallower and wider compared to the backbone of a full-precision network. |
Ping Xue; Yang Lu; Jingfei Chang; Xing Wei; Zhen Wei; |
| 1200 | Learning The Finer Things: Bayesian Structure Learning at The Instantiation Level Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While we commonly measure their performance through cross validation and accuracy metrics, how should these algorithms cope in domains that are extremely under-determined where accuracy is always unsatisfactory? We present a novel probabilistic graphical model structure learning approach that can learn, generalize and explain in these elusive domains by operating at the random variable instantiation level. |
Chase Yakaboski; Eugene Santos; Jr; |
| 1201 | Semidefinite Programming Versus Burer-Monteiro Factorization for Matrix Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The existing theoretical guarantees for the success of these methods have led to similar conservative conditions, which may wrongly imply that these methods have comparable performances. In this paper, we shed light on some major differences between these two methods. |
Baturalp Yalçın; Ziye Ma; Javad Lavaei; Somayeh Sojoudi; |
| 1202 | DeFL: Defending Against Model Poisoning Attacks in Federated Learning Via Critical Learning Periods Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by recent findings on critical learning periods (CLP) in DNNs, where small gradient errors have irrecoverable impact on the final model accuracy, we propose a new defense, called a CLP-aware defense against poisoning of FL (DeFL). |
Gang Yan; Hao Wang; Xu Yuan; Jian Li; |
| 1203 | T2G-FORMER: Organizing Tabular Features Into Relation Graphs Promotes Heterogeneous Feature Interaction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. |
Jiahuan Yan; Jintai Chen; Yixuan Wu; Danny Z. Chen; Jian Wu; |
| 1204 | Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work demonstrates the PAC-learnability of general reinforcement-learning objectives through sufficient conditions for PAC-learnability in two analysis settings. |
Cambridge Yang; Michael Littman; Michael Carbin; |
| 1205 | Reinforcement Causal Structure Learning on Order Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Reinforcement Causal Structure Learning on Order Graph (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. |
Dezhi Yang; Guoxian Yu; Jun Wang; Zhengtian Wu; Maozu Guo; |
| 1206 | AdaTask: A Task-Aware Adaptive Learning Rate Approach to Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter. |
Enneng Yang; Junwei Pan; Ximei Wang; Haibin Yu; Li Shen; Xihua Chen; Lei Xiao; Jie Jiang; Guibing Guo; |
| 1207 | WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing work either focuses on short sequence forecasting or makes predictions predominantly with time domain features, which is not effective at removing noises with irregular frequencies in MTS. Therefore, we propose WaveForM, an end-to-end graph enhanced Wavelet learning framework for long sequence FORecasting of MTS. |
Fuhao Yang; Xin Li; Min Wang; Hongyu Zang; Wei Pang; Mingzhong Wang; |
| 1208 | Layout Generation As Intermediate Action Sequence Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the generation process. |
Huiting Yang; Danqing Huang; Chin-Yew Lin; Shengfeng He; |
| 1209 | Learning-Assisted Algorithm Unrolling for Online Optimization with Budget Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new machine learning (ML) assisted unrolling approach, called LAAU (Learning-Assisted Algorithm Unrolling), which unrolls the agent’s online decision pipeline and leverages an ML model for updating the Lagrangian multiplier online. |
Jianyi Yang; Shaolei Ren; |
| 1210 | ADEPT: A DEbiasing PrompT Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose ADEPT, a method to debias PLMs using prompt tuning while maintaining the delicate balance between removing biases and ensuring representation ability. |
Ke Yang; Charles Yu; Yi R. Fung; Manling Li; Heng Ji; |
| 1211 | Generalized Semantic Segmentation By Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Different from them, we propose a Domain Projection and Contrastive Learning (DPCL) approach for generalized semantic segmentation, which includes two modules: Self-supervised Source Domain Projection (SSDP) and Multi-Level Contrastive Learning (MLCL). |
Liwei Yang; Xiang Gu; Jian Sun; |
| 1212 | CEM: Constrained Entropy Maximization for Task-Agnostic Safe Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a practical Constrained Entropy Maximization (CEM) algorithm to solve task-agnostic safe exploration problems, which naturally require a finite horizon and undiscounted constraints on safety costs. |
Qisong Yang; Matthijs T.J. Spaan; |
| 1213 | Understanding Representation Learnability of Nonlinear Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike previous linear setting work that depends on closed-form solutions, we use the gradient descent algorithm to train a 1-layer nonlinear SSL model with a certain initialization region and prove that the model converges to a local minimum. |
Ruofeng Yang; Xiangyuan Li; Bo Jiang; Shuai Li; |
| 1214 | Simple and Efficient Heterogeneous Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. In this paper, we conduct an in-depth and detailed study of these mechanisms and propose the Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). |
Xiaocheng Yang; Mingyu Yan; Shirui Pan; Xiaochun Ye; Dongrui Fan; |
| 1215 | T-distributed Spherical Feature Representation for Imbalanced Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The tail categories sharing the rest of the narrow eigenspace are too crowded together to accurately extract features. Above these issues, we propose a novel T-distributed spherical metric for equalized eigenspace in the imbalanced classification, which has the following innovations: 1) We design the T-distributed spherical metric, which has the characteristics of high kurtosis. |
Xiaoyu Yang; Yufei Chen; Xiaodong Yue; Shaoxun Xu; Chao Ma; |
| 1216 | Cluster-Guided Contrastive Graph Clustering Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. |
Xihong Yang; Yue Liu; Sihang Zhou; Siwei Wang; Wenxuan Tu; Qun Zheng; Xinwang Liu; Liming Fang; En Zhu; |
| 1217 | Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we give a quantitative characterization of the performance of offline hierarchical learning and highlight the importance of learning lossless primitives. |
Yiqin Yang; Hao Hu; Wenzhe Li; Siyuan Li; Jun Yang; Qianchuan Zhao; Chongjie Zhang; |
| 1218 | Prototypical Partial Optimal Transport for Universal Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the problem from the point of view of distribution matching which we only need to align two distributions partially. |
Yucheng Yang; Xiang Gu; Jian Sun; |
| 1219 | DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, physical limitations, budget restrictions, and many other factors usually impose constraints on a multi-agent system (MAS), which cannot be handled by traditional MARL frameworks. Specifically, this paper focuses on constrained MASes where agents work cooperatively to maximize the expected team-average return under various constraints on expected team-average costs, and develops a constrained cooperative MARL framework, named DeCOM, for such MASes. |
Zhaoxing Yang; Haiming Jin; Rong Ding; Haoyi You; Guiyun Fan; Xinbing Wang; Chenghu Zhou; |
| 1220 | Purifier: Defending Data Inference Attacks Via Transforming Confidence Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. |
Ziqi Yang; Lijin Wang; Da Yang; Jie Wan; Ziming Zhao; Ee-Chien Chang; Fan Zhang; Kui Ren; |
| 1221 | I-Code: An Integrative and Composable Multimodal Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present i-Code, a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. |
Ziyi Yang; Yuwei Fang; Chenguang Zhu; Reid Pryzant; DongDong Chen; Yu Shi; Yichong Xu; Yao Qian; Mei Gao; Yi-Ling Chen; Liyang Lu; Yujia Xie; Robert Gmyr; Noel Codella; Naoyuki Kanda; Bin Xiao; Lu Yuan; Takuya Yoshioka; Michael Zeng; Xuedong Huang; |
| 1222 | Learning Dynamic Latent Spaces for Lifelong Generative Modelling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a new model, namely the Online Recursive Variational Autoencoder (ORVAE). |
Fei Ye; Adrian G. Bors; |
| 1223 | Lifelong Compression Mixture Model Via Knowledge Relationship Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to simultaneously address network forgetting and model size optimization by developing the Lifelong Compression Mixture Model (LGMM) equipped with the Maximum Mean Discrepancy (MMD) based expansion criterion for model expansion. |
Fei Ye; Adrian G. Bors; |
| 1224 | Lifelong Variational Autoencoder Via Online Adversarial Expansion Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on this analysis, we propose a novel expansion criterion that aims to preserve the information diversity among the VAE components, while ensuring that it acquires more knowledge with fewer parameters. |
Fei Ye; Adrian G. Bors; |
| 1225 | Continual Variational Autoencoder Via Continual Generative Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a new unsupervised continual learning framework consisting of two memory systems using Variational Autoencoders (VAEs). |
Fei Ye; Adrian G. Bors; |
| 1226 | Certifiable Out-of-Distribution Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a certifiable out-of-distribution generalization method that provides provable OoD generalization performance guarantees via a functional optimization framework leveraging random distributions and max-margin learning for each input datum. |
Nanyang Ye; Lin Zhu; Jia Wang; Zhaoyu Zeng; Jiayao Shao; Chensheng Peng; Bikang Pan; Kaican Li; Jun Zhu; |
| 1227 | Random Walk Conformer: Learning Graph Representation from Long and Short Range Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new GNN framework, namely Random Walk Conformer (RWC), to exploit global correlations and local patterns based on the random walk, which is a promising method to discover the graph structure. |
Pei-Kai Yeh; Hsi-Wen Chen; Ming-Syan Chen; |
| 1228 | Lottery Pools: Winning More By Interpolating Tickets Without Increasing Training or Inference Cost Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we first observe that directly averaging the weights of the adjacent learned subnetworks significantly boosts the performance of LTs. Encouraged by this observation, we further propose an alternative way to perform an ensemble” over the subnetworks identified by iterative magnitude pruning via a simple interpolating strategy. |
Lu Yin; Shiwei Liu; Meng Fang; Tianjin Huang; Vlado Menkovski; Mykola Pechenizkiy; |
| 1229 | GOHSP: A Unified Framework of Graph and Optimization-Based Heterogeneous Structured Pruning for Vision Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose GOHSP, a unified framework of Graph and Optimization-based Structured Pruning for ViT models. |
Miao Yin; Burak Uzkent; Yilin Shen; Hongxia Jin; Bo Yuan; |
| 1230 | Policy-Based Primal-Dual Methods for Convex Constrained Markov Decision Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a policy-based primal-dual algorithm that updates the primal variable via policy gradient ascent and updates the dual variable via projected sub-gradient descent. |
Donghao Ying; Mengzi Amy Guo; Yuhao Ding; Javad Lavaei; Zuo-Jun Shen; |
| 1231 | Priori Anchor Labels Supervised Scalable Multi-View Bipartite Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the unsupervised dynamic strategy usually cannot obtain the optimal anchors for MVC. The main reasons are that it does not consider the fairness of different views and lacks the priori supervised guidance. To completely solve these problems, we first propose the priori anchor graph regularization (PAGG) for scalable multi-view bipartite graph clustering, dubbed as SMGC method. |
Jiali You; Zhenwen Ren; Xiaojian You; Haoran Li; Yuancheng Yao; |
| 1232 | STARS: Spatial-Temporal Active Re-sampling for Label-Efficient Learning from Noisy Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a theoretical framework to formally analyze the impact of noisy annotations and show that systematically re-sampling guarantees to reduce the noise rate, which can lead to improved generalization capability. |
Dayou Yu; Weishi Shi; Qi Yu; |
| 1233 | Boosted Dynamic Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Treating training and testing inputs differently at the two phases will cause the mismatch between training and testing data distributions. To mitigate this problem, we formulate an EDNN as an additive model inspired by gradient boosting, and propose multiple training techniques to optimize the model effectively. |
Haichao Yu; Haoxiang Li; Gang Hua; Gao Huang; Humphrey Shi; |
| 1234 | Stable Learning Via Sparse Variable Independence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, with theoretical justification, we propose SVI (Sparse Variable Independence) for the covariate-shift generalization problem. |
Han Yu; Peng Cui; Yue He; Zheyan Shen; Yong Lin; Renzhe Xu; Xingxuan Zhang; |
| 1235 | Compressing Transformers: Features Are Low-Rank, But Weights Are Not! Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The proposed methods are successfully applied to the language modeling task in NLP, too. |
Hao Yu; Jianxin Wu; |
| 1236 | Offline Imitation Learning with Suboptimal Demonstrations Via Relaxed Distribution Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that such constraints based on exact distribution matching can be overly conservative and hamper policy learning, especially when the imperfect offline data is highly suboptimal. To resolve this issue, we present RelaxDICE, which employs an asymmetrically-relaxed f-divergence for explicit support regularization. |
Lantao Yu; Tianhe Yu; Jiaming Song; Willie Neiswanger; Stefano Ermon; |
| 1237 | High-Level Semantic Feature Matters Few-Shot Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose a novel task-specific semantic feature learning method (TSECS) for FS-UDA. |
Lei Yu; Wanqi Yang; Shengqi Huang; Lei Wang; Ming Yang; |
| 1238 | Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a coordinate descent method for minimizing a class of DC functions based on sequential nonconvex approximation. |
Ganzhao Yuan; |
| 1239 | CEMA – Cost-Efficient Machine-Assisted Document Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose CEMA, a method for deploying machine learning to assist humans in complex document annotation. |
Guowen Yuan; Ben Kao; Tien-Hsuan Wu; |
| 1240 | Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. |
Li Yuan; Yi Cai; Jin Wang; Qing Li; |
| 1241 | ODE-RSSM: Learning Stochastic Recurrent State Space Model from Irregularly Sampled Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired from the equivalent linear transformation on integration limits, we propose an efficient reparameterization method for solving batched ODEs with non-uniform time spans in parallel for efficiently training the ODE-RSSM with irregularly sampled sequences. |
Zhaolin Yuan; Xiaojuan Ban; Zixuan Zhang; Xiaorui Li; Hong-Ning Dai; |
| 1242 | Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, the learned representation by these visual methods may be good for recognition but not optimal for estimating state value and solving the decision problem. To address this issue, we propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. |
Yang Yue; Bingyi Kang; Zhongwen Xu; Gao Huang; Shuicheng Yan; |
| 1243 | Learning Conflict-Noticed Architecture for Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle multi-task problems with a large number of tasks, we propose a progressive extension of the CoNAL method. |
Zhixiong Yue; Yu Zhang; Jie Liang; |
| 1244 | Quantum Multi-Agent Meta Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. |
Won Joon Yun; Jihong Park; Joongheon Kim; |
| 1245 | Linking Sketch Patches By Learning Synonymous Proximity for Graphic Sketch Representation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an order-invariant, semantics-aware method for graphic sketch representations. |
Sicong Zang; Shikui Tu; Lei Xu; |
| 1246 | Neural Integro-Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we introduce the Neural IDE (NIDE), a novel deep learning framework based on the theory of IDEs where integral operators are learned using neural networks. |
Emanuele Zappala; Antonio H. de O. Fonseca; Andrew H. Moberly; Michael J. Higley; Chadi Abdallah; Jessica A. Cardin; David van Dijk; |
| 1247 | Leveraging Structure for Improved Classification of Grouped Biased Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The groups overlap in the feature space and consequently the input-output patterns are related across the groups. To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups, defined in terms of the group-agnostic feature space. |
Daniel Zeiberg; Shantanu Jain; Predrag Radivojac; |
| 1248 | Are Transformers Effective for Time Series Forecasting? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the permutation-invariant self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. |
Ailing Zeng; Muxi Chen; Lei Zhang; Qiang Xu; |
| 1249 | Substructure Aware Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues. |
DingYi Zeng; Wanlong Liu; Wenyu Chen; Li Zhou; Malu Zhang; Hong Qu; |
| 1250 | ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Indeed, we empirically find that most state-of-the-art GCL methods cannot obtain discriminative representations and exhibit poor performance on imbalanced node classification. Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels. |
Liang Zeng; Lanqing Li; Ziqi Gao; Peilin Zhao; Jian Li; |
| 1251 | Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, the system must be able to adapt to changes in ambient illuminations and continue to drive safely on the road. In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment. |
Qiuhao Zeng; Wei Wang; Fan Zhou; Charles Ling; Boyu Wang; |
| 1252 | Acceleration of Large Transformer Model Training By Sensitivity-Based Layer Dropping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue, in this paper we propose a novel method to accelerate training—Sensitivity-Based Layer Dropping (SBLD). |
Yujie Zeng; Wenlong He; Ihor Vasyltsov; Jiali Pang; Lin Chen; |
| 1253 | Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, as the main contribution of this paper, we provide a more efficient approach of interventional SHAP for tree-based models by precomputing statistics of the background data based on the tree structure. |
Artjom Zern; Klaus Broelemann; Gjergji Kasneci; |
| 1254 | Enhanced Tensor Low-Rank and Sparse Representation Recovery for Incomplete Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel Enhanced Tensor Low-rank and Sparse Representation Recovery (ETLSRR) method, which reformulates the IMVC problem as a joint incomplete similarity graphs learning and complete tensor representation recovery problem. |
Chao Zhang; Huaxiong Li; Wei Lv; Zizheng Huang; Yang Gao; Chunlin Chen; |
| 1255 | Denoising Multi-Similarity Formulation: A Self-Paced Curriculum-Driven Approach for Robust Metric Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, due to the pairwise relationship and a new balance regularization term, the sub-problem w.r.t. sample weights is a nonconvex quadratic function. To efficiently solve this nonconvex quadratic problem, we propose a doubly stochastic projection coordinate gradient algorithm. |
Chenkang Zhang; Lei Luo; Bin Gu; |
| 1256 | Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. |
Daoan Zhang; Chenming Li; Haoquan Li; Wenjian Huang; Lingyun Huang; Jianguo Zhang; |
| 1257 | Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Neglecting data heterogeneity, existing approaches cannot provide good estimates and impede policy learning. To overcome this drawback, the present study proposes a latent variable model and a model-learning algorithm to infer a set of policies from data, which allows an agent to use as behavior policy the policy that best describes a particular trajectory. |
Guoxi Zhang; Hisashi Kashima; |
| 1258 | DARL: Distance-Aware Uncertainty Estimation for Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we propose a non-parametric distance-aware uncertainty estimator which is sensitive to the change in the input space for offline reinforcement learning. |
Hongchang Zhang; Jianzhun Shao; Shuncheng He; Yuhang Jiang; Xiangyang Ji; |
| 1259 | When Neural Networks Fail to Generalize? A Model Sensitivity Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on our analysis, we propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies. |
Jiajin Zhang; Hanqing Chao; Amit Dhurandhar; Pin-Yu Chen; Ali Tajer; Yangyang Xu; Pingkun Yan; |
| 1260 | Memorization Weights for Instance Reweighting in Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel algorithm to reweight the training samples based on self-supervised techniques to mitigate the negative effects of the atypical samples. |
Jianfu Zhang; Yan Hong; Qibin Zhao; |
| 1261 | FedALA: Adaptive Local Aggregation for Personalized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. |
Jianqing Zhang; Yang Hua; Hao Wang; Tao Song; Zhengui Xue; Ruhui Ma; Haibing Guan; |
| 1262 | Delving Into The Adversarial Robustness of Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems. |
Jie Zhang; Bo Li; Chen Chen; Lingjuan Lyu; Shuang Wu; Shouhong Ding; Chao Wu; |
| 1263 | DRGCN: Dynamic Evolving Initial Residual for Deep Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel model called Dynamic evolving initial Residual Graph Convolutional Network (DRGCN). |
Lei Zhang; Xiaodong Yan; Jianshan He; Ruopeng Li; Wei Chu; |
| 1264 | Let The Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-view data distribution. (ii) Searching for the optimal anchor number within hyper-parameters takes much extra tuning time, which makes existing methods impractical. (iii) How to flexibly fuse multi-view anchor graphs of diverse sizes has not been well explored in existing literature. To address the above issues, we propose a novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) in this paper. |
Pei Zhang; Siwei Wang; Liang Li; Changwang Zhang; Xinwang Liu; En Zhu; Zhe Liu; Lu Zhou; Lei Luo; |
| 1265 | Optimal Sparse Regression Trees Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work proposes a dynamic programming-with-bounds approach to the construction of provably-optimal sparse regression trees. |
Rui Zhang; Rui Xin; Margo Seltzer; Cynthia Rudin; |
| 1266 | High-Dimensional Dueling Optimization with Preference Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Fortunately, it has been observed that, in recommendation systems, the dueling results are mainly determined by the latent human preferences. In this paper, we abstract this phenomenon as the preferential intrinsic dimension and inject it into the dueling Bayesian optimization, resulting in the preferential embedding dueling Bayesian optimization (PE-DBO). |
Yangwenhui Zhang; Hong Qian; Xiang Shu; Aimin Zhou; |
| 1267 | Spectral Feature Augmentation for Graph Contrastive Learning and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we present a novel spectral feature argumentation for contrastive learning on graphs (and images). |
Yifei Zhang; Hao Zhu; Zixing Song; Piotr Koniusz; Irwin King; |
| 1268 | Scalable Bayesian Meta-Learning Through Generalized Implicit Gradients Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work markedly mitigates the scalability bottleneck by cross-fertilizing the benefits of implicit differentiation to probabilistic Bayesian meta-learning. |
Yilang Zhang; Bingcong Li; Shijian Gao; Georgios B. Giannakis; |
| 1269 | Dynamic Heterogeneous Graph Attention Neural Architecture Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to automate the design of DHGNN, which faces two major challenges: 1) how to design the search space to jointly consider the spatial-temporal dependencies and heterogeneous interactions in graphs; 2) how to design an efficient search algorithm in the potentially large and complex search space. |
Zeyang Zhang; Ziwei Zhang; Xin Wang; Yijian Qin; Zhou Qin; Wenwu Zhu; |
| 1270 | Dynamic Ensemble of Low-Fidelity Experts: Mitigating NAS “Cold-Start” Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on exploiting information in cheaper-to-obtain performance estimations (i.e., low-fidelity information) to mitigate the large data requirements of predictor training. |
Junbo Zhao; Xuefei Ning; Enshu Liu; Binxin Ru; Zixuan Zhou; Tianchen Zhao; Chen Chen; Jiajin Zhang; Qingmin Liao; Yu Wang; |
| 1271 | Tensorized Incomplete Multi-View Clustering with Intrinsic Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. |
Shuping Zhao; Jie Wen; Lunke Fei; Bob Zhang; |
| 1272 | Imbalanced Label Distribution Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the Imbalanced Label Distribution Learning (ILDL) problem. |
Xingyu Zhao; Yuexuan An; Ning Xu; Jing Wang; Xin Geng; |
| 1273 | CoopInit: Initializing Generative Adversarial Networks Via Cooperative Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes the CoopInit, a simple yet effective cooperative learning-based initialization strategy that can quickly learn a good starting point for GANs, with a very small computation overhead during training. |
Yang Zhao; Jianwen Xie; Ping Li; |
| 1274 | AutoGraph: Optimizing DNN Computation Graph for Parallel GPU Kernel Execution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a unified framework, AutoGraph, is proposed to obtain highly optimized computation graphs in favor of parallel executions of GPU kernels. |
Yuxuan Zhao; Qi Sun; Zhuolun He; Yang Bai; Bei Yu; |
| 1275 | Fairness and Explainability: Bridging The Gap Towards Fair Model Explanations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we bridge the gap between fairness and explainability by presenting a novel perspective of procedure-oriented fairness based on explanations. |
Yuying Zhao; Yu Wang; Tyler Derr; |
| 1276 | Adaptive Policy Learning for Offline-to-Online Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data. |
Han Zheng; Xufang Luo; Pengfei Wei; Xuan Song; Dongsheng Li; Jing Jiang; |
| 1277 | Multi-Level Confidence Learning for Trustworthy Multimodal Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. |
Xiao Zheng; Chang Tang; Zhiguo Wan; Chengyu Hu; Wei Zhang; |
| 1278 | CowClip: Reducing CTR Prediction Model Training Time from 12 Hours to 10 Minutes on 1 GPU Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To stabilize the training process in a large batch size setting, we develop the adaptive Column-wise Clipping (CowClip). |
Zangwei Zheng; Pengtai Xu; Xuan Zou; Da Tang; Zhen Li; Chenguang Xi; Peng Wu; Leqi Zou; Yijie Zhu; Ming Chen; Xiangzhuo Ding; Fuzhao Xue; Ziheng Qin; Youlong Cheng; Yang You; |
| 1279 | Data Imputation with Iterative Graph Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel Iterative graph Generation and Reconstruction framework for Missing data imputation(IGRM). |
Jiajun Zhong; Ning Gui; Weiwei Ye; |
| 1280 | Does It Pay to Optimize AUC? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in R2, which runs in O(n+n- log n+n-) where n+ and n- are the number of positive and negative samples respectively. |
Baojian Zhou; Steven Skiena; |
| 1281 | SLOTH: Structured Learning and Task-Based Optimization for Time Series Forecasting on Hierarchies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. |
Fan Zhou; Chen Pan; Lintao Ma; Yu Liu; Shiyu Wang; James Zhang; Xinxin Zhu; Xuanwei Hu; Yunhua Hu; Yangfei Zheng; Lei Lei; Hu Yun; |
| 1282 | Robust Temporal Smoothness in Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that even only one outlier task will destroy the performance of the entire model. |
Menghui Zhou; Yu Zhang; Yun Yang; Tong Liu; Po Yang; |
| 1283 | Combining Adversaries with Anti-adversaries in Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, the influence of adversarial training on deep learning models in terms of fairness, robustness, and generalization is theoretically investigated under more general perturbation scope that different samples can have different perturbation directions (the adversarial and anti-adversarial directions) and varied perturbation bounds. |
Xiaoling Zhou; Nan Yang; Ou Wu; |
| 1284 | Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Gradient-adaptive Constrained Policy Optimization (GCPO for short), a novel Pareto optimization method for CRL with two adaptive gradient recalibration techniques. |
Zixian Zhou; Mengda Huang; Feiyang Pan; Jia He; Xiang Ao; Dandan Tu; Qing He; |
| 1285 | Quantized Feature Distillation for Network Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel and highly effective QAT method, quantized feature distillation (QFD). |
Ke Zhu; Yin-Yin He; Jianxin Wu; |
| 1286 | Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This few-shot OoD generalization dilemma emerges as a challenging direction in deep neural network generalization research, where the performance suffers from overfitting on few-shot examples and OoD generalization errors. In this paper, leveraging a broader supervision source, we explore a novel Bayesian cross-modal image-text alignment learning method (Bayes-CAL) to address this issue. |
Lin Zhu; Xinbing Wang; Chenghu Zhou; Nanyang Ye; |
| 1287 | ContraFeat: Contrasting Deep Features for Semantic Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing semantic discovery methods on StyleGAN rely on manual selection of modified latent layers to obtain satisfactory manipulation results, which is tedious and demanding. In this paper, we propose a model that automates this process and achieves state-of-the-art semantic discovery performance. |
Xinqi Zhu; Chang Xu; Dacheng Tao; |
| 1288 | Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering. |
Yongxin Zhu; Zhen Liu; Yukang Liang; Xin Li; Hao Liu; Changcun Bao; Linli Xu; |
| 1289 | ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual confusion, dubbed "information leakage", resulting in notable degeneration of the generated segmentation mask. To alleviate this issue, we propose a simple-yet-effective explicit layer independence modeling approach, termed Independent Layer Synthesis GAN (ILSGAN), pursuing independent foreground-background layer generation by encouraging their discrepancy. |
Qiran Zou; Yu Yang; Wing Yin Cheung; Chang Liu; Xiangyang Ji; |
| 1290 | SVP-T: A Shape-Level Variable-Position Transformer for Multivariate Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method, called SVP-T. |
Rundong Zuo; Guozhong Li; Byron Choi; Sourav S Bhowmick; Daphne Ngar-yin Mah; Grace L.H. Wong; |
| 1291 | Mixed-Variable Black-Box Optimisation Using Value Proposal Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adopt a holistic view and aim to consolidate optimisation of the categorical and continuous sub-spaces under a single acquisition metric. |
Yan Zuo; Vu Nguyen; Amir Dezfouli; David Alexander; Benjamin Ward Muir; Iadine Chades; |
| 1292 | Synchronization and Diversity of Solutions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This problem has found applications in a variety of subfields of artificial intelligence, including planning, robotics, and multi-agent systems. In this work, we study this problem within the framework of diversity of solutions, an up-and-coming trend in the field of artificial intelligence where the goal is to compute a set of solutions that are sufficiently distinct from one another. |
Emmanuel Arrighi; Henning Fernau; Mateus de Oliveira Oliveira; Petra Wolf; |
| 1293 | The Multi-Agent Transportation Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the multi-agent transportation (MAT) problem, where agents have to transport containers from their starting positions to their designated goal positions. |
Pascal Bachor; Rolf-David Bergdoll; Bernhard Nebel; |
| 1294 | Emergent Quantized Communication Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an alternative approach to achieve discrete communication — quantization of communicated message. |
Boaz Carmeli; Ron Meir; Yonatan Belinkov; |
| 1295 | Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning Via Polarization Policy Gradient Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most MAPG algorithms cannot achieve good credit assignment because of the game-theoretic pathology known as centralized-decentralized mismatch. To address this issue, this paper presents a novel method, Multi-Agent Polarization Policy Gradient (MAPPG). |
Wubing Chen; Wenbin Li; Xiao Liu; Shangdong Yang; Yang Gao; |
| 1296 | Zero-Shot Assistance in Sequential Decision Problems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The difficulty is that we must account for potential biases of the agent which may cause it to seemingly irrationally reject advice. To do this we introduce a novel formalization of assistance that models these biases, allowing the assistant to infer and adapt to them. |
Sebastiaan De Peuter; Samuel Kaski; |
| 1297 | Multi-Unit Auctions for Allocating Chance-Constrained Resources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new auction mechanism for preallocating multi-unit resources among agents, while limiting the chance of resource violations. |
Anna Gautier; Bruno Lacerda; Nick Hawes; Michael Wooldridge; |
| 1298 | Reward-Based Negotiating Agent Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. |
Ryota Higa; Katsuhide Fujita; Toki Takahashi; Takumu Shimizu; Shinji Nakadai; |
| 1299 | Intersection Coordination with Priority-Based Search for Autonomous Vehicles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, the coordination of the vehicles is essentially a Multi-Agent Path Finding (MAPF) problem, for which dozens of efficient algorithms have been proposed in recent years. Inspired by these MAPF algorithms, we propose a three-level algorithm called PSL to solve the intersection coordination problem. |
Jiaoyang Li; The Anh Hoang; Eugene Lin; Hai L. Vu; Sven Koenig; |
| 1300 | Solving Large-Scale Pursuit-Evasion Games Using Pre-trained Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, even these methods quickly reach their limits when facing large combinatorial strategy spaces of the pursuit-evasion games. To improve their efficiency, we integrate the pre-training and fine-tuning paradigm into the core module of PSRO — the repeated computation of the best response. |
Shuxin Li; Xinrun Wang; Youzhi Zhang; Wanqi Xue; Jakub Černý; Bo An; |
| 1301 | Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly boosting the credit-level distinguishability of the VD network to break the bottleneck of multi-agent diversity. |
Shunyu Liu; Yihe Zhou; Jie Song; Tongya Zheng; Kaixuan Chen; Tongtian Zhu; Zunlei Feng; Mingli Song; |
| 1302 | Learning to Shape Rewards Using A Game of Two Partners Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Reinforcement Learning Optimising Shaping Algorithm (ROSA), an automated reward shaping framework in which the shaping-reward function is constructed in a Markov game between two agents. |
David Mguni; Taher Jafferjee; Jianhong Wang; Nicolas Perez-Nieves; Wenbin Song; Feifei Tong; Matthew Taylor; Tianpei Yang; Zipeng Dai; Hui Chen; Jiangcheng Zhu; Kun Shao; Jun Wang; Yaodong Yang; |
| 1303 | Reconstructing An Epidemic Outbreak Using Steiner Connectivity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. |
Ritwick Mishra; Jack Heavey; Gursharn Kaur; Abhijin Adiga; Anil Vullikanti; |
| 1304 | Formal Verification of Bayesian Mechanisms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, for the first time, we study the formal verification of Bayesian mechanisms through strategic reasoning. |
Munyque Mittelmann; Bastien Maubert; Aniello Murano; Laurent Perrussel; |
| 1305 | Memory-Augmented Theory of Mind Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. |
Dung Nguyen; Phuoc Nguyen; Hung Le; Kien Do; Svetha Venkatesh; Truyen Tran; |
| 1306 | Socially Optimal Non-discriminatory Restrictions for Continuous-Action Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address the following mechanism design problem: Given a multi-player Normal-Form Game (NFG) with a continuous action space, find a non-discriminatory (i.e., identical for all players) restriction of the action space which maximizes the resulting Nash Equilibrium with respect to a fixed social utility function. |
Michael Oesterle; Guni Sharon; |
| 1307 | Fault-Tolerant Offline Multi-Agent Path Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such planning is attractive to build reliable multi-robot systems. We present problem formalization, theoretical analysis such as computational complexities, and how to solve this offline planning problem. |
Keisuke Okumura; Sébastien Tixeuil; |
| 1308 | LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM). |
Keisuke Okumura; |
| 1309 | Networked Anti-coordination Games Meet Graphical Dynamical Systems: Equilibria and Convergence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Two key problems concerning evolutionary games are the existence of a pure Nash equilibrium (NE) and the convergence time. In this work, we study these two problems for anti-coordination games under sequential and synchronous update schemes. |
Zirou Qiu; Chen Chen; Madhav V. Marathe; S. S. Ravi; Daniel J. Rosenkrantz; Richard E. Stearns; Anil Vullikanti; |
| 1310 | Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the cooperative game with global reward, one agent learned by existing offline MARL often inherits this random policy, jeopardizing the utility of the entire team. In this paper, we investigate offline MARL with explicit consideration on the diversity of agent-wise trajectories and propose a novel framework called Shared Individual Trajectories (SIT) to address this problem. |
Qi Tian; Kun Kuang; Furui Liu; Baoxiang Wang; |
| 1311 | Resource Sharing Through Multi-Round Matchings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Applications such as employees sharing office spaces over a workweek can be modeled as problems where agents are matched to resources over multiple rounds. Agents’ requirements limit the set of compatible resources and the rounds in which they want to be matched. Viewing such an application as a multi-round matching problem on a bipartite compatibility graph between agents and resources, we show that a solution (i.e., a set of matchings, with one matching per round) can be found efficiently if one exists. |
Yohai Trabelsi; Abhijin Adiga; Sarit Kraus; S. S. Ravi; Daniel J. Rosenkrantz; |
| 1312 | Effective Integration of Weighted Cost-to-Go and Conflict Heuristic Within Suboptimal CBS Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants. |
Rishi Veerapaneni; Tushar Kusnur; Maxim Likhachev; |
| 1313 | DM²: Decentralized Multi-Agent Reinforcement Learning Via Distribution Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper studies the problem of distributed multi-agent learning without resorting to centralized components or explicit communication. |
Caroline Wang; Ishan Durugkar; Elad Liebman; Peter Stone; |
| 1314 | Emergence of Punishment in Social Dilemma with Environmental Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we contribute with a novel evolutionary game theoretic model to study the impacts of environmental feedback. |
Zhen Wang; Zhao Song; Chen Shen; Shuyue Hu; |
| 1315 | Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel exploration approach, which encodes a special structural prior on the reward function into exploration, for sparse-reward multi-agent tasks. |
Pei Xu; Junge Zhang; Qiyue Yin; Chao Yu; Yaodong Yang; Kaiqi Huang; |
| 1316 | Consensus Learning for Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by viewpoint invariance and contrastive learning, we propose consensus learning for cooperative multi-agent reinforcement learning in this study. |
Zhiwei Xu; Bin Zhang; Dapeng Li; Zeren Zhang; Guangchong Zhou; Hao Chen; Guoliang Fan; |
| 1317 | HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the intra-level and inter-level coordination in the human nervous system, we propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for fully cooperative multi-agent problems. |
Zhiwei Xu; Yunpeng Bai; Bin Zhang; Dapeng Li; Guoliang Fan; |
| 1318 | Hierarchical Mean-Field Deep Reinforcement Learning for Large-Scale Multiagent Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Hierarchical Mean-Field (HMF) learning framework to further improve the performance of existing MF methods. |
Chao Yu; |
| 1319 | Robust Multi-Agent Coordination Via Evolutionary Generation of Auxiliary Adversarial Attackers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This issue hasn’t been considered in problem formulation or efficient algorithm design. To address this issue, we firstly model the problem as a Limited Policy Adversary Dec-POMDP (LPA-Dec-POMDP), where some coordinators from a team might accidentally and unpredictably encounter a limited number of malicious action attacks, but the regular coordinators still strive for the intended goal. Then, we propose Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers (ROMANCE), which enables the trained policy to encounter diversified and strong auxiliary adversarial attacks during training, thus achieving high robustness under various policy perturbations. |
Lei Yuan; Ziqian Zhang; Ke Xue; Hao Yin; Feng Chen; Cong Guan; Lihe Li; Chao Qian; Yang Yu; |
| 1320 | DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we demonstrate that ignoring communication delays has detrimental effects on collaborations, especially in delay-sensitive tasks such as autonomous driving. |
Tingting Yuan; Hwei-Ming Chung; Jie Yuan; Xiaoming Fu; |
| 1321 | Effective and Stable Role-Based Multi-Agent Collaboration By Structural Information Principles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this article, we propose a mathematical Structural Information principles-based Role Discovery method, namely SIRD, and then present a SIRD optimizing MARL framework, namely SR-MARL, for multi-agent collaboration. |
Xianghua Zeng; Hao Peng; Angsheng Li; |
| 1322 | Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we show that no-regret dynamics can identify these worst-case responses in poly-time in smooth games. We propose a framework that uses this policy evaluation method for efficiently learning a robust principal policy using RL. |
Eric Zhao; Alexander R. Trott; Caiming Xiong; Stephan Zheng; |
| 1323 | Towards Robust Metrics for Concept Representation Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While concept learning lacks metrics to measure such phenomena, the field of disentanglement learning has explored the related notion of underlying factors of variation in the data, with plenty of metrics to measure the purity of such factors. In this paper, we show that such metrics are not appropriate for concept learning and propose novel metrics for evaluating the purity of concept representations in both approaches. |
Mateo Espinosa Zarlenga; Pietro Barbiero; Zohreh Shams; Dmitry Kazhdan; Umang Bhatt; Adrian Weller; Mateja Jamnik; |
| 1324 | On The Vulnerability of Backdoor Defenses for Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning in a practical setting by proposing a new federated backdoor attack framework for possible countermeasures. |
Pei Fang; Jinghui Chen; |
| 1325 | Distributionally Robust Optimization with Probabilistic Group Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework PG-DRO, which explores the idea of probabilistic group membership for distributionally robust optimization. |
Soumya Suvra Ghosal; Yixuan Li; |
| 1326 | Correct for Whom? Subjectivity and The Evaluation of Personalized Image Aesthetics Assessment Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we connect few-shot personalization, via Immanuel Kant’s concept of disinterested judgment, to an argument from feminist aesthetics about the biased tendencies of objective standards for subjective pleasures. |
Samuel Goree; Weslie Khoo; David J. Crandall; |
| 1327 | Covariate-Shift Generalization Via Random Sample Weighting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and effective non-parametric method for generating heterogeneous environments via Random Sample Weighting (RSW). |
Yue He; Xinwei Shen; Renzhe Xu; Tong Zhang; Yong Jiang; Wenchao Zou; Peng Cui; |
| 1328 | Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness considerations in mind. To address this problem, we propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning. |
Nathanael Jo; Bill Tang; Kathryn Dullerud; Sina Aghaei; Eric Rice; Phebe Vayanos; |
| 1329 | Improvement-Focused Causal Recourse (ICR) Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel method, Improvement-Focused Causal Recourse (ICR), which involves a conceptual shift: Firstly, we require ICR recommendations to guide toward improvement. |
Gunnar König; Timo Freiesleben; Moritz Grosse-Wentrup; |
| 1330 | Explaining Model Confidence Using Counterfactuals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that counterfactual explanations of confidence scores help study participants to better understand and better trust a machine learning model’s prediction. |
Thao Le; Tim Miller; Ronal Singh; Liz Sonenberg; |
| 1331 | Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. |
Yixuan Liu; Suyun Zhao; Li Xiong; Yuhan Liu; Hong Chen; |
| 1332 | XRand: Differentially Private Defense Against Explanation-Guided Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. |
Truc Nguyen; Phung Lai; Hai Phan; My T. Thai; |
| 1333 | Mitigating Adversarial Norm Training with Moral Axioms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the issue of adversarial attacks on ethical AI systems. |
Taylor Olson; Kenneth D. Forbus; |
| 1334 | Equity Promotion in Public Transportation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an optimization model to study how to integrate the two approaches together for equity-promotion purposes. |
Anik Pramanik; Pan Xu; Yifan Xu; |
| 1335 | Online Platforms and The Fair Exposure Problem Under Homophily Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce the fair exposure problem: given limited intervention power of the platform, the goal is to enforce balance in the spread of content (e.g., news articles) among two groups of users through constraints similar to those imposed by the Fairness Doctrine in the United States in the past. |
Jakob Schoeffer; Alexander Ritchie; Keziah Naggita; Faidra Monachou; Jessica Finocchiaro; Marc Juarez; |
| 1336 | Minimax AUC Fairness: Efficient Algorithm with Provable Convergence Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a minimax learning and bias mitigation framework that incorporates both intra-group and inter-group AUCs while maintaining utility. |
Zhenhuan Yang; Yan Lok Ko; Kush R. Varshney; Yiming Ying; |
| 1337 | Faster Fair Machine Via Transferring Fairness Constraints to Virtual Samples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although many new tailored algorithms have been designed to attempt to overcome this limitation, they often increase additional computation burden and cannot cope with all types of fairness metrics. To address these challenging issues, in this paper, we propose a novel method for fair classification. |
Zhou Zhai; Lei Luo; Heng Huang; Bin Gu; |
| 1338 | Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p in [0,1] over the infinite time horizon. |
Đorđe Žikelić; Mathias Lechner; Thomas A. Henzinger; Krishnendu Chatterjee; |
| 1339 | Robust Neuro-Symbolic Goal and Plan Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop neuro-symbolic recognition approaches that can combine learning and planning techniques, compensating for noise and missing observations using prior data. |
Leonardo Amado; Ramon Fraga Pereira; Felipe Meneguzzi; |
| 1340 | Heuristic Search for Multi-Objective Probabilistic Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we extend the reach of heuristic search to a more expressive class of problems, namely multi-objective stochastic shortest paths (MOSSPs), which require computing a coverage set of non-dominated policies. |
Dillon Z. Chen; Felipe Trevizan; Sylvie Thiébaux; |
| 1341 | Zero-Knowledge Proofs for Classical Planning Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider the scenario where a party who knows the solution to a planning task, called the prover, wants to convince a second party, the verifier, that it has the solution without revealing any information about the solution itself. |
Augusto B. Corrêa; Clemens Büchner; Remo Christen; |
| 1342 | Planning with Hidden Parameter Polynomial MDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an alternative approach for maintaining the belief over the latent parameters. |
Clarissa Costen; Marc Rigter; Bruno Lacerda; Nick Hawes; |
| 1343 | Privacy Attacks on Schedule-Driven Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a first threat model for published schedules, thereby defining a completely new class of data privacy problems. |
Stephan A. Fahrenkrog-Petersen; Arik Senderovich; Alexandra Tichauer; Ali Kaan Tutak; J. Christopher Beck; Matthias Weidlich; |
| 1344 | Markov Decision Processes with Time-Varying Geometric Discounting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An algorithm is presented to compute an epsilon-SPE, of which an upper bound of the time complexity, as a function of the convergence property of the time-varying discount factor, is provided. |
Jiarui Gan; Annika Hennes; Rupak Majumdar; Debmalya Mandal; Goran Radanovic; |
| 1345 | Learning-Augmented Algorithms for Online TSP on The Line Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce algorithms that (i) obtain a tight 1.5 competitive ratio for the closed variant and a 1.66 competitive ratio for the open variant in the case of perfect predictions, (ii) are robust against unbounded prediction error, and (iii) are smooth, i.e., their performance degrades gracefully as the prediction error increases. |
Themistoklis Gouleakis; Konstantinos Lakis; Golnoosh Shahkarami; |
| 1346 | Networked Restless Bandits with Positive Externalities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We thus introduce networked restless bandits, a novel multi-armed bandit setting in which arms are both restless and embedded within a directed graph. |
Christine Herlihy; John P. Dickerson; |
| 1347 | Planning for Learning Object Properties Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). |
Leonardo Lamanna; Luciano Serafini; Mohamadreza Faridghasemnia; Alessandro Saffiotti; Alessandro Saetti; Alfonso Gerevini; Paolo Traverso; |
| 1348 | Fully Online Matching with Stochastic Arrivals and Departures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a fully online matching problem with stochastic arrivals and departures. |
Zihao Li; Hao Wang; Zhenzhen Yan; |
| 1349 | Towards Automated Modeling Assistance: An Efficient Approach for Repairing Flawed Planning Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we are interested in automatically correcting a flawed domain. |
Songtuan Lin; Alban Grastien; Pascal Bercher; |
| 1350 | Was Fixing This Really That Hard? On The Complexity of Correcting HTN Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the complexity of finding a set of (optimal or suboptimal) model corrections so that those plans are (resp. |
Songtuan Lin; Pascal Bercher; |
| 1351 | On Total-Order HTN Plan Verification with Method Preconditions – An Extension of The CYK Parsing Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the plan verification problem for totally ordered (TO) HTN planning. |
Songtuan Lin; Gregor Behnke; Simona Ondrčková; Roman Barták; Pascal Bercher; |
| 1352 | A Dynamics and Task Decoupled Reinforcement Learning Architecture for High-Efficiency Dynamic Target Intercept Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study to apply UAVs in the dynamic target intercept (DTI) task, where the dynamics systems equipped by different UAV models are correspondingly distinct. |
Dora D. Liu; Liang Hu; Qi Zhang; Tangwei Ye; Usman Naseem; Zhong Yuan Lai; |
| 1353 | AlphaRoute: Large-Scale Coordinated Route Planning Via Monte Carlo Tree Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes AlphaRoute, an AlphaGo inspired algorithm for coordinating large-scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree Search (MCTS). |
Guiyang Luo; Yantao Wang; Hui Zhang; Quan Yuan; Jinglin Li; |
| 1354 | Learning Rational Subgoals from Demonstrations and Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals. |
Zhezheng Luo; Jiayuan Mao; Jiajun Wu; Tomás Lozano-Pérez; Joshua B. Tenenbaum; Leslie Pack Kaelbling; |
| 1355 | Learning Safe Numeric Action Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Learning safe action models for planning has been recently explored for domains in which states are sufficiently described with Boolean variables. In this work, we go beyond this limitation and propose the NSAM algorithm. |
Argaman Mordoch; Brendan Juba; Roni Stern; |
| 1356 | Automated Verification of Social Laws in Numeric Settings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a method to verify whether a given social law is robust via compilation to numeric planning. |
Ronen Nir; Alexander Shleyfman; Erez Karpas; |
| 1357 | Expressive Optimal Temporal Planning Via Optimization Modulo Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the problem of optimal temporal planning for a very expressive class of problems using a reduction of the bounded planning problem to Optimization Modulo Theory (OMT) a powerful discrete/continuous optimization framework. |
Stefan Panjkovic; Andrea Micheli; |
| 1358 | Flexible Budgets in Restless Bandits: A Primal-Dual Algorithm for Efficient Budget Allocation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In real-world planning settings, this flexibility in budget is often constrained to within a subset of consecutive planning steps, e.g., weekly planning of a monthly budget. In this paper we define a general class of RMABs with flexible budget, which we term F-RMABs, and provide an algorithm to optimally solve for them. |
Paula Rodriguez Diaz; Jackson A. Killian; Lily Xu; Arun Sai Suggala; Aparna Taneja; Milind Tambe; |
| 1359 | Structurally Restricted Fragments of Numeric Planning – A Complexity Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a well-known restricted form of Hoffmann’s simple numeric planning, which is undecidable. |
Alexander Shleyfman; Daniel Gnad; Peter Jonsson; |
| 1360 | Predicate Invention for Bilevel Planning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. |
Tom Silver; Rohan Chitnis; Nishanth Kumar; Willie McClinton; Tomás Lozano-Pérez; Leslie Kaelbling; Joshua B. Tenenbaum; |
| 1361 | Smoothed Online Combinatorial Optimization Using Imperfect Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study smoothed online combinatorial optimization problems when an imperfect predictive model is available, where the model can forecast the future cost functions with uncertainty. |
Kai Wang; Zhao Song; Georgios Theocharous; Sridhar Mahadevan; |
| 1362 | Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, prior works often learn the model by maximizing the predictive accuracy instead of final RMAB solution quality, causing a mismatch between training and evaluation objectives. To address this shortcoming, we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality. |
Kai Wang; Shresth Verma; Aditya Mate; Sanket Shah; Aparna Taneja; Neha Madhiwalla; Aparna Hegde; Milind Tambe; |
| 1363 | Neural TSP Solver with Progressive Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue, we propose a novel progressive distillation framework, by adopting curriculum learning to train TSP samples in increasing order of their problem size and progressively distilling high-level knowledge from small models to large models via a distillation loss. |
Dongxiang Zhang; Ziyang Xiao; Yuan Wang; Mingli Song; Gang Chen; |
| 1364 | The Linear Distance Traveling Tournament Problem Allows An EPTAS Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that for any 3≤k=o(∛n), LDTTP-k allows an efficient polynomial-time approximation scheme (EPTAS). |
Jingyang Zhao; Mingyu Xiao; |
| 1365 | Learning Relational Causal Models with Cycles Through Relational Acyclification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. |
Ragib Ahsan; David Arbour; Elena Zheleva; |
| 1366 | Causal Effect Identification in Cluster DAGs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new graphical modeling tool called cluster DAGs (for short, C-DAGs) that allows for the partial specification of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram. |
Tara V. Anand; Adele H. Ribeiro; Jin Tian; Elias Bareinboim; |
| 1367 | A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions. |
Ankur Ankan; Johannes Textor; |
| 1368 | Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce approaches that can work together in a score-based learning paradigm, to augment data with potentially different types of background knowledge. |
Debarun Bhattacharjya; Tian Gao; Dharmashankar Subramanian; Xiao Shou; |
| 1369 | Entropy Regularization for Population Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Entropy regularization is known to improve exploration in sequential decision-making problems. We show that this same mechanism can also lead to nearly unbiased and lower-variance estimates of the mean reward in the optimize-and-estimate structured bandit setting. |
Ben Chugg; Peter Henderson; Jacob Goldin; Daniel E. Ho; |
| 1370 | Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present the first principled approach for mining structural motifs in lifted graphical models, languages that blend first-order logic with probabilistic models, which uses a stochastic process to measure the similarity of entities in the data. |
Jonathan Feldstein; Dominic Phillips; Efthymia Tsamoura; |
| 1371 | A Faster Practical Approximation Scheme for The Permanent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We advance the rejection sampling approach, which provides probabilistic accuracy guarantees, unlike importance sampling. |
Juha Harviainen; Mikko Koivisto; |
| 1372 | Neural Diffeomorphic Non-uniform B-spline Flows Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose diffeomorphic non-uniform B-spline flows that are at least twice continuously differentiable while bi-Lipschitz continuous, enabling efficient parametrization while retaining analytic inverse transforms based on a sufficient condition for diffeomorphism. |
Seongmin Hong; Se Young Chun; |
| 1373 | Identification and Estimation of The Probabilities of Potential Outcome Types Using Covariate Information in Studies with Non-compliance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose novel identification conditions and a statistical estimation method for the probabilities of potential outcome types using covariate information in randomized trials in which the treatment assignment is randomized but subject compliance is not perfect. |
Yuta Kawakami; Ryusei Shingaki; Manabu Kuroki; |
| 1374 | Computing Divergences Between Discrete Decomposable Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, in the absence of any assumptions on the structure or independencies within these distributions, computing the divergence between them is an intractable problem in high dimensions. We show that we are able to compute a wide family of functionals and divergences, such as the alpha-beta divergence, between two decomposable models, i.e. chordal Markov networks, in time exponential to the treewidth of these models. |
Loong Kuan Lee; Nico Piatkowski; François Petitjean; Geoffrey I. Webb; |
| 1375 | Out-of-Distribution Generalization By Neural-Symbolic Joint Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Different from prior neural-symbolic methods that require background knowledge and candidate logical rules to be provided, we aim to induce task semantics with minimal priors. |
Anji Liu; Hongming Xu; Guy Van den Broeck; Yitao Liang; |
| 1376 | Novel Ordering-Based Approaches for Causal Structure Learning in The Presence of Unobserved Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. |
Ehsan Mokhtarian; Mohmmadsadegh Khorasani; Jalal Etesami; Negar Kiyavash; |
| 1377 | Maximizing The Probability of Fixation in The Positional Voter Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we generalize the standard biased Voter model to the positional Voter model, in which the invasion bias is effectuated only on an arbitrary subset of the network nodes, called biased nodes. |
Petros Petsinis; Andreas Pavlogiannis; Panagiotis Karras; |
| 1378 | Certifying Fairness of Probabilistic Circuits Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an algorithm to search for discrimination patterns in a general class of probabilistic models, namely probabilistic circuits. |
Nikil Roashan Selvam; Guy Van den Broeck; YooJung Choi; |
| 1379 | Probabilities of Potential Outcome Types in Experimental Studies: Identification and Estimation Based on Proxy Covariate Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, even in randomized experiments, assumptions on the data generating process, such as monotonicity, are required to evaluate the probabilities of the potential outcome types. To solve the problem without such assumptions in experimental studies, a novel identification condition based on proxy covariate information is proposed in this paper. |
Ryusei Shingaki; Manabu Kuroki; |
| 1380 | Lifted Inference with Linear Order Axiom Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we prove that adding a linear order axiom (which forces one of the predicates in φ to introduce a linear ordering of the domain elements in each model of φ) on top of the counting quantifiers still permits a computation time polynomial in the domain size. |
Jan Tóth; Ondřej Kuželka; |
| 1381 | Vector Causal Inference Between Two Groups of Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new constraint-based non-parametric approach for inferring the causal relationship between two vector-valued random variables from observational data. |
Jonas Wahl; Urmi Ninad; Jakob Runge; |
| 1382 | Efficient Enumeration of Markov Equivalent DAGs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first linear-time delay algorithm. |
Marcel Wienöbst; Malte Luttermann; Max Bannach; Maciej Liskiewicz; |
| 1383 | Differentially Private Nonlinear Causal Discovery from Numerical Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a method to infer nonlinear causal relations from observed numerical data by using regression-based conditional independence test (RCIT) that consists of kernel ridge regression (KRR) and Hilbert-Schmidt independence criterion (HSIC) with permutation approximation. |
Hao Zhang; Yewei Xia; Yixin Ren; Jihong Guan; Shuigeng Zhou; |
| 1384 | Safe Interval Path Planning with Kinodynamic Constraints Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In other words, the robot is subject to kinodynamic constraints. Unfortunately, as we show in this work, in such a case, the original SIPP is incomplete. |
Zain Alabedeen Ali; Konstantin Yakovlev; |
| 1385 | Diversity Maximization in The Presence of Outliers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The state-of-the-art algorithm for the diversity maximization problem is based on furthest point retrieval, which is too sensitive to outliers. We therefore address the problem of diversity maximization with outliers and propose two algorithms with performance guarantee. |
Daichi Amagata; |
| 1386 | Fair Short Paths in Vertex-Colored Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the setting where each vertex is assigned to a group (color), we provide a framework to model multiple natural fairness aspects. |
Matthias Bentert; Leon Kellerhals; Rolf Niedermeier; |
| 1387 | AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce AC-Band, a general approach for the AC problem based on multi-armed bandits that provides theoretical guarantees while exhibiting strong practical performance. |
Jasmin Brandt; Elias Schede; Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney; |
| 1388 | GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signages given their topology and showcase it in the context of gene regulation. |
Angelina Brilliantova; Hannah Miller; Ivona Bezáková; |
| 1389 | Optimal Pathfinding on Weighted Grid Maps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we introduce Weighted Jump Point Search (JPSW), a new type of pathfinding algorithm which breaks weighted grid symmetries by introducing a tiebreaking policy that allows us to apply effective pruning rules in symmetric regions. |
Mark Carlson; Sajjad K. Moghadam; Daniel D. Harabor; Peter J. Stuckey; Morteza Ebrahimi; |
| 1390 | Warm-Starting Nested Rollout Policy Adaptation with Optimal Stopping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Meta-NRPA, which combines optimal stopping theory with NRPA for warm-starting and significantly improves the performance of NRPA. |
Chen Dang; Cristina Bazgan; Tristan Cazenave; Morgan Chopin; Pierre-Henri Wuillemin; |
| 1391 | A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide a theoretical analysis of well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover. We propose a class of problems on which these EMO algorithms using crossover find the Pareto set in expected polynomial time. |
Duc-Cuong Dang; Andre Opris; Bahare Salehi; Dirk Sudholt; |
| 1392 | Runtime Analysis for The NSGA-II: Provable Speed-Ups from Crossover Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Continuing this research direction, we prove that the NSGA-II optimizes the OneJumpZeroJump benchmark asymptotically faster when crossover is employed. |
Benjamin Doerr; Zhongdi Qu; |
| 1393 | From Understanding The Population Dynamics of The NSGA-II to The First Proven Lower Bounds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Via a first mathematical understanding of the population dynamics of the NSGA-II, that is, by estimating the expected number of individuals having a certain objective value, we prove that the NSGA-II with suitable population size needs Omega(Nn log n) function evaluations to find the Pareto front of the OneMinMax problem and Omega(Nn^k) evaluations on the OneJumpZeroJump problem with jump size k. |
Benjamin Doerr; Zhongdi Qu; |
| 1394 | Ultrafast Euclidean Shortest Path Computation Using Hub Labeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This adversely affects their query processing cost. We address these limitations by proposing a novel adaptation of hub labeling which is the state-of-the-art approach for shortest distance computation in road networks. |
Jinchun Du; Bojie Shen; Muhammad Aamir Cheema; |
| 1395 | A Formal Metareasoning Model of Concurrent Planning and Execution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this incurs the risk of making incorrect action choices, especially if actions are irreversible. This tradeoff between opportunity and risk is the problem addressed in this paper. |
Amihay Elboher; Ava Bensoussan; Erez Karpas; Wheeler Ruml; Shahaf S. Shperberg; Eyal Shimony; |
| 1396 | TransPath: Learning Heuristics for Grid-Based Pathfinding Via Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search. |
Daniil Kirilenko; Anton Andreychuk; Aleksandr Panov; Konstantin Yakovlev; |
| 1397 | Large-State Reinforcement Learning for Hyper-Heuristics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, for the first time we use the trajectory of solution changes for a larger set of features for reinforcement learning in the novel hyper-heuristic LAST-RL (Large-State Reinforcement Learning). |
Lucas Kletzander; Nysret Musliu; |
| 1398 | Human Assisted Learning By Evolutionary Multi-Objective Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new framework HAL-EMO based on Evolutionary Multi-objective Optimization, which reformulates HAL as a bi-objective optimization problem that minimizes the number of selected instances for human decision and the total errors simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve it. |
Dan-Xuan Liu; Xin Mu; Chao Qian; |
| 1399 | OPT-GAN: A Broad-Spectrum Global Optimizer for Black-Box Problems By Learning Distribution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. |
Minfang Lu; Shuai Ning; Shuangrong Liu; Fengyang Sun; Bo Zhang; Bo Yang; Lin Wang; |
| 1400 | Analyzing and Improving The Use of The FastMap Embedding in Pathfinding Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper studies several aspects of FastMap embeddings, showing the relationship of FastMap to general additive heuristics. |
Reza Mashayekhi; Dor Atzmon; Nathan R. Sturtevant; |
| 1401 | Fully Computer-Assisted Proofs in Extremal Combinatorics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a fully computer-assisted proof system for solving a particular family of problems in Extremal Combinatorics. |
Olaf Parczyk; Sebastian Pokutta; Christoph Spiegel; Tibor Szabó; |
| 1402 | Electrophysiological Brain Source Imaging Via Combinatorial Search with Provable Optimality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. |
Guihong Wan; Meng Jiao; Xinglong Ju; Yu Zhang; Haim Schweitzer; Feng Liu; |
| 1403 | Improved Algorithm for Regret Ratio Minimization in Multi-Objective Submodular Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel HS-RRM algorithm by transforming RRM into HittingSet problems based on the notions of ε-kernel and δ-net, where any α-approximation algorithm for single-objective submodular maximization is used as an oracle. |
Yanhao Wang; Jiping Zheng; Fanxu Meng; |
| 1404 | Efficient Gradient Approximation Method for Constrained Bilevel Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve the problem, we develop a gradient-based approach, called gradient approximation method, which determines the descent direction by computing several representative gradients of the objective function inside a neighborhood of the current estimate. |
Siyuan Xu; Minghui Zhu; |
| 1405 | A Generalized Scalarization Method for Evolutionary Multi-Objective Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: When p is set to a small value, some middle subproblems have very small preference regions so that their direction vectors cannot pass through their corresponding preference regions. Therefore, we propose a generalized Lp (GLp) scalarization to ensure that the subproblem’s direction vector passes through its preference region. |
Ruihao Zheng; Zhenkun Wang; |
| 1406 | Generalized Category Discovery with Decoupled Prototypical Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). |
Wenbin An; Feng Tian; Qinghua Zheng; Wei Ding; Qianying Wang; Ping Chen; |
| 1407 | Structured Case-Based Reasoning for Inference-Time Adaptation of Text-to-SQL Parsers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose StructCBR, a structured case-based reasoning approach, which leverages subtree-level similarity between logical forms of cases and candidate outputs, resulting in better decoder decisions. |
Abhijeet Awasthi; Soumen Chakrabarti; Sunita Sarawagi; |
| 1408 | SegFormer: A Topic Segmentation Model with Controllable Range of Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a new text segmentation model SegFormer with unidirectional attention blocks to better model sentence representations. |
Haitao Bai; Pinghui Wang; Ruofei Zhang; Zhou Su; |
| 1409 | Rich Event Modeling for Script Event Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, in this paper, we propose the Rich Event Prediction (REP) framework for script event prediction. |
Long Bai; Saiping Guan; Zixuan Li; Jiafeng Guo; Xiaolong Jin; Xueqi Cheng; |
| 1410 | Avocodo: Generative Adversarial Network for Artifact-Free Vocoder Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, in preliminary experiments, we discovered that the multi-scale analysis which focuses on the low-frequency bands causes unintended artifacts, e.g., aliasing and imaging artifacts, which degrade the synthesized speech waveform quality. Therefore, in this paper, we investigate the relationship between these artifacts and GAN-based vocoders and propose a GAN-based vocoder, called Avocodo, that allows the synthesis of high-fidelity speech with reduced artifacts. |
Taejun Bak; Junmo Lee; Hanbin Bae; Jinhyeok Yang; Jae-Sung Bae; Young-Sun Joo; |
| 1411 | End-to-End Deep Reinforcement Learning for Conversation Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an end-to-end reinforcement learning (RL) approach that directly optimizes a global metric. |
Karan Bhukar; Harshit Kumar; Dinesh Raghu; Ajay Gupta; |
| 1412 | Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to augment LMs with the temporal logic induction ability, which frames the temporal reasoning by defining three modular components: temporal dependency inducer and temporal concept defuzzifier and logic validator. |
Bibo Cai; Xiao Ding; Zhouhao Sun; Bing Qin; Ting Liu; Baojun wang; Lifeng Shang; |
| 1413 | Zero-Shot Cross-Lingual Event Argument Extraction with Language-Oriented Prefix-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, when applying existing generation-based methods to zero-shot cross-lingual EAE, we find two critical challenges, including Language Discrepancy and Template Construction. In this paper, we propose a novel method termed as Language-oriented Prefix-tuning Network (LAPIN) to address the above challenges. |
Pengfei Cao; Zhuoran Jin; Yubo Chen; Kang Liu; Jun Zhao; |
| 1414 | RPA: Reasoning Path Augmentation in Iterative Retrieving for Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Within the RP, two fundamental challenges emerge for better performance: (i) what the order of the justifications in the RP should be, and (ii) what if the wrong justification has been in the path. In this paper, we propose Reasoning Path Augmentation (RPA), which uses reasoning path reordering and augmentation to handle the above two challenges, respectively. |
Ziyi Cao; Bingquan Liu; Shaobo Li; |
| 1415 | Leveraging Modality-Specific Representations for Audio-Visual Speech Recognition Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a reinforcement learning (RL) based framework called MSRL, where the agent dynamically harmonizes modality-invariant and modality-specific representations in the auto-regressive decoding process. |
Chen Chen; Yuchen Hu; Qiang Zhang; Heqing Zou; Beier Zhu; Eng Siong Chng; |
| 1416 | Converge to The Truth: Factual Error Correction Via Iterative Constrained Editing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose VENCE, a novel method for factual error correction (FEC) with minimal edits. |
Jiangjie Chen; Rui Xu; Wenxuan Zeng; Changzhi Sun; Lei Li; Yanghua Xiao; |
| 1417 | Adversarial Word Dilution As Text Data Augmentation in Low-Resource Regime Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an Adversarial Word Dilution (AWD) method that can generate hard positive examples as text data augmentations to train the low-resource text classification model efficiently. |
Junfan Chen; Richong Zhang; Zheyan Luo; Chunming Hu; Yongyi Mao; |
| 1418 | CP-Rec: Contextual Prompting for Conversational Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by recent advances in prompt-based learning, we propose a novel contextual prompting framework for dialogue management, which optimizes prompts based on context, topics, and user profiles. |
Keyu Chen; Shiliang Sun; |
| 1419 | A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce our MQTTS system whose architecture is designed for multiple code generation and monotonic alignment, along with the use of a clean silence prompt to improve synthesis quality. |
Li-Wei Chen; Shinji Watanabe; Alexander Rudnicky; |
| 1420 | Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. |
Ruijun Chen; Jin Wang; Liang-Chih Yu; Xuejie Zhang; |
| 1421 | Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we raise two questions: i) Can diversity be further improved with an explicit objective? |
Wenqing Chen; Jidong Tian; Caoyun Fan; Yitian Li; Hao He; Yaohui Jin; |
| 1422 | Learning Towards Selective Data Augmentation for Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: (1) low-quality (the dialog model cannot generate a high-quality response for the case), (2) representative (the case should represent the property of the whole dataset). Herein, we explore this idea by proposing a Selective Data Augmentation framework (SDA) for the response generation task. |
Xiuying Chen; Mingzhe Li; Jiayi Zhang; Xiaoqiang Xia; Chen Wei; Jianwei Cui; Xin Gao; Xiangliang Zhang; Rui Yan; |
| 1423 | Learn from Yesterday: A Semi-supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. |
Yongrui Chen; Xinnan Guo; Tongtong Wu; Guilin Qi; Yang Li; Yang Dong; |
| 1424 | A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). |
Lizhi Cheng; Wenmian Yang; Weijia Jia; |
| 1425 | Unsupervised Explanation Generation Via Correct Instantiations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes Neon, a two-phrase, unsupervised explanation generation framework. |
Sijie Cheng; Zhiyong Wu; Jiangjie Chen; Zhixing Li; Yang Liu; Lingpeng Kong; |
| 1426 | Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-Shot In-Context Learners Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. |
Hyunsoo Cho; Hyuhng Joon Kim; Junyeob Kim; Sang-Woo Lee; Sang-goo Lee; Kang Min Yoo; Taeuk Kim; |
| 1427 | Neural Dynamic Focused Topic Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we leverage recent advances in neural variational inference and present an alternative neural approach to the dynamic Focused Topic Model. |
Kostadin Cvejoski; Ramsés J. Sánchez; César Ojeda; |
| 1428 | Improving Simultaneous Machine Translation with Monolingual Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose to leverage monolingual data to improve SiMT, which trains a SiMT student on the combination of bilingual data and external monolingual data distilled by Seq-KD. |
Hexuan Deng; Liang Ding; Xuebo Liu; Meishan Zhang; Dacheng Tao; Min Zhang; |
| 1429 | Domain-Adapted Dependency Parsing for Cross-Domain Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such dependency information is not inherently provided in most NER corpora, making the methods with low usability in practice. To effectively exploit the potential of word-dependency knowledge, motivated by the success of Multi-Task Learning on cross-domain NER, we investigate a novel NER learning method incorporating cross-domain Dependency Parsing (DP) as its auxiliary learning task. |
Chenxiao Dou; Xianghui Sun; Yaoshu Wang; Yunjie Ji; Baochang Ma; Xiangang Li; |
| 1430 | MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). |
Longxu Dou; Yan Gao; Mingyang Pan; Dingzirui Wang; Wanxiang Che; Dechen Zhan; Jian-Guang Lou; |
| 1431 | Learning to Select from Multiple Options Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: TE treats input texts as premises (P), options as hypotheses (H), then handles the selection problem by modeling (P, H) pairwise. Two limitations: first, the pairwise modeling is unaware of other options, which is less intuitive since humans often determine the best options by comparing competing candidates; second, the inference process of pairwise TE is time-consuming, especially when the option space is large. To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling. Context-TE is able to learn more reliable decision for the H since it considers various context. Second, we speed up Context-TE by coming up with Parallel-TE, which learns the decisions of multiple options simultaneously. |
Jiangshu Du; Wenpeng Yin; Congying Xia; Philip S. Yu; |
| 1432 | Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. |
Liam Dugan; Daphne Ippolito; Arun Kirubarajan; Sherry Shi; Chris Callison-Burch; |
| 1433 | Diffuser: Efficient Transformers with Multi-Hop Attention Diffusion for Long Sequences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To combine advantages of both the efficiency of sparse transformer and the expressiveness of full-attention Transformer, we propose Diffuser, a new state-of-the-art efficient Transformer. |
Aosong Feng; Irene Li; Yuang Jiang; Rex Ying; |
| 1434 | Cogito Ergo Summ: Abstractive Summarization of Biomedical Papers Via Semantic Parsing Graphs and Consistency Rewards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents CogitoErgoSumm, the first framework for biomedical abstractive summarization equipping large pre-trained language models with rich semantic graphs. |
Giacomo Frisoni; Paolo Italiani; Stefano Salvatori; Gianluca Moro; |
| 1435 | MIGA: A Unified Multi-Task Generation Framework for Conversational Text-to-SQL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a two-stage unified MultI-task Generation frAmework (MIGA) that leverages PLMs’ ability to tackle conversational text-to-SQL. |
Yingwen Fu; Wenjie Ou; Zhou Yu; Yue Lin; |
| 1436 | On The Effectiveness of Parameter-Efficient Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: How to choose the tunable parameters? In this paper, we first categorize the existing methods into random approaches, rule-based approaches, and projection-based approaches based on how they choose which parameters to tune. |
Zihao Fu; Haoran Yang; Anthony Man-Cho So; Wai Lam; Lidong Bing; Nigel Collier; |
| 1437 | SumREN: Summarizing Reported Speech About Events in News Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. |
Revanth Gangi Reddy; Heba Elfardy; Hou Pong Chan; Kevin Small; Heng Ji; |
| 1438 | ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Undesirably, these limitations would hinder the model’s performance in the target language. This paper proposes an unsupervised prototype knowledge distillation network (ProKD) to address these issues. |
Ling Ge; Chunming Hu; Guanghui Ma; Hong Zhang; Jihong Liu; |
| 1439 | Denoising Pre-training for Machine Translation Quality Estimation with Curriculum Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study shows that the noise mainly comes from the differences between pseudo and real translation outputs. To handle this problem, we propose CLQE, a denoising pre-training framework for QE based on curriculum learning. |
Xiang Geng; Yu Zhang; Jiahuan Li; Shujian Huang; Hao Yang; Shimin Tao; Yimeng Chen; Ning Xie; Jiajun Chen; |
| 1440 | Generating Coherent Narratives By Learning Dynamic and Discrete Entity States with A Contrastive Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, we extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. We propose a contrastive framework to learn the state representations in a discrete space, and insert additional attention layers into the decoder to better exploit these states. |
Jian Guan; Zhenyu Yang; Rongsheng Zhang; Zhipeng Hu; Minlie Huang; |
| 1441 | Learning to Imagine: Distillation-Based Interactive Context Exploitation for Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To reconcile the contextual consideration with avoiding the introduction of redundant information, we propose DICE-DST, a model-agnostic module widely applicable to the partial-history DST models, which aims to strengthen the ability of context exploitation for the encoder of each DST model. |
Jinyu Guo; Kai Shuang; Kaihang Zhang; Yixuan Liu; Jijie Li; Zihan Wang; |
| 1442 | RenewNAT: Renewing Potential Translation for Non-autoregressive Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose RenewNAT, a flexible framework with high efficiency and effectiveness, to incorporate the merits of fully and iterative NAT models. |
Pei Guo; Yisheng Xiao; Juntao Li; Min Zhang; |
| 1443 | Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose IS-CSE (instance smoothing contrastive sentence embedding) to smooth the boundaries of embeddings in the feature space. |
Hongliang He; Junlei Zhang; Zhenzhong Lan; Yue Zhang; |
| 1444 | Competition or Cooperation? Exploring Unlabeled Data Via Challenging Minimax Game for Semi-supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the consensus of two modules greatly restricts the model from exploring diverse relation expressions in unlabeled set, which hinders the performance as well as model generalization. To tackle this problem, in this paper, we propose a novel competition-based method AdvSRE. |
Yu Hong; Jiahang Li; Jianchuan Feng; Chenghua Huang; Zhixu Li; JIanfeng Qu; Yanghua Xiao; Wei Wang; |
| 1445 | Feature Normalization and Cartography-Based Demonstrations for Prompt-Based Fine-Tuning on Emotion-Related Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel data-agnostic strategy for prompt-based fine-tuning that leverages feature moments (a.k.a., mean and standard deviation) as a data augmentation technique and employs training dynamics (i.e., confidence and variability) to allow more informative samples to be concatenated for generating demonstrations as input context. |
Mahshid Hosseini; Cornelia Caragea; |
| 1446 | A Simple Yet Effective Subsequence-Enhanced Approach for Cross-Domain NER Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to incorporate subsequence-level features for promoting the cross-domain NER. |
Jinpeng Hu; DanDan Guo; Yang Liu; Zhuo Li; Zhihong Chen; Xiang Wan; Tsung-Hui Chang; |
| 1447 | A Question-Answering Approach to Key Value Pair Extraction from Form-Like Document Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. |
Kai Hu; Zhuoyuan Wu; Zhuoyao Zhong; Weihong Lin; Lei Sun; Qiang Huo; |
| 1448 | SEAT: Stable and Explainable Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, a natural question is whether we can find an alternative to vanilla attention, which is more stable and could keep the key characteristics of the explanation. In this paper, we provide a rigorous definition of such an attention method named SEAT (Stable and Explainable ATtention). |
Lijie Hu; Yixin Liu; Ninghao Liu; Mengdi Huai; Lichao Sun; Di Wang; |
| 1449 | Personalized Dialogue Generation with Persona-Adaptive Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To achieve that, in this paper, we propose an effective framework with Persona-Adaptive Attention (PAA), which adaptively integrates the weights from the persona and context information via our designed attention. |
Qiushi Huang; Yu Zhang; Tom Ko; Xubo Liu; Bo Wu; Wenwu Wang; H Tang; |
| 1450 | Question Decomposition Tree for Answering Complex Questions Over Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. |
Xiang Huang; Sitao Cheng; Yiheng Shu; Yuheng Bao; Yuzhong Qu; |
| 1451 | Hierarchical Text Classification As Sub-hierarchy Sequence Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, we propose the Hierarchy DECoder (HiDEC), which decodes a text sequence into a sub-hierarchy sequence using recursive hierarchy decoding, classifying all parents at the same level into children at once. |
SangHun Im; GiBaeg Kim; Heung-Seon Oh; Seongung Jo; Dong Hwan Kim; |
| 1452 | IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These models can then be evaluated on SLU tasks, such as the SUPERB benchmark. In this work, we extend this to Indic languages by releasing the IndicSUPERB benchmark. |
Tahir Javed; Kaushal Bhogale; Abhigyan Raman; Pratyush Kumar; Anoop Kunchukuttan; Mitesh M. Khapra; |
| 1453 | SheetPT: Spreadsheet Pre-training Based on Hierarchical Attention Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, pervasive data dependence and semantic relations across the sheet require comprehensive modeling of all the information rather than only the tables. In this paper, we propose SheetPT, the first pre-training technique on spreadsheets to enable effective representation learning under this scenario. |
Ran Jia; Qiyu Li; Zihan Xu; Xiaoyuan Jin; Lun Du; Haoyu Dong; Xiao Lv; Shi Han; Dongmei Zhang; |
| 1454 | SeDepTTS: Enhancing The Naturalness Via Semantic Dependency and Local Convolution for Text-to-Speech Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, we will mine linguistic information of the original text based on a semantic dependency and the semantic relationship between nodes is regarded as prior knowledge to revise the distribution of self-attention. On the other hand, given the strong correlation between input characters, we introduce a one-dimensional (1-D) convolution neural network (CNN) producing query(Q) and value(V) in the self-attention mechanism for a better fusion of local contextual information. |
Chenglong Jiang; Ying Gao; Wing W.Y. Ng; Jiyong Zhou; Jinghui Zhong; Hongzhong Zhen; |
| 1455 | Prototypical Fine-Tuning: Towards Robust Performance Under Varying Data Sizes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we move towards combining large parametric models with non-parametric prototypical networks. |
Yiqiao Jin; Xiting Wang; Yaru Hao; Yizhou Sun; Xing Xie; |
| 1456 | Cross-Modal Distillation for Speaker Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a multimodal learning framework, VGSR (Vision-Guided Speaker Recognition). |
Yufeng Jin; Guosheng Hu; Haonan Chen; Duoqian Miao; Liang Hu; Cairong Zhao; |
| 1457 | Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances. We propose MOSES, a deep neural network which takes a multimodal (sarcastic) dialogue instance as an input and generates a natural language sentence as its explanation. |
Shivani Kumar; Ishani Mondal; Md Shad Akhtar; Tanmoy Chakraborty; |
| 1458 | COCA: COllaborative CAusal Regularization for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through detailed causal-graph analyses and careful inspections of their learning processes, we reveal that AVQA models are not only prone to over-exploit prevalent language bias, but also suffer from additional joint-modal biases caused by the shortcut relations between textual-auditory/visual co-occurrences and dominated answers. In this paper, we propose a COllabrative CAusal (COCA) Regularization to remedy this more challenging issue of data biases. |
Mingrui Lao; Nan Pu; Yu Liu; Kai He; Erwin M. Bakker; Michael S. Lew; |
| 1459 | Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we argue that, due to the discrepancy from multilingual MLM pretraining, a naive specialization as such can be suboptimal. |
Jaeseong Lee; Dohyeon Lee; Seung-won Hwang; |
| 1460 | LIQUID: A Framework for List Question Answering Dataset Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although several recent studies have aimed to generate synthetic questions with single-span answers, no study has been conducted on the creation of list questions with multiple, non-contiguous spans as answers. To address this gap, we propose LIQUID, an automated framework for generating list QA datasets from unlabeled corpora. |
Seongyun Lee; Hyunjae Kim; Jaewoo Kang; |
| 1461 | UniSyn: An End-to-End Unified Model for Text-to-Speech and Singing Voice Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods usually suffer from some limitations, which rely on either both singing and speaking data from the same person or cascaded models of multiple tasks. To address these problems, a simplified elegant framework for TTS and SVS, named UniSyn, is proposed in this paper. |
Yi Lei; Shan Yang; Xinsheng Wang; Qicong Xie; Jixun Yao; Lei Xie; Dan Su; |
| 1462 | SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose SoftCorrect with a soft error detection mechanism to avoid the limitations of both explicit and implicit error detection. |
Yichong Leng; Xu Tan; Wenjie Liu; Kaitao Song; Rui Wang; Xiang-Yang Li; Tao Qin; Ed Lin; Tie-Yan Liu; |
| 1463 | Sequence Generation with Label Augmentation for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. |
Bo Li; Dingyao Yu; Wei Ye; Jinglei Zhang; Shikun Zhang; |
| 1464 | Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. |
Bo Li; Wei Ye; Jinglei Zhang; Shikun Zhang; |
| 1465 | Online Noisy Continual Relation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Another is that noisy labels are inevitable in real-world, as relation samples may be contaminated by label inconsistencies or labeled with distant supervision. In this work, therefore, we propose a novel continual relation learning framework that simultaneously addresses both online and noisy relation learning challenges. |
Guozheng Li; Peng Wang; Qiqing Luo; Yanhe Liu; Wenjun Ke; |
| 1466 | RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a ranking-enhanced encoding and skeleton-aware decoding framework to decouple the schema linking and the skeleton parsing. |
Haoyang Li; Jing Zhang; Cuiping Li; Hong Chen; |
| 1467 | Graphix-T5: Mixing Pre-trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore ways to further augment the pre-trained T5 model with specialized components for text-to-SQL parsing. |
Jinyang Li; Binyuan Hui; Reynold Cheng; Bowen Qin; Chenhao Ma; Nan Huo; Fei Huang; Wenyu Du; Luo Si; Yongbin Li; |
| 1468 | Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: And to guide HGSum to learn the compression, we introduce an additional objective that maximizes the similarity between the compressed graph and the graph constructed from the ground-truth summary during training. |
Miao Li; Jianzhong Qi; Jey Han Lau; |
| 1469 | TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. |
Minghao Li; Tengchao Lv; Jingye Chen; Lei Cui; Yijuan Lu; Dinei Florencio; Cha Zhang; Zhoujun Li; Furu Wei; |
| 1470 | Mitigating Negative Style Transfer in Hybrid Dialogue System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We devise supervised and self-supervised positive and negative sample constructions for diverse datasets. |
Shimin Li; Qinyuan Cheng; Linyang Li; Xipeng Qiu; |
| 1471 | Low Resource Quantitative Information Extraction Via Structure Searching and Prefix-Based Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study quantitative information extraction in the low-resource setting. |
Tongliang Li; Zixiang Wang; Zhoujun Li; |
| 1472 | SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, the majority of daily dialogues take place in a specific context or circumstance, which requires rich external knowledge to understand the background of a certain dialogue. In this paper, we address these challenges by explicitly modeling the discourse relations between utterances and incorporating symbolic knowledge into multi-party conversations. |
Wei Li; Luyao Zhu; Rui Mao; Erik Cambria; |
| 1473 | PGSS: Pitch-Guided Speech Separation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the effect of pitch priming in auditory scene analysis (ASA) mechanisms, a novel pitch-guided speech separation framework is proposed in this work. |
Xiang Li; Yiwen Wang; Yifan Sun; Xihong Wu; Jing Chen; |
| 1474 | DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning Over Tabular and Textual Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To attend to the retrieved information that is relevant to each generation step, in this paper, we propose DyRRen, an extended retriever-reranker-generator framework where each generation step is enhanced by a dynamic reranking of retrieved sentences. |
Xiao Li; Yin Zhu; Sichen Liu; Jiangzhou Ju; Yuzhong Qu; Gong Cheng; |
| 1475 | Heterogeneous-Branch Collaborative Learning for Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. |
Yiwei Li; Shaoxiong Feng; Bin Sun; Kan Li; |
| 1476 | Learning to Know Myself: A Coarse-to-Fine Persona-Aware Training Framework for Personalized Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the nature of persona sparsity in conversation scenarios, previous persona-based dialogue agents trained with Maximum Likelihood Estimation tend to overlook the given personas and generate responses irrelevant or inconsistent with personas. To address this problem, we propose a two-stage coarse-to-fine persona-aware training framework to improve the persona consistency of a dialogue agent progressively. |
Yunpeng Li; Yue Hu; Yajing Sun; Luxi Xing; Ping Guo; Yuqiang Xie; Wei Peng; |
| 1477 | WIERT: Web Information Extraction Via Render Tree Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present WIERT, a method that effectively utilizes the render tree of a web page based on a pretrained language model. |
Zimeng Li; Bo Shao; Linjun Shou; Ming Gong; Gen Li; Daxin Jiang; |
| 1478 | STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. |
Shuo Liang; Wei Wei; Xian-Ling Mao; Yuanyuan Fu; Rui Fang; Dangyang Chen; |
| 1479 | Generalizing Math Word Problem Solvers Via Solution Diversification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator. |
Zhenwen Liang; Jipeng Zhang; Lei Wang; Yan Wang; Jie Shao; Xiangliang Zhang; |
| 1480 | On Grounded Planning for Embodied Tasks with Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is a challenging task as LMs lack the ability to perceive the environment through vision and feedback from the physical environment. In this paper, we address this important research question and present the first investigation into the topic. |
Bill Yuchen Lin; Chengsong Huang; Qian Liu; Wenda Gu; Sam Sommerer; Xiang Ren; |
| 1481 | DeAR: A Deep-Learning-Based Audio Re-recording Resilient Watermarking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. |
Chang Liu; Jie Zhang; Han Fang; Zehua Ma; Weiming Zhang; Nenghai Yu; |
| 1482 | Detecting and Grounding Important Characters in Visual Stories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to address this limitation, we introduce the VIST-Character dataset, which provides rich character-centric annotations, including visual and textual co-reference chains and importance ratings for characters. Based on this dataset, we propose two new tasks: important character detection and character grounding in visual stories. |
Danyang Liu; Frank Keller; |
| 1483 | Boosting Few-Shot Text Classification Via Distribution Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. |
Han Liu; Feng Zhang; Xiaotong Zhang; Siyang Zhao; Fenglong Ma; Xiao-Ming Wu; Hongyang Chen; Hong Yu; Xianchao Zhang; |
| 1484 | SSPAttack: A Simple and Sweet Paradigm for Black-Box Hard-Label Textual Adversarial Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and sweet paradigm for hard-label textual adversarial attack, named SSPAttack. |
Han Liu; Zhi Xu; Xiaotong Zhang; Xiaoming Xu; Feng Zhang; Fenglong Ma; Hongyang Chen; Hong Yu; Xianchao Zhang; |
| 1485 | LADA-Trans-NER: Adaptive Efficient Transformer for Chinese Named Entity Recognition Using Lexicon-Attention and Data-Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Lexicon-Attention and Data-Augmentation (LADA) method for Chinese NER. |
Jiguo Liu; Chao Liu; Nan Li; Shihao Gao; Mingqi Liu; Dali Zhu; |
| 1486 | Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce selective knowledge distillation by introducing an NAT evaluator to select NAT-friendly targets that are of high quality and easy to learn. |
Min Liu; Yu Bao; Chengqi Zhao; Shujian Huang; |
| 1487 | A Disentangled-Attention Based Framework with Persona-Aware Prompt Learning for Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Another challenge is that it is crucial to consider the context and the conversation flow to dynamically determine when to invoke different types of persona signals. To address these problems, we propose a disentangled-attention based pre-training architecture, which incorporates persona-aware prompt learning to bridge the connection between the selected persona and response generation. |
Pingsheng Liu; Zhengjie Huang; Xiechi Zhang; Linlin Wang; Gerard de Melo; Xin Lin; Liang Pang; Liang He; |
| 1488 | Towards Credible Human Evaluation of Open-Domain Dialog Systems Using Interactive Setup Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we adopt the interactive evaluation framework and further apply to multiple models with a focus on per-turn evaluation techniques. |
Sijia Liu; Patrick Lange; Behnam Hedayatnia; Alexandros Papangelis; Di Jin; Andrew Wirth; Yang Liu; Dilek Hakkani-Tur; |
| 1489 | Unsupervised Paraphrasing Under Syntax Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Different from them, in this paper we investigate the structural patterns of word usages termed as the word composable knowledge and integrate it into the paraphrase generation to control the syntax in an explicit way. |
Tianyuan Liu; Yuqing Sun; Jiaqi Wu; Xi Xu; Yuchen Han; Cheng Li; Bin Gong; |
| 1490 | Adjective Scale Probe: Can Language Models Encode Formal Semantics Information? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose a diagnostic dataset to investigate how well language models understand the degree semantics of adjectives. |
Wei Liu; Ming Xiang; Nai Ding; |
| 1491 | Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification. |
Xiaokang Liu; Jianquan Li; Jingjing Mu; Min Yang; Ruifeng Xu; Benyou Wang; |
| 1492 | Learning Compositional Tasks from Language Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While progress has been made in supervised learning settings, no work has yet studied compositional generalization of a reinforcement learning agent following natural language instructions in an embodied environment. We develop a set of tasks in a photo-realistic simulated kitchen environment that allow us to study the degree to which a behavioral policy captures the systematicity in language by studying its zero-shot generalization performance on held out natural language instructions. |
Lajanugen Logeswaran; Wilka Carvalho; Honglak Lee; |
| 1493 | SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality. In this paper, we propose a Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph (SPRING) with abilities of reasoning multi-hops spatial relations and connecting them with visual attributes in crowded situated scenarios. |
Yuxing Long; Binyuan Hui; Fulong Ye; Yanyang Li; Zhuoxin Han; Caixia Yuan; Yongbin Li; Xiaojie Wang; |
| 1494 | Universal Information Extraction As Unified Semantic Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas. |
Jie Lou; Yaojie Lu; Dai Dai; Wei Jia; Hongyu Lin; Xianpei Han; Le Sun; Hua Wu; |
| 1495 | PUnifiedNER: A Prompting-Based Unified NER System for Diverse Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a “versatile” model—the Prompting-based Unified NER system (PUnifiedNER)—that works with data from different domains and can recognise up to 37 entity types simultaneously, and theoretically it could be as many as possible. |
Jinghui Lu; Rui Zhao; Brian Mac Namee; Fei Tan; |
| 1496 | KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the collaboration among different knowledge sources and present KICE, a Knowledge-evolving framework by Iterative Consolidation and Expansion with the guidance of PLMs and rule-based patterns. |
Yilin Lu; Xiaoqiang Wang; Haofeng Yang; Siliang Tang; |
| 1497 | Zero-Shot Slot Filling with Slot-Prefix Prompting and Attention Relationship Descriptor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a new prompting scheme that utilizes both learnable tokens and slot names to guide the model to focus on the relevant text spans for a given slot. |
Qiaoyang Luo; Lingqiao Liu; |
| 1498 | Feature-Level Debiased Natural Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods still encode biased latent features in representations and neglect the dynamic nature of bias, which hinders model prediction. We propose an NLU debiasing method, named debiasing contrastive learning (DCT), to simultaneously alleviate the above problems based on contrastive learning. |
Yougang Lyu; Piji Li; Yechang Yang; Maarten de Rijke; Pengjie Ren; Yukun Zhao; Dawei Yin; Zhaochun Ren; |
| 1499 | Graph Component Contrastive Learning for Concept Relatedness Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formalize the CRE properties and introduce a graph structure named ConcreteGraph. |
Yueen Ma; Zixing Song; Xuming Hu; Jingjing Li; Yifei Zhang; Irwin King; |
| 1500 | HybridPrompt: Bridging Language Models and Human Priors in Prompt Tuning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They generally do not integrate human priors to compensate for universal knowledge from language models, so as to fit the challenging VQA problem and generate reliable answers. To address these issues, we propose HybridPrompt, a cloze- and verify-style hybrid prompt framework with bridging language models and human priors in prompt tuning for VQA. |
Zhiyuan Ma; Zhihuan Yu; Jianjun Li; Guohui Li; |
| 1501 | Inferential Knowledge-Enhanced Integrated Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an Inferential Knowledge-Enhanced Integrated Reasoning method. |
Jianguo Mao; Wenbin Jiang; Hong Liu; Xiangdong Wang; Yajuan Lyu; |
| 1502 | AUC Maximization for Low-Resource Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. |
Ngoc Dang Nguyen; Wei Tan; Lan Du; Wray Buntine; RIchard Beare; Changyou Chen; |
| 1503 | Unveiling The Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, most works treat PLM as a black box in which the generation process of the target logical form is hidden beneath the decoder modules, which greatly hinders the model’s intrinsic interpretability. To address these two issues, we propose to incorporate the current PLMs with a hierarchical decoder network. |
Lunyiu Nie; Jiuding Sun; Yanlin Wang; Lun Du; Shi Han; Dongmei Zhang; Lei Hou; Juanzi Li; Jidong Zhai; |
| 1504 | Towards A Holistic Understanding of Mathematical Questions with Contrastive Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer. |
Yuting Ning; Zhenya Huang; Xin Lin; Enhong Chen; Shiwei Tong; Zheng Gong; Shijin Wang; |
| 1505 | Improving The Cross-Lingual Generalisation in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While several benefits were realized for multilingual vision-language pretrained models, recent benchmarks across various tasks and languages showed poor cross-lingual generalisation when multilingually pre-trained vision-language models are applied to non-English data, with a large gap between (supervised) English performance and (zero-shot) cross-lingual transfer. In this work, we explore the poor performance of these models on a zero-shot cross-lingual visual question answering (VQA) task, where models are fine-tuned on English visual-question data and evaluated on 7 typologically diverse languages. |
Farhad Nooralahzadeh; Rico Sennrich; |
| 1506 | RWEN-TTS: Relation-Aware Word Encoding Network for Natural Text-to-Speech Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: First, most approaches only use graph networks to utilize syntactic and semantic information without considering linguistic features. Second, most previous works do not explicitly consider adjacent words when encoding syntactic and semantic information, even though it is obvious that adjacent words are usually meaningful when encoding the current word. To address these issues, we propose Relation-aware Word Encoding Network (RWEN), which effectively allows syntactic and semantic information based on two modules (i.e., Semantic-level Relation Encoding and Adjacent Word Relation Encoding). |
Shinhyeok Oh; HyeongRae Noh; Yoonseok Hong; Insoo Oh; |
| 1507 | Hierarchical Event Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present an extension to the event grounding task that requires tackling hierarchical event structures from the KB. |
Jiefu Ou; Adithya Pratapa; Rishubh Gupta; Teruko Mitamura; |
| 1508 | RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a Retriever-Reranker-Reader framework by newly proposing a Reader-INherited evidence reranKer (RINK) where a reranker module is designed by finetuning the reader’s neural architecture based on a simple prompting method. |
Eunhwan Park; Sung-Min Lee; Dearyong Seo; Seonhoon Kim; Inho Kang; Seung-Hoon Na; |
| 1509 | Relation-Aware Language-Graph Transformer for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing GNN-based modules for QA do not take advantage of rich relational information of KGs and depend on limited information interaction between the LM and the KG. To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner. |
Jinyoung Park; Hyeong Kyu Choi; Juyeon Ko; Hyeonjin Park; Ji-Hoon Kim; Jisu Jeong; Kyungmin Kim; Hyunwoo Kim; |
| 1510 | Multi-Mask Label Mapping for Prompt-Based Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a multi-mask prompt-based approach with Multi-Mask Label Mapping (MMLM) to reduce the impact of misleading lexical cues by allowing the model to exploit multiple lexical cues. |
Jirui Qi; Richong Zhang; Jaein Kim; Junfan Chen; Wenyi Qin; Yongyi Mao; |
| 1511 | SSMI: Semantic Similarity and Mutual Information Maximization Based Enhancement for Chinese NER Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, this paper introduces a novel Chinese NER model named SSMI based on semantic similarity and MIM. |
Pengnian Qi; Biao Qin; |
| 1512 | Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System Across Multiple Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we first empirically show that the existing single-KB EToDs fail to work on multi-KB settings that require models to reason across various KBs. To solve this issue, we take the first step to consider the multi-KBs scenario in EToDs and introduce a KB-over-KB Heterogeneous Graph Attention Network (KoK-HAN) to facilitate model to reason over multiple KBs. |
Libo Qin; Zhouyang Li; Qiying Yu; Lehan Wang; Wanxiang Che; |
| 1513 | BERT-ERC: Fine-Tuning BERT Is Enough for Emotion Recognition in Conversation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. |
Xiangyu Qin; Zhiyu Wu; Tingting Zhang; Yanran Li; Jian Luan; Bin Wang; Li Wang; Jinshi Cui; |
| 1514 | Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-Grained Student Ensemble Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we argue that the performance of the current self-training frameworks for DS-NER is severely underestimated by their plain designs, including both inadequate student learning and coarse-grained teacher updating. Therefore, in this paper, we make the first attempt to alleviate these issues by proposing: (1) adaptive teacher learning comprised of joint training of two teacher-student networks and considering both consistent and inconsistent predictions between two teachers, thus promoting comprehensive student learning. |
Xiaoye Qu; Jun Zeng; Daizong Liu; Zhefeng Wang; Baoxing Huai; Pan Zhou; |
| 1515 | Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an end-to-end instance-wise and prototype-wise contrastive learning model with cross-attention mechanism for cross-domain rumor detection. |
Hongyan Ran; Caiyan Jia; |
| 1516 | Prompting Neural Machine Translation with Translation Memories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a simple but effective method to introduce TMs into neural machine translation (NMT) systems. |
Abudurexiti Reheman; Tao Zhou; Yingfeng Luo; Di Yang; Tong Xiao; Jingbo Zhu; |
| 1517 | Improving Interpretability Via Explicit Word Interaction Graph Layer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent NLP literature has seen growing interest in improving model interpretability. Along this direction, we propose a trainable neural network layer that learns a global interaction graph between words and then selects more informative words using the learned word interactions. |
Arshdeep Sekhon; Hanjie Chen; Aman Shrivastava; Zhe Wang; Yangfeng Ji; Yanjun Qi; |
| 1518 | Rephrasing The Reference for Non-autoregressive Machine Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the reference sentence may be inappropriate for the training when the NAT output is closer to other translations. In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output. |
Chenze Shao; Jinchao Zhang; Jie Zhou; Yang Feng; |
| 1519 | Drop Clause: Enhancing Performance, Robustness and Pattern Recognition Capabilities of The Tsetlin Machine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the logical learning element of TMs. |
Jivitesh Sharma; Rohan Yadav; Ole-Christoffer Granmo; Lei Jiao; |
| 1520 | CoP: Factual Inconsistency Detection By Controlling The Preference Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt. |
Shuaijie She; Xiang Geng; Shujian Huang; Jiajun Chen; |
| 1521 | Which Shortcut Solution Do Question Answering Models Prefer to Learn? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. |
Kazutoshi Shinoda; Saku Sugawara; Akiko Aizawa; |
| 1522 | Exploring Faithful Rationale for Multi-Hop Fact Verification Via Salience-Aware Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Intuitively, a faithful rationale bears complementary information being able to extract other rationales through the multi-hop reasoning process. To tackle such disadvantages, we cast explainable multi-hop fact verification as subgraph extraction, which can be solved based on graph convolutional network (GCN) with salience-aware graph learning. |
Jiasheng Si; Yingjie Zhu; Deyu Zhou; |
| 1523 | A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Speaker Turn-Aware Multi-Task Adversarial Network (STMAN) for dialogue-level USE and utterance-level SA. |
Kaisong Song; Yangyang Kang; Jiawei Liu; Xurui Li; Changlong Sun; Xiaozhong Liu; |
| 1524 | A Latent-Variable Model for Intrinsic Probing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Indeed, it is natural to assume that these pre-trained representations do encode some level of linguistic knowledge as they have brought about large empirical improvements on a wide variety of NLP tasks, which suggests they are learning true linguistic generalization. In this work, we focus on intrinsic probing, an analysis technique where the goal is not only to identify whether a representation encodes a linguistic attribute but also to pinpoint where this attribute is encoded. |
Karolina Stańczak; Lucas Torroba Hennigen; Adina Williams; Ryan Cotterell; Isabelle Augenstein; |
| 1525 | Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation Via Hybrid Latent Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we also found that discrete latent variables have difficulty capturing more diverse expressions. To tackle these problems, we combine the merits of both continuous and discrete latent variables and propose a Hybrid Latent Variable (HLV) method. |
Bin Sun; Yitong Li; Fei Mi; Weichao Wang; Yiwei Li; Kan Li; |
| 1526 | ConvNTM: Conversational Neural Topic Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Conversational Neural Topic Model (ConvNTM) designed in particular for the conversational scenario. |
Hongda Sun; Quan Tu; Jinpeng Li; Rui Yan; |
| 1527 | Contrastive Learning Reduces Hallucination in Conversations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, LMs suffer from the problem of “hallucination:” they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. |
Weiwei Sun; Zhengliang Shi; Shen Gao; Pengjie Ren; Maarten de Rijke; Zhaochun Ren; |
| 1528 | Revisiting Denoising Diffusion Probabilistic Models for Speech Enhancement: Condition Collapse, Efficiency and Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For efficiency, we introduce the fast sampling technique to reduce the sampling process into several steps and exploit a refinement network to calibrate the defective speech. |
Wenxin Tai; Fan Zhou; Goce Trajcevski; Ting Zhong; |
| 1529 | SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a new multi-image document VQA dataset, SlideVQA, containing 2.6k+ slide decks composed of 52k+ slide images and 14.5k questions about a slide deck. |
Ryota Tanaka; Kyosuke Nishida; Kosuke Nishida; Taku Hasegawa; Itsumi Saito; Kuniko Saito; |
| 1530 | Reducing Sentiment Bias in Pre-trained Sentiment Classification Via Adaptive Gumbel Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an adaptive Gumbel-attacked classifier that immunes sentiment bias from an adversarial-attack perspective. |
Jiachen Tian; Shizhan Chen; Xiaowang Zhang; Xin Wang; Zhiyong Feng; |
| 1531 | Latent Constraints on Unsupervised Text-Graph Alignment with Information Asymmetry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenge posed by information asymmetry, we propose the assumption that asymmetric information is encoded in unobservable latent variables and only affects the one-way generation processes. |
Jidong Tian; Wenqing Chen; Yitian Li; Caoyun Fan; Hao He; Yaohui Jin; |
| 1532 | M-sense: Modeling Narrative Structure in Short Personal Narratives Using Protagonist’s Mental Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters’ inferred mental state along with linguistic information at the syntactic and semantic levels. |
Prashanth Vijayaraghavan; Deb Roy; |
| 1533 | Taming Continuous Posteriors for Latent Variational Dialogue Policies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success.Until now, categorical posteriors have been argued to be one of the main drivers of performance. In this work we revisit Gaussian variational posteriors for latent-action RL and show that they can yield even better performance than categoricals. |
Marin Vlastelica; Patrick Ernst; Gyuri Szarvas; |
| 1534 | Uncertainty-Aware Self-Training for Low-Resource Neural Sequence Labeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. |
Jianing Wang; Chengyu Wang; Jun Huang; Ming Gao; Aoying Zhou; |
| 1535 | Disentangled CVAEs with Contrastive Learning for Explainable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous methods struggle to interpret the input IDs of user–item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a self-regularization contrastive learning loss. |
Linlin Wang; Zefeng Cai; Gerard de Melo; Zhu Cao; Liang He; |
| 1536 | FmLRE: A Low-Resource Relation Extraction Model Based on Feature Mapping Similarity Calculation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing work takes advantages of self-training or distant supervision to expand the limited labeled data in the data-driven approaches, while the selection bias of pseudo labels may cause the error accumulation in subsequent relation classification. To address this issue, this paper proposes fmLRE, an iterative feedback method based on feature mapping similarity calculation to improve the accuracy of pseudo labels. |
Peng Wang; Tong Shao; Ke Ji; Guozheng Li; Wenjun Ke; |
| 1537 | Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A contributing factor to this problem is that NMT models trained with the one-to-one paradigm struggle to handle the source diversity phenomenon, where inputs with the same meaning can be expressed differently. In this work, we treat this problem as a bilevel optimization problem and present a consistency-aware meta-learning (CAML) framework derived from the model-agnostic meta-learning (MAML) algorithm to address it. |
Rongxiang Weng; Qiang Wang; Wensen Cheng; Changfeng Zhu; Min Zhang; |
| 1538 | Zero-Shot Face-Based Voice Conversion: Bottleneck-Free Speech Disentanglement in The Real-World Scenario Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study how a face can be converted to a voice, which is a face-based voice conversion. |
Shao-En Weng; Hong-Han Shuai; Wen-Huang Cheng; |
| 1539 | Adversarial Self-Attention for Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper advances self-attention mechanism to its robust variant for Transformer-based pre-trained language models (e.g. BERT). We propose Adversarial Self-Attention mechanism (ASA), which adversarially biases the attentions to effectively suppress the model reliance on features (e.g. specific keywords) and encourage its exploration of broader semantics. |
Hongqiu Wu; Ruixue Ding; Hai Zhao; Pengjun Xie; Fei Huang; Min Zhang; |
| 1540 | See How You Read? Multi-Reading Habits Fusion Reasoning for Multi-Modal Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome them, we propose multi-reading habits fusion reasoning networks (MRHFR) for multi-modal fake news detection. |
Lianwei Wu; Pusheng Liu; Yanning Zhang; |
| 1541 | Identify Event Causality with Knowledge and Analogy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to utilize both external knowledge and internal analogy to improve ECI. |
Sifan Wu; Ruihui Zhao; Yefeng Zheng; Jian Pei; Bang Liu; |
| 1542 | Continual Graph Convolutional Network for Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a continual GCN model (ContGCN) to generalize inferences from observed documents to unobserved documents. |
Tiandeng Wu; Qijiong Liu; Yi Cao; Yao Huang; Xiao-Ming Wu; Jiandong Ding; |
| 1543 | InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). |
Xiaobao Wu; Xinshuai Dong; Thong Nguyen; Chaoqun Liu; Liang-Ming Pan; Anh Tuan Luu; |
| 1544 | VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose VideoDubber, a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech. |
Yihan Wu; Junliang Guo; Xu Tan; Chen Zhang; Bohan Li; Ruihua Song; Lei He; Sheng Zhao; Arul Menezes; Jiang Bian; |
| 1545 | Don’t Be So Sure! Boosting ASR Decoding Via Confidence Relaxation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We perform a layer analysis to reveal and visualize how predictions evolve, and propose a decoding procedure that improves the performance of fine-tuned ASR models. |
Tomer Wullach; Shlomo E. Chazan; |
| 1546 | AMOM: Adaptive Masking Over Masking for Conditional Masked Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we further introduce a simple yet effective adaptive masking over masking strategy to enhance the refinement capability of the decoder and make the encoder optimization easier. |
Yisheng Xiao; Ruiyang Xu; Lijun Wu; Juntao Li; Tao Qin; Tie-Yan Liu; Min Zhang; |
| 1547 | Global Mixup: Eliminating Ambiguity with Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the one-stage generation paradigm and the use of linear interpolation have two defects: (1) The label of the generated sample is simply combined from the labels of the original sample pairs without reasonable judgment, resulting in ambiguous labels. (2) Linear combination significantly restricts the sampling space for generating samples. To address these issues, we propose a novel and effective augmentation method, Global Mixup, based on global clustering relationships. |
Xiangjin Xie; Li Yangning; Wang Chen; Kai Ouyang; Zuotong Xie; Hai-Tao Zheng; |
| 1548 | MoEC: Mixture of Expert Clusters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Mixture of Expert Clusters — a general approach to enable expert layers to learn more diverse and appropriate knowledge by imposing variance-based constraints on the routing stage. |
Yuan Xie; Shaohan Huang; Tianyu Chen; Furu Wei; |
| 1549 | Factual and Informative Review Generation for Explainable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by recent success in using retrieved content in addition to parametric knowledge for generation, we propose to augment the generator with a personalized retriever, where the retriever’s output serves as external knowledge for enhancing the generator. |
Zhouhang Xie; Sameer Singh; Julian McAuley; Bodhisattwa Prasad Majumder; |
| 1550 | Dialogue Rewriting Via Skeleton-Guided Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that previous dialogue rewriting approaches are neither effective nor data-efficient to resolve RealDia. |
Chunlei Xin; Hongyu Lin; Shan Wu; Xianpei Han; Bo Chen; Wen Dai; Shuai Chen; Bin Wang; Le Sun; |
| 1551 | Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing. |
Jing Xu; Dandan Song; Chong Liu; Siu Cheung Hui; Fei Li; Qiang Ju; Xiaonan He; Jian Xie; |
| 1552 | Balanced Meta Learning and Diverse Sampling for Lifelong Task-Oriented Dialogue Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-stage lifelong task-oriented dialogue generation method to mitigate catastrophic forgetting and encourage knowledge transfer simultaneously, inspired by the learning process. |
Qiancheng Xu; Min Yang; Ruifeng Xu; |
| 1553 | Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe that the noise type and speech feature vary within a sequence of speech and the local and non-local can respectively process different types of corrupted speech regions. To leverage this, we propose Selector-Enhancer, a dual-attention based convolution neural network (CNN) with a feature-filter that can dynamically select regions from low-resolution speech features and feed them to local or non-local attention operations. |
Xinmeng Xu; Weiping Tu; Yuhong Yang; |
| 1554 | A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel approach, which jointly models the cross-lingual alignment information and the mono-lingual syntax information using a graph. |
Zenan Xu; Linjun Shou; Jian Pei; Ming Gong; Qinliang Su; Xiaojun Quan; Daxin Jiang; |
| 1555 | Improving Biomedical Entity Linking with Cross-Entity Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They lack fine-grained interaction among candidates, and potentially cannot handle ambiguous mentions when facing candidates both with high lexical similarity. We cope with this issue using a re-ranking model based on prompt tuning, which represents mention context and all candidates at once, letting candidates in comparison attend to each other. |
Zhenran Xu; Yulin Chen; Baotian Hu; |
| 1556 | Nested Named Entity Recognition As Building Local Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they either require fixed recognition order or introduce complex hypergraphs. To tackle this problem, we propose a novel model named Local Hypergraph Builder Network (LHBN) that builds multiple simpler local hypergraphs to capture named entities instead of a single complex full-size hypergraph. |
Yukun Yan; Bingling Cai; Sen Song; |
| 1557 | A Domain-Transfer Meta Task Design Paradigm for Few-Shot Slot Tagging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It violates the basic principle of meta-learning which the meta task is consistent with the real testing task, leading to historical information forgetting problem. In this paper, we introduce a novel domain-transfer meta task design paradigm to tackle this problem. |
Fengyi Yang; Xi Zhou; Yating Yang; Bo Ma; Rui Dong; Abibulla Atawulla; |
| 1558 | Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While many previous solutions simply concatenate the posts into a long text and then encode the text by sequential or hierarchical models, they introduce unwarranted orders for the posts, which may mislead the models. In this paper, we propose a dynamic deep graph convolutional network (D-DGCN) to overcome the above limitation. |
Tao Yang; Jinghao Deng; Xiaojun Quan; Qifan Wang; |
| 1559 | What Does Your Face Sound Like? 3D Face Shape Towards Voice Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to address the issues, we introduce 3D face shape which (1) has an anatomical relationship between voice characteristics, partaking in the "bone conduction" of human timbre production, and (2) is naturally independent of irrelevant factors by excluding the blending process. |
Zhihan Yang; Zhiyong Wu; Ying Shan; Jia Jia; |
| 1560 | FiTs: Fine-Grained Two-Stage Training for Knowledge-Aware Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities. To address the above two problems, we propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance: The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them, named knowledge adaptive post-training. |
Qichen Ye; Bowen Cao; Nuo Chen; Weiyuan Xu; Yuexian Zou; |
| 1561 | On The Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the bad calibration of deep neural network results in various uncertainty for the single score such that the unreliable predictions always misinform user decisions. To investigate these issues, we present an efficient calibration and uncertainty estimation framework PG-DRR for dialog response retrieval models which adds a Gaussian Process layer to a deterministic deep neural network and recovers conjugacy for tractable posterior inference by Pólya-Gamma augmentation. |
Tong Ye; Shijing Si; Jianzong Wang; Ning Cheng; Zhitao Li; Jing Xiao; |
| 1562 | Preserve Context Information for Extract-Generate Long-Input Summarization Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a context-aware extract-generate framework (CAEG) for long-input text summarization. |
Ruifeng Yuan; Zili Wang; Ziqiang Cao; Wenjie Li; |
| 1563 | Transferable Post-hoc Calibration on Pretrained Transformers in Noisy Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Character or word spelling mistakes are frequently encountered in real applications and greatly threaten transformer model safety. Research on calibration under noisy settings is rare, and we focus on this direction. |
Jun Zhang; Wen Yao; Xiaoqian Chen; Ling Feng; |
| 1564 | Quantum-Inspired Representation for Long-Tail Senses of Word Sense Disambiguation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by quantum superposition states, a representation method in Hilbert space is proposed to reduce the dependence on large sample sizes and thus combat LTSs. |
Junwei Zhang; Ruifang He; Fengyu Guo; |
| 1565 | MPMQA: Multimodal Question Answering on Product Manuals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. |
Liang Zhang; Anwen Hu; Jing Zhang; Shuo Hu; Qin Jin; |
| 1566 | Exploring Self-Distillation Based Relational Reasoning Training for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to deal with the first problem, we propose a document-level RE model with a reasoning module that contains a core unit, the reasoning multi-head self-attention unit. |
Liang Zhang; Jinsong Su; Zijun Min; Zhongjian Miao; Qingguo Hu; Biao Fu; Xiaodong Shi; Yidong Chen; |
| 1567 | Multi-Action Dialog Policy Learning from Logged User Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The task is challenging since the logged user feedback provides only partial label feedback limited to the particular historical dialog actions predicted by the agent. To fully exploit such feedback information, we propose BanditMatch, which addresses the task from a feedback-enhanced semi-supervised learning perspective with a hybrid learning objective of SSL and bandit learning. |
Shuo Zhang; Junzhou Zhao; Pinghui Wang; Tianxiang Wang; Zi Liang; Jing Tao; Yi Huang; Junlan Feng; |
| 1568 | Improving End-to-End Speech Translation By Leveraging Auxiliary Speech and Text Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a method for introducing a text encoder into pre-trained end-to-end speech translation systems. |
Yuhao Zhang; Chen Xu; Bojie Hu; Chunliang Zhang; Tong Xiao; Jingbo Zhu; |
| 1569 | A Neural Span-Based Continual Named Entity Recognition Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose SpanKL, a simple yet effective Span-based model with Knowledge distillation (KD) to preserve memories and multi-Label prediction to prevent conflicts in CL-NER. |
Yunan Zhang; Qingcai Chen; |
| 1570 | Language Model Pre-training on True Negatives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, on the basis of defining the false negative issue in discriminative PrLMs that has been ignored for a long time, we design enhanced pre-training methods to counteract false negative predictions and encourage pre-training language models on true negatives by correcting the harmful gradient updates subject to false negative predictions. |
Zhuosheng Zhang; Hai Zhao; Masao Utiyama; Eiichiro Sumita; |
| 1571 | MCL: Multi-Granularity Contrastive Learning Framework for Chinese NER Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we go beyond the typical lattice structure and propose a novel Multi-Granularity Contrastive Learning framework (MCL), that aims to optimize the inter-granularity distribution distance and emphasize the critical matched words in the lexicon. |
Shan Zhao; ChengYu Wang; Minghao Hu; Tianwei Yan; Meng Wang; |
| 1572 | Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose Knowledge-Bridged Causal Interaction Network (KBCIN) with commonsense knowledge (CSK) leveraged as three bridges. |
Weixiang Zhao; Yanyan Zhao; Zhuojun Li; Bing Qin; |
| 1573 | Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such methods usually model role classification as naive multi-class classification and treat arguments individually, which neglects label semantics and interactions between arguments and thus hindering performance and generalization of models. In this paper, we propose a query-based framework named ArGument Extractor with Definitions in FrameNet (AGED) to mitigate these problems. |
Ce Zheng; Yiming Wang; Baobao Chang; |
| 1574 | Event Process Typing Via Hierarchical Optimal Transport Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle this task, existing methods mainly rely on the matching of the event process level and label level representation, which ignores two important characteristics: Process Hierarchy and Label Hierarchy. In this paper, we propose a Hierarchical Optimal Transport (HOT) method to address the above problem. |
Bo Zhou; Yubo Chen; Kang Liu; Jun Zhao; |
| 1575 | Improving Distantly Supervised Relation Extraction By Natural Language Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. |
Kang Zhou; Qiao Qiao; Yuepei Li; Qi Li; |
| 1576 | A Generative Approach for Script Event Prediction Via Contrastive Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. |
Fangqi Zhu; Jun Gao; Changlong Yu; Wei Wang; Chen Xu; Xin Mu; Min Yang; Ruifeng Xu; |
| 1577 | KPT: Keyword-Guided Pre-training for Grounded Dialog Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose KPT (Keyword-guided Pre-Training), a novel self-supervised pre-training method for grounded dialog generation without relying on extra knowledge annotation. |
Qi Zhu; Fei Mi; Zheng Zhang; Yasheng Wang; Yitong Li; Xin Jiang; Qun Liu; Xiaoyan Zhu; Minlie Huang; |
| 1578 | An Ensemble Distillation Framework for Sentence Embeddings with Multilingual Round-Trip Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel unsupervised contrastive learning framework to improve state-of-the-art sentence embeddings. |
Tianyu Zong; Likun Zhang; |
| 1579 | COSMOS: Catching Out-of-Context Image Misuse Using Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new method that automatically highlights out-of-context image and text pairs, for assisting fact-checkers. |
Shivangi Aneja; Chris Bregler; Matthias Niessner; |
| 1580 | Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. |
Chandrayee Basu; Rosni Vasu; Michihiro Yasunaga; Qian Yang; |
| 1581 | On The Challenges of Using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. |
Sumana Basu; Marc-André Legault; Adriana Romero-Soriano; Doina Precup; |
| 1582 | On The Cost of Demographic Parity in Influence Maximization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we propose to consider fairness as a notion to be guaranteed by an algorithm rather than as a criterion to be maximized. |
Ruben Becker; Gianlorenzo D’Angelo; Sajjad Ghobadi; |
| 1583 | Improving Fairness in Information Exposure By Adding Links Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This assumption may however be flawed in reality — the spreading entity may be purely efficiency-oriented. In this paper we propose to study two optimization problems with the goal to modify the network structure by adding links in such a way that efficiency-oriented information spreading becomes automatically fair. |
Ruben Becker; Gianlorenzo D’Angelo; Sajjad Ghobadi; |
| 1584 | A Fair Incentive Scheme for Community Health Workers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we partner with a NGO D-Tree International to design a fair monetary compensation scheme for tasks performed by CHWs in the semi-autonomous region of Zanzibar in Tanzania, Africa. |
Avinandan Bose; Tracey Li; Arunesh Sinha; Tien Mai; |
| 1585 | Rehabilitating Homeless: Dataset and Key Insights Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a large anonymized dataset of homelessness alongside insights into the data-driven rehabilitation of homeless people. |
Anna Bykova; Nikolay Filippov; Ivan P. Yamshchikov; |
| 1586 | Counterfactuals for The Future Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider cases where we might reasonably make a different assumption about exogenous variables; namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. |
Lucius E. J. Bynum; Joshua R. Loftus; Julia Stoyanovich; |
| 1587 | Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we design an adaptive sub-network proposer guided by a supervised contrastive loss to discriminate money laundering transactions from massive benign transactions. |
Ziwei Chai; Yang Yang; Jiawang Dan; Sheng Tian; Changhua Meng; Weiqiang Wang; Yifei Sun; |
| 1588 | Estimating Geographic Spillover Effects of COVID-19 Policies from Large-Scale Mobility Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we identify a novel setting and develop a suitable methodology that allow us to make unconfounded estimates of spillover effects of local policies. |
Serina Chang; Damir Vrabac; Jure Leskovec; Johan Ugander; |
| 1589 | Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. |
Giovanni Charles; Timothy M. Wolock; Peter Winskill; Azra Ghani; Samir Bhatt; Seth Flaxman; |
| 1590 | Leveraging Old Knowledge to Continually Learn New Classes in Medical Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on how old knowledge can be leveraged to learn new classes without catastrophic forgetting. |
Evelyn Chee; Mong Li Lee; Wynne Hsu; |
| 1591 | SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. |
Chao-Peng Chen; Jun-Wei Hsieh; Ping-Yang Chen; YI-Kuan Hsieh; Bor-Shiun Wang; |
| 1592 | Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. |
Jiahao Chen; Zitao Liu; Shuyan Huang; Qiongqiong Liu; Weiqi Luo; |
| 1593 | Critical Firms Prediction for Stemming Contagion Risk in Networked-Loans Through Graph-Based Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Regarding the issues, we propose a novel approach to predict critical firms for stemming contagion risk in the bank industry with deep reinforcement learning integrated with high-order graph message-passing networks. |
Dawei Cheng; Zhibin Niu; Jianfu Zhang; Yiyi Zhang; Changjun Jiang; |
| 1594 | GAN-Based Domain Inference Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a generative adversarial network (GAN) based method to explore likely or similar domains of a target model — the model domain inference (MDI) attack. |
Yuechun Gu; Keke Chen; |
| 1595 | Physics Guided Neural Networks for Time-Aware Fairness: An Application in Crop Yield Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a physics-guided neural network model to predict crop yield and maintain the fairness over space. |
Erhu He; Yiqun Xie; Licheng Liu; Weiye Chen; Zhenong Jin; Xiaowei Jia; |
| 1596 | “Nothing Abnormal”: Disambiguating Medical Reports Via Contrastive Knowledge Infusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the audience expectation gap in healthcare and summarize common ambiguities that lead patients to be confused about their diagnosis into three categories: medical jargon, contradictory findings, and misleading grammatical errors. |
Zexue He; An Yan; Amilcare Gentili; Julian McAuley; Chun-Nan Hsu; |
| 1597 | MTDiag: An Effective Multi-Task Framework for Automatic Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an effective multi-task framework for automatic diagnosis called MTDiag. |
Zhenyu Hou; Yukuo Cen; Ziding Liu; Dongxue Wu; Baoyan Wang; Xuanhe Li; Lei Hong; Jie Tang; |
| 1598 | Walkability Optimization: Formulations, Algorithms, and A Case Study of Toronto Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We tackle the problem of Walkability Optimization through the lens of combinatorial optimization. |
Weimin Huang; Elias B. Khalil; |
| 1599 | Low Emission Building Control with Zero-Shot Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. |
Scott Jeen; Alessandro Abate; Jonathan M. Cullen; |
| 1600 | Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most conventional works about NPP cannot model the complex spatio-temporal dependencies and congestion evolution patterns. To address these limitations, we propose a spatio-temporal graph neural point process framework, named STGNPP for traffic congestion event prediction. |
Guangyin Jin; Lingbo Liu; Fuxian Li; Jincai Huang; |
| 1601 | Taxonomizing and Measuring Representational Harms: A Look at Image Tagging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own analytic foundation. |
Jared Katzman; Angelina Wang; Morgan Scheuerman; Su Lin Blodgett; Kristen Laird; Hanna Wallach; Solon Barocas; |
| 1602 | Winning The CityLearn Challenge: Adaptive Optimization with Evolutionary Search Under Trajectory-Based Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel method using the solution function of optimization as policies to compute the actions for sequential decision-making, while notably adapting the parameters of the optimization model from online observations. |
Vanshaj Khattar; Ming Jin; |
| 1603 | Robust Planning Over Restless Groups: Engagement Interventions for A Large-Scale Maternal Telehealth Program Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Partnering with ARMMAN, we model the problem of allocating limited interventions across mothers as a restless multi-armed bandit (RMAB), where the realities of large scale and model uncertainty present key new technical challenges. |
Jackson A. Killian; Arpita Biswas; Lily Xu; Shresth Verma; Vineet Nair; Aparna Taneja; Aparna Hegde; Neha Madhiwalla; Paula Rodriguez Diaz; Sonja Johnson-Yu; Milind Tambe; |
| 1604 | Equivariant Message Passing Neural Network for Crystal Material Discovery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While existing work has shown impressive success on supervised tasks such as property prediction, the progress on unsupervised tasks such as material generation is still hampered by the limited extent to which the equivalent geometric representations of the same crystal are considered. To address this challenge, we propose EPGNN a periodic equivariant message-passing neural network that learns crystal lattice deformation in an unsupervised fashion. |
Astrid Klipfel; Zied Bouraoui; Olivier Peltre; Yaël Fregier; Najwa Harrati; Adlane Sayede; |
| 1605 | Accurate Fairness: Improving Individual Fairness Without Trading Accuracy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy. |
Xuran Li; Peng Wu; Jing Su; |
| 1606 | Point-to-Region Co-learning for Poverty Mapping at High Resolution Using Satellite Imagery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a dynamic point-to-region co-learning framework to learn heterogeneity patterns that cannot be reflected by point-level information and generalize deep learners to new areas with no labels. |
Zhili Li; Yiqun Xie; Xiaowei Jia; Kara Stuart; Caroline Delaire; Sergii Skakun; |
| 1607 | AirFormer: Predicting Nationwide Air Quality in China with Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel Transformer termed AirFormer to predict nationwide air quality in China, with an unprecedented fine spatial granularity covering thousands of locations. |
Yuxuan Liang; Yutong Xia; Songyu Ke; Yiwei Wang; Qingsong Wen; Junbo Zhang; Yu Zheng; Roger Zimmermann; |
| 1608 | SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. |
Tianci Liu; Haoyu Wang; Yaqing Wang; Xiaoqian Wang; Lu Su; Jing Gao; |
| 1609 | Human Mobility Modeling During The COVID-19 Pandemic Via Deep Graph Diffusion Infomax Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on mobility modelling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. |
Yang Liu; Yu Rong; Zhuoning Guo; Nuo Chen; Tingyang Xu; Fugee Tsung; Jia Li; |
| 1610 | Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph NeuralNetwork (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chirality-awareness, and interpretability. |
Yunchao (Lance) Liu; Yu Wang; Oanh Vu; Rocco Moretti; Bobby Bodenheimer; Jens Meiler; Tyler Derr; |
| 1611 | Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. |
Zhexiong Liu; Licheng Liu; Yiqun Xie; Zhenong Jin; Xiaowei Jia; |
| 1612 | A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. |
Feng Lu; Wei Li; Zhiqiang Zhou; Cheng Song; Yifei Sun; Yuwei Zhang; Yufei Ren; Xiaofei Liao; Hai Jin; Ailin Luo; Albert Y. Zomaya; |
| 1613 | Bugs in The Data: How ImageNet Misrepresents Biodiversity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. |
Alexandra Sasha Luccioni; David Rolnick; |
| 1614 | LUCID: Exposing Algorithmic Bias Through Inverse Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most group fairness notions assess a model’s equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. |
Carmen Mazijn; Carina Prunkl; Andres Algaba; Jan Danckaert; Vincent Ginis; |
| 1615 | Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a learning-based framework for handover parameter configuration. |
Mehrtash Mehrabi; Walid Masoudimansour; Yingxue Zhang; Jie Chuai; Zhitang Chen; Mark Coates; Jianye Hao; Yanhui Geng; |
| 1616 | Help Me Heal: A Reinforced Polite and Empathetic Mental Health and Legal Counseling Dialogue System for Crime Victims Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Further, the use of polite encoded language in the counseling reflects the nobility and creates a familiar, warm, and comfortable atmosphere to resolve human issues. Therefore, focusing on these two aspects, we propose a Polite and Empathetic Mental Health and Legal Counseling Dialogue System (Po-Em-MHLCDS) for the victims of crimes. |
Kshitij Mishra; Priyanshu Priya; Asif Ekbal; |
| 1617 | Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with A Novel Carbon-Aware Accuracy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Carburacy, the first carbon-aware accuracy measure that captures both model effectiveness and eco-sustainability. |
Gianluca Moro; Luca Ragazzi; Lorenzo Valgimigli; |
| 1618 | Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. |
Duy M. H. Nguyen; Hoang Nguyen; Truong T. N. Mai; Tri Cao; Binh T. Nguyen; Nhat Ho; Paul Swoboda; Shadi Albarqouni; Pengtao Xie; Daniel Sonntag; |
| 1619 | For The Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a first effort, we design a Chinese Heterogeneous Graph Attention (CHGAT) model to capture the heterogeneity in component relationships and incorporate the pronunciations of characters. |
Zihao Pan; Kai Peng; Shuai Ling; Haipeng Zhang; |
| 1620 | FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing works face two roadblocks: the scarcity of comprehensive and largescale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously. |
Peng Qi; Yuyan Bu; Juan Cao; Wei Ji; Ruihao Shui; Junbin Xiao; Danding Wang; Tat-Seng Chua; |
| 1621 | EINNs: Epidemiologically-Informed Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information. |
Alexander Rodríguez; Jiaming Cui; Naren Ramakrishnan; Bijaya Adhikari; B. Aditya Prakash; |
| 1622 | Counterfactual Fairness Is Basically Demographic Parity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider the celebrated definition of counterfactual fairness. |
Lucas Rosenblatt; R. Teal Witter; |
| 1623 | Detecting Anomalous Networks of Opioid Prescribers and Dispensers in Prescription Drug Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel graph-based framework for detecting anomalous opioid prescribing patterns in state Prescription Drug Monitoring Program (PDMP) data, which could aid governments in deterring opioid diversion and abuse. |
Katie Rosman; Daniel B. Neill; |
| 1624 | Practical Disruption of Image Translation Deepfake Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we propose Leaking Transferable Perturbations (LTP), an algorithm that significantly reduces the number of queries needed to disrupt an image translation network by dynamically re-purposing previous disruptions into new query efficient disruptions. |
Nataniel Ruiz; Sarah Adel Bargal; Cihang Xie; Stan Sclaroff; |
| 1625 | Daycare Matching in Japan: Transfers and Siblings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a daycare matching problem in Japan and report the design and implementation of a new centralized algorithm, which is going to be deployed in one municipality in the Tokyo metropolis. |
Zhaohong Sun; Yoshihiro Takenami; Daisuke Moriwaki; Yoji Tomita; Makoto Yokoo; |
| 1626 | City-Scale Pollution Aware Traffic Routing By Sampling Max Flows Using MCMC Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We give the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. |
Shreevignesh Suriyanarayanan; Praveen Paruchuri; Girish Varma; |
| 1627 | Weather2vec: Representation Learning for Causal Inference with Non-local Confounding in Air Pollution and Climate Studies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interference. Then, it proposes a broadly applicable framework, termed weather2vec, that uses the theory of balancing scores to learn representations of non-local information into a scalar or vector defined for each observational unit, which is subsequently used to adjust for confounding in conjunction with causal inference methods. |
Mauricio Tec; James G. Scott; Corwin M. Zigler; |
| 1628 | Evaluating Digital Agriculture Recommendations with Causal Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). |
Ilias Tsoumas; Georgios Giannarakis; Vasileios Sitokonstantinou; Alkiviadis Koukos; Dimitra Loka; Nikolaos Bartsotas; Charalampos Kontoes; Ioannis Athanasiadis; |
| 1629 | Everyone’s Voice Matters: Quantifying Annotation Disagreement Using Demographic Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Overall, we propose an innovative disagreement prediction mechanism for better design of the annotation process that will achieve more accurate and inclusive results for NLP systems. |
Ruyuan Wan; Jaehyung Kim; Dongyeop Kang; |
| 1630 | MixFairFace: Towards Ultimate Fairness Via MixFair Adapter in Face Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose MixFairFace framework to improve the fairness in face recognition models. |
Fu-En Wang; Chien-Yi Wang; Min Sun; Shang-Hong Lai; |
| 1631 | PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing mobility trajectory generation methods still require real-world human trajectories centrally collected as the training data, where there exists an inescapable risk of privacy leakage. To overcome this limitation, in this paper, we propose PateGail, a privacy-preserving imitation learning model to generate mobility trajectories, which utilizes the powerful generative adversary imitation learning model to simulate the decision-making process of humans. |
Huandong Wang; Changzheng Gao; Yuchen Wu; Depeng Jin; Lina Yao; Yong Li; |
| 1632 | Noise Based Deepfake Detection Via Multi-Head Relative-Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present a noise-based Deepfake detection model, NoiseDF for short, which focuses on the underlying forensic noise traces left behind the Deepfake videos. |
Tianyi Wang; Kam Pui Chow; |
| 1633 | Semi-supervised Credit Card Fraud Detection Via Attribute-Driven Graph Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. |
Sheng Xiang; Mingzhi Zhu; Dawei Cheng; Enxia Li; Ruihui Zhao; Yi Ouyang; Ling Chen; Yefeng Zheng; |
| 1634 | Privacy-Preserved Evolutionary Graph Modeling Via Gromov-Wasserstein Autoregression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed a Gromov-Wasserstein Autoregressive (GWAR) model to capture the generative mechanisms of evolutionary graphs, which does not require the correspondence information and thus preserves the privacy of the graphs’ nodes. |
Yue Xiang; Dixin Luo; Hongteng Xu; |
| 1635 | Auto-CM: Unsupervised Deep Learning for Satellite Imagery Composition and Cloud Masking Using Spatio-Temporal Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an autonomous image composition and masking (Auto-CM) framework to learn to solve the fundamental tasks in a label-free manner, by leveraging different dynamics of events in both geographic domains and time-series. |
Yiqun Xie; Zhili Li; Han Bao; Xiaowei Jia; Dongkuan Xu; Xun Zhou; Sergii Skakun; |
| 1636 | ERASER: AdvERsArial Sensitive Element Remover for Image Privacy Preservation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although generative methods produce better images, most of them suffer from insufficiency in the frequency domain, which influences image quality. Therefore, we propose the AdvERsArial Sensitive Element Remover (ERASER) to guarantee both image privacy and image quality. |
Guang Yang; Juan Cao; Danding Wang; Peng Qi; Jintao Li; |
| 1637 | Deep Learning on A Healthy Data Diet: Finding Important Examples for Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We hence propose a general method for pruning both the factual and counterfactual examples to maximize the model’s fairness as measured by the demographic parity, equality of opportunity, and equality of odds. |
Abdelrahman Zayed; Prasanna Parthasarathi; Gonçalo Mordido; Hamid Palangi; Samira Shabanian; Sarath Chandar; |
| 1638 | On The Effectiveness of Curriculum Learning in Educational Text Scoring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering the difficult nature of this task, we argued that the performance of an ATS model could be potentially boosted by carefully selecting data of varying complexities in the training process. Therefore, we aimed to investigate the effectiveness of curriculum learning (CL) in scoring educational text. |
Zijie Zeng; Dragan Gasevic; Guangliang Chen; |
| 1639 | Censored Fairness Through Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As many have begun to work on this problem, most existing work depends on the availability of class label for the given fairness definition and algorithm which may not align with real-world usage. In this work, we study an AI fairness problem that stems from the gap between the design of a "fair" model in the lab and its deployment in the real-world. |
Wenbin Zhang; Tina Hernandez-Boussard; Jeremy Weiss; |
| 1640 | A Continual Pre-training Approach to Tele-Triaging Pregnant Women in Kenya Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reports on a collaborative effort with Jacaranda Health to develop a state-of-the-art natural language processing (NLP) framework, TRIM-AI (TRIage for Mothers using AI), which can automatically predict the emergency level (or severity of medical condition) of a pregnant mother based on the content of their SMS messages. |
Wenbo Zhang; Hangzhi Guo; Prerna Ranganathan; Jay Patel; Sathyanath Rajasekharan; Nidhi Danayak; Manan Gupta; Amulya Yadav; |
| 1641 | Future Aware Pricing and Matching for Sustainable On-Demand Ride Pooling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a novel framework that handles the pricing and matching problems together, while also considering the future impact of the pricing and matching decisions. |
Xianjie Zhang; Pradeep Varakantham; Hao Jiang; |
| 1642 | A Crowd-AI Collaborative Duo Relational Graph Learning Framework Towards Social Impact Aware Photo Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on an NSI-aware photo classification problem where the goal is to develop a novel crowd-AI collaborative learning framework that leverages online crowd workers to quantitatively estimate and effectively reduce the NSI of misclassified photos. |
Yang Zhang; Ziyi Kou; Lanyu Shang; Huimin Zeng; Zhenrui Yue; Dong Wang; |
| 1643 | People Taking Photos That Faces Never Share: Privacy Protection and Fairness Enhancement from Camera to User Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose a practical and systematic solution to invertiblely protect face information in the full-process pipeline from camera to final users. |
Junjie Zhu; Lin Gu; Xiaoxiao Wu; Zheng Li; Tatsuya Harada; Yingying Zhu; |
| 1644 | OpenMapFlow: A Library for Rapid Map Creation with Machine Learning and Remote Sensing Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To reduce the labor of producing dense prediction maps, we present OpenMapFlow—an open-source python library for rapid map creation with ML and remote sensing data. |
Ivan Zvonkov; Gabriel Tseng; Catherine Nakalembe; Hannah Kerner; |
| 1645 | Formally Verified SAT-Based AI Planning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present an executable formally verified SAT encoding of ground classical AI planning problems. |
Mohammad Abdulaziz; Friedrich Kurz; |
| 1646 | Shielding in Resource-Constrained Goal POMDPs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We take a two-step approach to the RSGO problem. |
Michal Ajdarów; Šimon Brlej; Petr Novotný; |
| 1647 | Implicit Bilevel Optimization: Differentiating Through Bilevel Optimization Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend existing single-level optimization programming approaches and thus propose Differentiating through Bilevel Optimization Programming (BiGrad) for end-to-end learning of models that use Bilevel Programming as a layer. |
Francesco Alesiani; |
| 1648 | Query-Based Hard-Image Retrieval for Object Detection at Test Time Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The problem of predicting such potential failures at test time has largely been overlooked in the literature and conventional approaches based on detection uncertainty fall short in that they are agnostic to such fine-grained characterisation of errors. In this work, we propose to reformulate the problem of finding "hard" images as a query-based hard image retrieval task, where queries are specific definitions of "hardness", and offer a simple and intuitive method that can solve this task for a large family of queries. |
Edward Ayers; Jonathan Sadeghi; John Redford; Romain Mueller; Puneet K. Dokania; |
| 1649 | Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based controller synthesis method for continuous-state models with stochastic noise and uncertain parameters. |
Thom Badings; Licio Romao; Alessandro Abate; Nils Jansen; |
| 1650 | Accelerating Inverse Learning Via Intelligent Localization with Exploratory Sampling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent. |
Sirui Bi; Victor Fung; Jiaxin Zhang; |
| 1651 | Attention-Conditioned Augmentations for Self-Supervised Anomaly Detection and Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conducted extensive validation and ablations on the benchmark of industrial images and achieved superior performance against competing methods. |
Behzad Bozorgtabar; Dwarikanath Mahapatra; |
| 1652 | Robust-by-Design Classification Via Unitary-Gradient Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a novel family of classifiers, namely Signed Distance Classifiers (SDCs), that, from a theoretical perspective, directly output the exact distance of x from the classification boundary, rather than a probability score (e.g., SoftMax). |
Fabio Brau; Giulio Rossolini; Alessandro Biondi; Giorgio Buttazzo; |
| 1653 | Ensemble-in-One: Ensemble Learning Within Random Gated Networks for Enhanced Adversarial Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Ensemble-in-One (EIO), a simple but effective method to efficiently enlarge the ensemble with a random gated network (RGN). |
Yi Cai; Xuefei Ning; Huazhong Yang; Yu Wang; |
| 1654 | Safe Reinforcement Learning Via Shielding Under Partial Observability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that a carefully integrated shield ensures safety and can improve the convergence rate and final performance of RL agents. |
Steven Carr; Nils Jansen; Sebastian Junges; Ufuk Topcu; |
| 1655 | PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the light of ever increasing stress on the modern grid infrastructure and the grid operators, this paper presents a reinforcement learning (RL) framework, PowRL, to mitigate the effects of unexpected network events, as well as reliably maintain electricity everywhere on the network at all times. |
Anandsingh Chauhan; Mayank Baranwal; Ansuma Basumatary; |
| 1656 | Two Wrongs Don’t Make A Right: Combating Confirmation Bias in Learning with Label Noise Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To sufficiently exploit information from noisy labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR)—a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. |
Mingcai Chen; Hao Cheng; Yuntao Du; Ming Xu; Wenyu Jiang; Chongjun Wang; |
| 1657 | Testing The Channels of Convolutional Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To understand and debug convolutional neural networks (CNNs) we propose techniques for testing the channels of CNNs. |
Kang Choi; Donghyun Son; Younghoon Kim; Jiwon Seo; |
| 1658 | Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new algorithm to train a robust malware detector. |
Bao Gia Doan; Shuiqiao Yang; Paul Montague; Olivier De Vel; Tamas Abraham; Seyit Camtepe; Salil S. Kanhere; Ehsan Abbasnejad; Damith C. Ranashinghe; |
| 1659 | Correct-by-Construction Reinforcement Learning of Cardiac Pacemakers from Duration Calculus Requirements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As these specifications are written in a natural language, they are not amenable for automated verification, synthesis, or reinforcement learning of pacemaker systems. This paper presents a formalization of these requirements for a dual-chamber pacemaker in \emph{duration calculus} (DC), a highly expressive real-time specification language. |
Kalyani Dole; Ashutosh Gupta; John Komp; Shankaranarayanan Krishna; Ashutosh Trivedi; |
| 1660 | SafeLight: A Reinforcement Learning Method Toward Collision-Free Traffic Signal Control Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. |
Wenlu Du; Junyi Ye; Jingyi Gu; Jing Li; Hua Wei; Guiling Wang; |
| 1661 | PatchNAS: Repairing DNNs in Deployment with Patched Network Architecture Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel network repairing framework called PatchNAS from the architecture perspective, where we freeze the pretrained DNNs and introduce a small patch network to deal with failure samples at runtime. |
Yuchu Fang; Wenzhong Li; Yao Zeng; Yang Zheng; Zheng Hu; Sanglu Lu; |
| 1662 | Similarity Distribution Based Membership Inference Attack on Person Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, we propose a novel membership inference attack method based on the inter-sample similarity distribution. |
Junyao Gao; Xinyang Jiang; Huishuai Zhang; Yifan Yang; Shuguang Dou; Dongsheng Li; Duoqian Miao; Cheng Deng; Cairong Zhao; |
| 1663 | Out-of-Distribution Detection Is Not All You Need Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we argue that OOD detection is not a well-suited framework to design efficient runtime monitors and that it is more relevant to evaluate monitors based on their ability to discard incorrect predictions. |
Joris Guerin; Kevin Delmas; Raul Ferreira; Jérémie Guiochet; |
| 1664 | Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is still largely unknown if the nature of the representations induced by the two learning paradigms is similar. We investigate this under the lens of adversarial robustness. |
Rohit Gupta; Naveed Akhtar; Ajmal Mian; Mubarak Shah; |
| 1665 | AutoCost: Evolving Intrinsic Cost for Zero-Violation Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the analysis, we propose AutoCost, a simple yet effective framework that automatically searches for cost functions that help constrained RL to achieve zero-violation performance. |
Tairan He; Weiye Zhao; Changliu Liu; |
| 1666 | Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification Under Real-World Conditions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate the robustness of our proposed approach on a real-world application for skin cancer classification and show that it facilitates safe decision-making under real-world uncertainties. |
Achim Hekler; Titus J. Brinker; Florian Buettner; |
| 1667 | Robust Training of Neural Networks Against Bias Field Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the problem of training neural networks such that they are robust against a class of smooth intensity perturbations modelled by bias fields. |
Patrick Henriksen; Alessio Lomuscio; |
| 1668 | Redactor: A Data-Centric and Individualized Defense Against Inference Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, we cannot necessarily control the labeling process and the model trainings by other parties either. In this setting, we study the problem of targeted disinformation generation where the goal is to dilute the data and thus make a model safer and more robust against inference attacks on a specific target (e.g., a person’s profile) by only inserting new data. |
Geon Heo; Steven Euijong Whang; |
| 1669 | Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we propose to upweight hard-class pair losses in model optimization, which prompts learning discriminative features from hard classes. |
Pengyue Hou; Jie Han; Xingyu Li; |
| 1670 | CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose, Code Attack, a simple yet effective black-box attack model that uses code structure to generate effective, efficient, and imperceptible adversarial code samples and demonstrates the vulnerabilities of the state-of-the-art PL models to code-specific adversarial attacks. |
Akshita Jha; Chandan K. Reddy; |
| 1671 | Formalising The Robustness of Counterfactual Explanations for Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce an abstraction framework based on interval neural networks to verify the ∆-robustness of CFXs against a possibly infinite set of changes to the model parameters, i.e., weights and biases. |
Junqi Jiang; Francesco Leofante; Antonio Rago; Francesca Toni; |
| 1672 | READ: Aggregating Reconstruction Error Into Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder. |
Wenyu Jiang; Yuxin Ge; Hao Cheng; Mingcai Chen; Shuai Feng; Chongjun Wang; |
| 1673 | Sample-Dependent Adaptive Temperature Scaling for Improved Calibration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With this insight, we base our method on the observation that different samples contribute to the calibration error by varying amounts, with some needing to increase their confidence and others needing to decrease it. Therefore, for each input, we propose to predict a different temperature value, allowing us to adjust the mismatch between confidence and accuracy at a finer granularity. |
Tom Joy; Francesco Pinto; Ser-Nam Lim; Philip H.S. Torr; Puneet K. Dokania; |
| 1674 | Heuristic Search in Dual Space for Constrained Fixed-Horizon POMDPs with Durative Actions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current extensions, such as constrained and chance-constrained POMDPs, have limitations in modeling real-world planning problems because they assume that all actions have a fixed duration. To address this issue, we propose a unified model that encompasses durative POMDP and its constrained extensions. |
Majid Khonji; Duoaa Khalifa; |
| 1675 | Iteratively Enhanced Semidefinite Relaxations for Efficient Neural Network Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an enhanced semidefinite program (SDP) relaxation to enable the tight and efficient verification of neural networks (NNs). |
Jianglin Lan; Yang Zheng; Alessio Lomuscio; |
| 1676 | A Semidefinite Relaxation Based Branch-and-Bound Method for Tight Neural Network Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel method based on semidefinite program (SDP) for the tight and efficient verification of neural networks. |
Jianglin Lan; Benedikt Brückner; Alessio Lomuscio; |
| 1677 | Robust Image Steganography: Hiding Messages in Frequency Coefficients Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an end-to-end robust steganography system based on the invertible neural network (INN). |
Yuhang Lan; Fei Shang; Jianhua Yang; Xiangui Kang; Enping Li; |
| 1678 | Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. |
Mathias Lechner; Đorđe Žikelić; Krishnendu Chatterjee; Thomas A. Henzinger; Daniela Rus; |
| 1679 | Revisiting The Importance of Amplifying Bias for Debiasing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a simple yet effective data sample selection method which removes the bias conflicting samples to construct a bias amplified dataset for training fB. |
Jungsoo Lee; Jeonghoon Park; Daeyoung Kim; Juyoung Lee; Edward Choi; Jaegul Choo; |
| 1680 | WAT: Improve The Worst-Class Robustness in Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, these methods sacrifice a good deal of the average robust accuracy. Accordingly, this paper proposes a novel framework of worst-class adversarial training and leverages no-regret dynamics to solve this problem. |
Boqi Li; Weiwei Liu; |
| 1681 | PLMmark: A Secure and Robust Black-Box Watermarking Framework for Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of the existing watermarking schemes focus on image classification tasks. The schemes designed for the textual domain lack security and reliability. Moreover, how to protect the IP of widely-used pre-trained language models (PLMs) remains a blank. To fill these gaps, we propose PLMmark, the first secure and robust black-box watermarking framework for PLMs. |
Peixuan Li; Pengzhou Cheng; Fangqi Li; Wei Du; Haodong Zhao; Gongshen Liu; |
| 1682 | Rethinking Label Refurbishment: Model Robustness Under Label Noise Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, some of these methods are still poorly understood in the presence of label noise. In this paper, we revisit four label refurbishment methods and reveal the strong connection between them. |
Yangdi Lu; Zhiwei Xu; Wenbo He; |
| 1683 | A Holistic Approach to Undesired Content Detection in The Real World Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. |
Todor Markov; Chong Zhang; Sandhini Agarwal; Florentine Eloundou Nekoul; Theodore Lee; Steven Adler; Angela Jiang; Lilian Weng; |
| 1684 | A Risk-Sensitive Approach to Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a more direct approach whereby risk-sensitive objectives, specified in terms of the cumulative distribution function (CDF) of the distribution of full-episode rewards, are optimized. |
Jared Markowitz; Ryan W. Gardner; Ashley Llorens; Raman Arora; I-Jeng Wang; |
| 1685 | Anonymization for Skeleton Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an anonymization framework based on adversarial learning to protect potential privacy leakage from the skeleton dataset. |
Saemi Moon; Myeonghyeon Kim; Zhenyue Qin; Yang Liu; Dongwoo Kim; |
| 1686 | Monitoring Model Deterioration with Explainable Uncertainty Estimation Via Non-parametric Bootstrap Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deteri- oration when target labels are not available. |
Carlos Mougan; Dan Saattrup Nielsen; |
| 1687 | Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel certification method, which is the first work to leverage a scalable approach for c-MARLs to determine actions with guaranteed certified bounds. |
Ronghui Mu; Wenjie Ruan; Leandro Soriano Marcolino; Gaojie Jin; Qiang Ni; |
| 1688 | Constrained Reinforcement Learning in Hard Exploration Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, we propose a scalable hierarchical approach for constrained RL problems that employs backward cost value functions in the context of task hierarchy and a novel intrinsic reward function in lower levels of the hierarchy to enable cost constraint enforcement. |
Pathmanathan Pankayaraj; Pradeep Varakantham; |
| 1689 | Defending from Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents Z-Mask, an effective and deterministic strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. |
Giulio Rossolini; Federico Nesti; Fabio Brau; Alessandro Biondi; Giorgio Buttazzo; |
| 1690 | Formally Verified Solution Methods for Markov Decision Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We formally verify executable algorithms for solving Markov decision processes (MDPs) in the interactive theorem prover Isabelle/HOL. |
Maximilian Schäffeler; Mohammad Abdulaziz; |
| 1691 | Improving Training and Inference of Face Recognition Models Via Random Temperature Scaling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the observation, a unified framework for uncertainty modeling and FR, Random Temperature Scaling (RTS), is proposed to learn a reliable FR algorithm. |
Lei Shang; Mouxiao Huang; Wu Shi; Yuchen Liu; Yang Liu; Wang Steven; Baigui Sun; Xuansong Xie; Yu Qiao; |
| 1692 | Task and Model Agnostic Adversarial Attack on Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It is thus important to test the vulnerability of GNNs to adversarial perturbations in a model and task-agnostic setting. In this work, we study this problem and show that Gnns remain vulnerable even when the downstream task and model are unknown. |
Kartik Sharma; Samidha Verma; Sourav Medya; Arnab Bhattacharya; Sayan Ranu; |
| 1693 | Robust Sequence Networked Submodular Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the Robust optimization for sequence Networked submodular maximization (RoseNets) problem. |
Qihao Shi; Bingyang Fu; Can Wang; Jiawei Chen; Sheng Zhou; Yan Feng; Chun Chen; |
| 1694 | Safe Policy Improvement for POMDPs Via Finite-State Controllers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies. |
Thiago D. Simão; Marnix Suilen; Nils Jansen; |
| 1695 | STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation. |
Nikhil Kumar Singh; Indranil Saha; |
| 1696 | Understanding and Enhancing Robustness of Concept-Based Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we first propose and analyze different malicious attacks to evaluate the security vulnerability of concept based models. Subsequently, we propose a potential general adversarial training-based defense mechanism to increase robustness of these systems to the proposed malicious attacks. |
Sanchit Sinha; Mengdi Huai; Jianhui Sun; Aidong Zhang; |
| 1697 | Misspecification in Inverse Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a mathematical analysis of how robust different IRL models are to misspecification, and answer precisely how the demonstrator policy may differ from each of the standard models before that model leads to faulty inferences about the reward function R. |
Joar Skalse; Alessandro Abate; |
| 1698 | Planning and Learning for Non-markovian Negative Side Effects Using Finite State Controllers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). |
Aishwarya Srivastava; Sandhya Saisubramanian; Praveen Paruchuri; Akshat Kumar; Shlomo Zilberstein; |
| 1699 | Toward Robust Uncertainty Estimation with Random Activation Functions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel approach for uncertainty quantification via ensembles, called Random Activation Functions (RAFs) Ensemble, that aims at improving the ensemble diversity toward a more robust estimation, by accommodating each neural network with a different (random) activation function. |
Yana Stoyanova; Soroush Ghandi; Maryam Tavakol; |
| 1700 | Improving Robust Fariness Via Balance Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we observed two unfair phenomena during adversarial training: different difficulties in generating adversarial examples from each class (source-class fairness) and disparate target class tendencies when generating adversarial examples (target-class fairness). |
Chunyu Sun; Chenye Xu; Chengyuan Yao; Siyuan Liang; Yichao Wu; Ding Liang; Xianglong Liu; Aishan Liu; |
| 1701 | DPAUC: Differentially Private AUC Computation in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. |
Jiankai Sun; Xin Yang; Yuanshun Yao; Junyuan Xie; Di Wu; Chong Wang; |
| 1702 | Conflicting Interactions Among Protection Mechanisms for Machine Learning Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we first provide a framework for analyzing such conflicting interactions. We then focus on systematically analyzing pairwise interactions between protection mechanisms for one concern, model and data ownership verification, with two other classes of ML protection mechanisms: differentially private training, and robustness against model evasion. |
Sebastian Szyller; N. Asokan; |
| 1703 | Neural Policy Safety Verification Via Predicate Abstraction: CEGAR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This involves dealing with a new source of spuriousness in abstract unsafe paths, pertaining not to transition behavior but to the decisions of the neural network pi. We introduce two methods tackling this issue based on the states involved, and we show that global SMT calls deciding spuriousness exactly can be avoided. |
Marcel Vinzent; Siddhant Sharma; Jöerg Hoffmann; |
| 1704 | Towards Verifying The Geometric Robustness of Large-Scale Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper aims to verify the robustness of large-scale DNNs against the combination of multiple geometric transformations with a provable guarantee. |
Fu Wang; Peipei Xu; Wenjie Ruan; Xiaowei Huang; |
| 1705 | Revisiting Item Promotion in GNN-Based Collaborative Filtering: A Masked Targeted Topological Attack Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Fundamentally different from existing attacks, this work revisits the item promotion task and reformulates it from a targeted topological attack perspective for the first time. |
Yongwei Wang; Yong Liu; Zhiqi Shen; |
| 1706 | Robust Average-Reward Markov Decision Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. |
Yue Wang; Alvaro Velasquez; George Atia; Ashley Prater-Bennette; Shaofeng Zou; |
| 1707 | Robust Graph Meta-Learning Via Manifold Calibration with Proxy Subgraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue, we propose a new approach for graph meta-learning that is robust against structural noise, called Proxy subgraph-based Manifold Calibration method (Pro-MC). |
Zhenzhong Wang; Lulu Cao; Wanyu Lin; Min Jiang; Kay Chen Tan; |
| 1708 | HOTCOLD Block: Fooling Thermal Infrared Detectors with A Novel Wearable Design Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the need for a physically practical and stealthy adversarial attack, we introduce HotCold Block, a novel physical attack for infrared detectors that hide persons utilizing the wearable Warming Paste and Cooling Paste. |
Hui Wei; Zhixiang Wang; Xuemei Jia; Yinqiang Zheng; Hao Tang; Shin’ichi Satoh; Zheng Wang; |
| 1709 | Beyond NaN: Resiliency of Optimization Layers in The Face of Infeasibility Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Prior work has successfully incorporated optimization layers as the last layer in neural networks for various problems, thereby allowing joint learning and planning in one neural network forward pass. In this work, we identify a weakness in such a set-up where inputs to the optimization layer lead to undefined output of the neural network. |
Wai Tuck Wong; Sarah Kinsey; Ramesha Karunasena; Thanh H. Nguyen; Arunesh Sinha; |
| 1710 | DeepGemini: Verifying Dependency Fairness for Deep Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DeepGemini, a novel fairness formal analysis technique for DNNs, which contains two key components: discriminatory sample discovery and fairness score computation. |
Xuan Xie; Fuyuan Zhang; Xinwen Hu; Lei Ma; |
| 1711 | Auditing and Robustifying COVID-19 Misinformation Datasets Via Anticontent Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the context of COVID-19 misinformation detection, we conduct an in-the-wild audit of multiple datasets and demonstrate that models trained with several prominently cited recent datasets are vulnerable to anticontent when evaluated in the wild. |
Clay H. Yoo; Ashiqur R. KhudaBukhsh; |
| 1712 | User-Oriented Robust Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Clearly, the aforementioned max-min robustness is oftentimes too conservative to satisfy user preference. Therefore, in this paper, we integrate user preference into policy learning in robust RL, and propose a novel User-Oriented Robust RL (UOR-RL) framework. |
Haoyi You; Beichen Yu; Haiming Jin; Zhaoxing Yang; Jiahui Sun; |
| 1713 | Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel two-phase method for the safe weight set computation. |
Xia Zeng; Zhengfeng Yang; Li Zhang; Xiaochao Tang; Zhenbing Zeng; Zhiming Liu; |
| 1714 | Reachability Analysis of Neural Network Control Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a verification framework for NNCS based on Lipschitzian optimisation, called DeepNNC. |
Chi Zhang; Wenjie Ruan; Peipei Xu; |
| 1715 | BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, feature restoration is commonly ignored by CNNs, which may be the reason why CNNs are ineffective for the POIR problem. Inspired by this, we propose a novel brain-inspired feature restoration network (BIFRNet) to solve the POIR problem. |
Jiahong Zhang; Lihong Cao; Qiuxia Lai; Bingyao Li; Yunxiao Qin; |
| 1716 | Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The detection of shared-nuisance OOD (SN-OOD) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for these failures and propose nuisance-aware OOD detection to address them. |
Lily H. Zhang; Rajesh Ranganath; |
| 1717 | Evaluating Model-Free Reinforcement Learning Toward Safety-Critical Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics. In this paper, we revisit prior work in this scope from the perspective of state-wise safe RL and categorize them as projection-based, recovery-based, and optimization-based approaches, respectively. |
Linrui Zhang; Qin Zhang; Li Shen; Bo Yuan; Xueqian Wang; Dacheng Tao; |
| 1718 | Video-Audio Domain Generalization Via Confounder Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a DeVADG framework that conducts uni-modal and cross-modal deconfounding through back-door adjustment. |
Shengyu Zhang; Xusheng Feng; Wenyan Fan; Wenjing Fang; Fuli Feng; Wei Ji; Shuo Li; Li Wang; Shanshan Zhao; Zhou Zhao; Tat-Seng Chua; Fei Wu; |
| 1719 | Rethinking Safe Control in The Presence of Self-Seeking Humans Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper uses an interaction-based payoff structure from evolutionary game theory to model humans’ short-sighted, self-seeking behaviors. |
Zixuan Zhang; Maitham AL-Sunni; Haoming Jing; Hirokazu Shirado; Yorie Nakahira; |
| 1720 | Towards Safe AI: Sandboxing DNNs-Based Controllers in Stochastic Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a construction scheme for a so-called Safe-visor architecture to sandbox DNNs-based controllers. |
Bingzhuo Zhong; Hongpeng Cao; Majid Zamani; Marco Caccamo; |