Paper Digest: AAAI 2021 Highlights
Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords. The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2021, it is to be held online.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper. For users searching for papers/patents/grants with highlights, related papers, patents, grants, experts and organizations, please try our search console. We also provide an exclusive professor search service to find more than 400K professors across the US using their research work.
If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest service to get updates on new papers published in your area every day. You are also welcome to follow us on Twitter and Linkedin to get updated with new conference digests.
Paper Digest Team
team@paperdigest.org
TABLE 1: Paper Digest: AAAI 2021 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. |
Longyuan Li; Jihai Zhang; Junchi Yan; Yaohui Jin; Yunhao Zhang; Yanjie Duan; Guangjian Tian; |
| 2 | Bayesian Distributional Policy Gradients Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Previous works in distributional RL focused mainly on computing the state-action-return distributions, here we model the state-return distributions. |
Luchen Li; A. Aldo Faisal; |
| 3 | Learning Graph Neural Networks with Approximate Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The first provably efficient algorithm for learning graph neural networks (GNNs) with one hidden layer for node information convolution is provided in this paper. |
Qunwei Li; Shaofeng Zou; Wenliang Zhong; |
| 4 | Multi-View Representation Learning with Manifold Smoothness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the manifold smoothness into multi-view representation learning and propose MvDGAT which learns the representation and the intrinsic manifold simultaneously with graph attention network. |
Shu Li; Wei Wang; Wen-Tao Li; Pan Chen; |
| 5 | Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a simple yet effective method, namely Bi-Classifier Determinacy Maximization (BCDM), to tackle this problem. |
Shuang Li; Fangrui Lv; Binhui Xie; Chi Harold Liu; Jian Liang; Chen Qin; |
| 6 | Sublinear Classical and Quantum Algorithms for General Matrix Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. |
Tongyang Li; Chunhao Wang; Shouvanik Chakrabarti; Xiaodi Wu; |
| 7 | A Free Lunch for Unsupervised Domain Adaptive Object Detection Without Source Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. |
Xianfeng Li; Weijie Chen; Di Xie; Shicai Yang; Peng Yuan; Shiliang Pu; Yueting Zhuang; |
| 8 | Improving Adversarial Robustness Via Probabilistically Compact Loss with Logit Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Many methods have been proposed to improve adversarial robustness (e.g., adversarial training and new loss functions to learn adversarially robust feature representations). |
Xin Li; Xiangrui Li; Deng Pan; Dongxiao Zhu; |
| 9 | MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present MFES-HB, an efficient Hyperband method that is capable of utilizing both the high-fidelity and low-fidelity measurements to accelerate the convergence of HPO tasks. |
Yang Li; Yu Shen; Jiawei Jiang; Jinyang Gao; Ce Zhang; Bin Cui; |
| 10 | Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a residual structure and theoretical guarantees. |
Yangyang Li; Lin Kong; Fanhua Shang; Yuanyuan Liu; Hongying Liu; Zhouchen Lin; |
| 11 | One-shot Graph Neural Architecture Search with Dynamic Search Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel dynamic one-shot search space for multi-branch neural architectures of GNNs. |
Yanxi Li; Zean Wen; Yunhe Wang; Chang Xu; |
| 12 | Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we start with a two-stream decoupled design of encoder-decoder structure, in which two decoupled cross-modal encoder and decoder are involved to separately perform each type of proxy tasks, for simultaneous VL understanding and generation pretraining. |
Yehao Li; Yingwei Pan; Ting Yao; Jingwen Chen; Tao Mei; |
| 13 | Online Optimal Control with Affine Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose Online Gradient Descent with Buffer Zones (OGD-BZ). |
Yingying Li; Subhro Das; Na Li; |
| 14 | TRQ: Ternary Neural Networks With Residual Quantization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a stem-residual framework which provides new insight into Ternary quantization, termed Residual Quantization (TRQ), to achieve more powerful TNNs. |
Yue Li; Wenrui Ding; Chunlei Liu; Baochang Zhang; Guodong Guo; |
| 15 | Contrastive Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. |
Yunfan Li; Peng Hu; Zitao Liu; Dezhong Peng; Joey Tianyi Zhou; Xi Peng; |
| 16 | Longitudinal Deep Kernel Gaussian Process Regression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. |
Junjie Liang; Yanting Wu; Dongkuan Xu; Vasant G Honavar; |
| 17 | Large Norms of CNN Layers Do Not Hurt Adversarial Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on our theorem, we propose a novel regularization method termed norm decay, which can effectively reduce the norms of convolutional layers and fully-connected layers. |
Youwei Liang; Dong Huang; |
| 18 | Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this challenge, in this paper we propose doubly residual neural (DRN) decoder. |
Siyu Liao; Chunhua Deng; Miao Yin; Bo Yuan; |
| 19 | From Label Smoothing to Label Relaxation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As an alternative, we propose a generalized technique called label relaxation, in which the target is a set of probabilities represented in terms of an upper probability distribution. |
Julian Lienen; Eyke Hüllermeier; |
| 20 | Sample Selection for Universal Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a scoring scheme that is effective in identifying the samples of the shared classes. |
Omri Lifshitz; Lior Wolf; |
| 21 | Class-Attentive Diffusion Network for Semi-Supervised Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. |
Jongin Lim; Daeho Um; Hyung Jin Chang; Dae Ung Jo; Jin Young Choi; |
| 22 | Auto-Encoding Transformations in Reparameterized Lie Groups for Unsupervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Particularly, we focus on homographies, a general group of planar transformations containing the Euclidean, similarity and affine transformations as its special cases. |
Feng Lin; Haohang Xu; Houqiang Li; Hongkai Xiong; Guo-Jun Qi; |
| 23 | Multi-Proxy Wasserstein Classifier for Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we adopt optimal transport theory to calculate a non-uniform matching flow between the elements in the feature map of a sample and the proxies of a class in a closed way. |
Benlin Liu; Yongming Rao; Jiwen Lu; Jie Zhou; Cho-Jui Hsieh; |
| 24 | TransTailor: Pruning The Pre-trained Model for Improved Transfer Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose TransTailor, targeting at pruning the pre-trained model for improved transfer learning. |
Bingyan Liu; Yifeng Cai; Yao Guo; Xiangqun Chen; |
| 25 | Learning A Few-shot Embedding Model with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The objective of this paper is to repurpose the contrastive learning for such matching to learn a few-shot embedding model. |
Chen Liu; Yanwei Fu; Chengming Xu; Siqian Yang; Jilin Li; Chengjie Wang; Li Zhang; |
| 26 | Unchain The Search Space with Hierarchical Differentiable Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation, in this paper, we propose a Hierarchical Differentiable Architecture Search (H-DAS) that performs architecture search both at the cell level and at the stage level. |
Guanting Liu; Yujie Zhong; Sheng Guo; Matthew R. Scott; Weilin Huang; |
| 27 | Overcoming Catastrophic Forgetting in Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel scheme dedicated to overcoming catastrophic forgetting problem and hence strengthen continual learning in GNNs. |
Huihui Liu; Yiding Yang; Xinchao Wang; |
| 28 | Stable Adversarial Learning Under Distributional Shifts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct differentiated robustness optimization, where covariates are differentiated according to the stability of their correlations with the target. |
Jiashuo Liu; Zheyan Shen; Peng Cui; Linjun Zhou; Kun Kuang; Bo Li; Yishi Lin; |
| 29 | Hierarchical Multiple Kernel Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a brief insight of the aforementioned issue and propose a hierarchical approach to perform clustering while preserving advantageous details maximumly. |
Jiyuan Liu; Xinwang Liu; Siwei Wang; Sihang Zhou; Yuexiang Yang; |
| 30 | Dynamically Grown Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. |
Lanlan Liu; Yuting Zhang; Jia Deng; Stefano Soatto; |
| 31 | FLAME: Differentially Private Federated Learning in The Shuffle Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i.e., accuracy in the curator model and strong privacy without relying on any trusted party. |
Ruixuan Liu; Yang Cao; Hong Chen; Ruoyang Guo; Masatoshi Yoshikawa; |
| 32 | Post-training Quantization with Multiple Points: Mixed Precision Without Mixed Precision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers; this is in contrast to typical quantization methods that approximate each weight using a single low precision number. |
Xingchao Liu; Mao Ye; Dengyong Zhou; Qiang Liu; |
| 33 | Train A One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a simple unsupervised visual representation learning method with a pretext task of discriminating all images in a dataset using a parametric, instance-level classifier. |
Yu Liu; Lianghua Huang; Pan Pan; Bin Wang; Yinghui Xu; Rong Jin; |
| 34 | ROSITA: Refined BERT COmpreSsion with InTegrAted Techniques Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Pre-trained language models of the BERT family have defined the state-of-the-arts in a wide range of NLP tasks. |
Yuanxin Liu; Zheng Lin; Fengcheng Yuan; |
| 35 | Task Aligned Generative Meta-learning for Zero-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this regard, we propose a novel Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ), aiming to mitigate the potentially biased training and to enable meta-ZSL to accommodate real-world datasets that contain diverse distributions. |
Zhe Liu; Yun Li; Lina Yao; Xianzhi Wang; Guodong Long; |
| 36 | Learning from EXtreme Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a selective importance sampling estimator (sIS) that operates in a significantly more favorable bias-variance regime. |
Romain Lopez; Inderjit S. Dhillon; Michael I. Jordan; |
| 37 | Improving Causal Discovery By Optimal Bayesian Network Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that optimal score-based exhaustive search is remarkably useful for causal discovery: it requires weaker conditions to guarantee asymptotic correctness, and outperforms well-known methods including PC, GES, GSP, and NOTEARS. |
Ni Y Lu; Kun Zhang; Changhe Yuan; |
| 38 | Stochastic Graphical Bandits with Adversarial Corruptions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study graphical bandits with a reward model that interpolates between the two extremes, where the rewards are overall stochastically generated but a small fraction of them can be adversarially corrupted. |
Shiyin Lu; Guanghui Wang; Lijun Zhang; |
| 39 | Stochastic Bandits with Graph Feedback in Non-Stationary Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we study stochastic bandits with graph feedback in non-stationary environments and propose algorithms with graph-dependent dynamic regret bounds. |
Shiyin Lu; Yao Hu; Lijun Zhang; |
| 40 | Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper deals with distributed reinforcement learning problems with safety constraints. |
Songtao Lu; Kaiqing Zhang; Tianyi Chen; Tamer Başar; Lior Horesh; |
| 41 | Tailoring Embedding Function to Heterogeneous Few-Shot Tasks By Global and Local Feature Adaptors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Global and Local Feature Adaptor (GLoFA), a unifying framework that tailors the instance representation to specific tasks by global and local feature adaptors. |
Su Lu; Han-Jia Ye; De-Chuan Zhan; |
| 42 | PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. |
Chuan Luo; Pu Zhao; Chen Chen; Bo Qiao; Chao Du; Hongyu Zhang; Wei Wu; Shaowei Cai; Bing He; Saravanakumar Rajmohan; Qingwei Lin; |
| 43 | Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a tighter error bound for COD whose leading term considers the potential approximate low-rank structure and the correlation of input matrices. |
Luo Luo; Cheng Chen; Guangzeng Xie; Haishan Ye; |
| 44 | Semi-supervised Medical Image Segmentation Through Dual-task Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To answer this question, we propose a novel dual-task-consistency semi-supervised framework for the first time. |
Xiangde Luo; Jieneng Chen; Tao Song; Guotai Wang; |
| 45 | Adaptive Knowledge Driven Regularization for Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explicitly take into account the interaction between connected neurons, and propose an adaptive internal knowledge driven regularization method, CORR-Reg. |
Zhaojing Luo; Shaofeng Cai; Can Cui; Beng Chin Ooi; Yang Yang; |
| 46 | Multi-Domain Multi-Task Rehearsal for Lifelong Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i.e., biased … |
Fan Lyu; Shuai Wang; Wei Feng; Zihan Ye; Fuyuan Hu; Song Wang; |
| 47 | On The Adequacy of Untuned Warmup for Adaptive Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we refute this analysis and provide an alternative explanation for the necessity of warmup based on the magnitude of the update term, which is of greater relevance to training stability. |
Jerry Ma; Denis Yarats; |
| 48 | Learning Representations for Incomplete Time Series Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper pro- poses a novel unsupervised temporal representation learning model, named Clustering Representation Learning on Incom- plete time-series data (CRLI). |
Qianli Ma; Chuxin Chen; Sen Li; Garrison W. Cottrell; |
| 49 | Joint-Label Learning By Dual Augmentation for Time Series Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Joint-label learning by Dual Augmentation (JobDA), which can enrich the training samples without expanding the distribution of the original data. |
Qianli Ma; Zhenjing Zheng; Jiawei Zheng; Sen Li; Wanqing Zhuang; Garrison W. Cottrell; |
| 50 | Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an unsupervised approach, coined OTCoarsening, with the use of optimal transport. |
Tengfei Ma; Jie Chen; |
| 51 | Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. |
Yuzhe Ma; Jon A Sharp; Ruizhe Wang; Earlence Fernandes; Xiaojin Zhu; |
| 52 | Exact Reduction of Huge Action Spaces in General Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we address the large action-space problem by sequentializing actions, which can reduce the action-space size significantly, even down to two actions at the expense of an increased planning horizon. |
Sultan J. Majeed; Marcus Hutter; |
| 53 | Composite Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a new procedure called Composite Adversarial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms and their hyper-parameters from a candidate pool of 32 base attackers. |
Xiaofeng Mao; Yuefeng Chen; Shuhui Wang; Hang Su; Yuan He; Hui Xue; |
| 54 | Deep Mutual Information Maximin for Cross-Modal Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel deep mutual information maximin (DMIM) method for cross-modal clustering is proposed to maximally preserve the shared information of multiple modalities while eliminating the superfluous information of individual modalities in an end-to-end manner. |
Yiqiao Mao; Xiaoqiang Yan; Qiang Guo; Yangdong Ye; |
| 55 | Searching for Machine Learning Pipelines Using A Context-Free Grammar Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a different approach and focus on generating and optimizing pipelines of complex directed acyclic graph shapes. |
Radu Marinescu; Akihiro Kishimoto; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea; |
| 56 | Scalable Graph Networks for Particle Simulations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we introduce an approach that transforms a fully-connected interaction graph into a hierarchical one which reduces the number of edges to O(N). |
Karolis Martinkus; Aurelien Lucchi; Nathanaël Perraudin; |
| 57 | Infinite Gaussian Mixture Modeling with An Improved Estimation of The Number of Clusters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The current paper shows that the nature of this inconsistency is an overestimation, and we pinpoint that this problem is an inherent part of the training algorithm. |
Avi Matza; Yuval Bistritz; |
| 58 | Exacerbating Algorithmic Bias Through Fairness Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose new types of data poisoning attacks where an adversary intentionally targets the fairness of a system. |
Ninareh Mehrabi; Muhammad Naveed; Fred Morstatter; Aram Galstyan; |
| 59 | Physarum Powered Differentiable Linear Programming Layers and Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an efficient and differentiable solver for general linear programming problems which can be used in a plug and play manner within deep neural networks as a layer. |
Zihang Meng; Sathya N. Ravi; Vikas Singh; |
| 60 | Lenient Regret for Multi-Armed Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the Multi-Armed Bandit (MAB) problem, where an agent sequentially chooses actions and observes rewards for the actions it took. |
Nadav Merlis; Shie Mannor; |
| 61 | Policy Optimization As Online Learning with Mediator Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this observation, we propose an algorithm, RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST), for regret minimization in PO, that employs a randomized exploration strategy, differently from the existing optimistic approaches. |
Alberto Maria Metelli; Matteo Papini; Pierluca D’Oro; Marcello Restelli; |
| 62 | Consistency and Finite Sample Behavior of Binary Class Probability Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: One of our core contributions is a novel way to derive finite sample L1-convergence rates of this estimator for different surrogate loss functions. |
Alexander Mey; Marco Loog; |
| 63 | Discovering Fully Oriented Causal Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To efficiently discover causal networks in practice, we introduce the GLOBE algorithm, which greedily adds, removes, and orients edges such that it minimizes the overall cost. |
Osman A Mian; Alexander Marx; Jilles Vreeken; |
| 64 | Generative Semi-supervised Learning for Multivariate Time Series Imputation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data. |
Xiaoye Miao; Yangyang Wu; Jun Wang; Yunjun Gao; Xudong Mao; Jianwei Yin; |
| 65 | A General Class of Transfer Learning Regression Without Implementation Cost Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. |
Shunya Minami; Song Liu; Stephen Wu; Kenji Fukumizu; Ryo Yoshida; |
| 66 | Scheduling of Time-Varying Workloads Using Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Deep Reinforcement Learning (DRL) based approach to exploit various temporal resource usage patterns of time varying workloads as well as a technique for creating equivalence classes among a large number of production workloads to improve scalability of our method. |
Shanka Subhra Mondal; Nikhil Sheoran; Subrata Mitra; |
| 67 | Improved Mutual Information Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to estimate the KL divergence using a relaxed likelihood ratio estimation in a Reproducing Kernel Hilbert space. |
Youssef Mroueh; Igor Melnyk; Pierre Dognin; Jarret Ross; Tom Sercu; |
| 68 | Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we examine the problem of infusing RL agents with commonsense knowledge. |
Keerthiram Murugesan; Mattia Atzeni; Pavan Kapanipathi; Pushkar Shukla; Sadhana Kumaravel; Gerald Tesauro; Kartik Talamadupula; Mrinmaya Sachan; Murray Campbell; |
| 69 | Task-Agnostic Exploration Via Policy Gradient of A Non-Parametric State Entropy Estimate Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the entropy of the state distribution induced by finite-horizon trajectories is a sensible target. |
Mirco Mutti; Lorenzo Pratissoli; Marcello Restelli; |
| 70 | Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a general consistency condition covering communication-reduced and asynchronous distributed SGD implementations. |
Giorgi Nadiradze; Ilia Markov; Bapi Chatterjee; Vyacheslav Kungurtsev; Dan Alistarh; |
| 71 | Game of Gradients: Mitigating Irrelevant Clients in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we resolve important and related FRCS problems viz., selecting clients with relevant data, detecting clients that possess data relevant to a particular target label, and rectifying corrupted data samples of individual clients. |
Lokesh Nagalapatti; Ramasuri Narayanam; |
| 72 | Objective-Based Hierarchical Clustering of Deep Embedding Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to address the challenge of scaling up hierarchical clustering to such large datasets we propose a new practical hierarchical clustering algorithm B++&C. |
Stanislav Naumov; Grigory Yaroslavtsev; Dmitrii Avdiukhin; |
| 73 | 5* Knowledge Graph Embeddings with Projective Transformations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a novel KGE model 5*E in projective geometry, which supports multiple simultaneous transformations — specifically inversion, reflection, translation, rotation, and homothety. |
Mojtaba Nayyeri; Sahar Vahdati; Can Aykul; Jens Lehmann; |
| 74 | Advice-Guided Reinforcement Learning in A Non-Markovian Environment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we generalize both approaches and enable the user to give advice to the agent, representing the user’s best knowledge about the reward function, potentially fragmented, partial, or even incorrect. |
Daniel Neider; Jean-Raphael Gaglione; Ivan Gavran; Ufuk Topcu; Bo Wu; Zhe Xu; |
| 75 | Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on the feature-level uncertainty. |
A. Tuan Nguyen; Hyewon Jeong; Eunho Yang; Sung Ju Hwang; |
| 76 | Modular Graph Transformer Networks for Multi-Label Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a multi-label image classification framework based on graph transformer networks to fully exploit inter-label interactions. |
Hoang D. Nguyen; Xuan-Son Vu; Duc-Trong Le; |
| 77 | Differentially Private K-Means Via Exponential Mechanism and Max Cover Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new (ϵₚ, δₚ)-differentially private algorithm for the k-means clustering problem. |
Huy L. Nguyen; Anamay Chaturvedi; Eric Z Xu; |
| 78 | Minimum Robust Multi-Submodular Cover for Fairness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study a novel problem, Minimum Robust Multi-Submodular Cover for Fairness (MinRF), as follows: given a ground set V; m monotone submodular functions f_1,…,f_m; m thresholds T_1,…,T_m and a non-negative integer r; MinRF asks for the smallest set S such that f_i(S \ X) ≥ T_i for all i ∈ [m] and |X| ≤ r. |
Lan N. Nguyen; My T. Thai; |
| 79 | Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel temporal latent auto-encoder method which enables nonlinear factorization of multivariate time series, learned end-to-end with a temporal deep learning latent space forecast model. |
Nam Nguyen; Brian Quanz; |
| 80 | An Information-Theoretic Framework for Unifying Active Learning Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. |
Quoc Phong Nguyen; Bryan Kian Hsiang Low; Patrick Jaillet; |
| 81 | Top-k Ranking Bayesian Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel approach to top-k ranking Bayesian optimization (top-k ranking BO) which is a practical and significant generalization of preferential BO to handle top-k ranking and tie/indifference observations. |
Quoc Phong Nguyen; Sebastian Tay; Bryan Kian Hsiang Low; Patrick Jaillet; |
| 82 | Distributional Reinforcement Learning Via Moment Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return. |
Thanh Nguyen-Tang; Sunil Gupta; Svetha Venkatesh; |
| 83 | Precision-based Boosting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a generic refinement of all of these AdaBoost variants. |
Mohammad Hossein Nikravan; Marjan Movahedan; Sandra Zilles; |
| 84 | Improving Model Robustness By Adaptively Correcting Perturbation Levels with Active Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this observation, we propose to adaptively adjust the perturbation levels for each example in the training process. |
Kun-Peng Ning; Lue Tao; Songcan Chen; Sheng-Jun Huang; |
| 85 | Learning of Structurally Unambiguous Probabilistic Grammars Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we address the first problem. |
Dolav Nitay; Dana Fisman; Michal Ziv-Ukelson; |
| 86 | RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes RT3D, a model compression and mobile acceleration framework for 3D CNNs, seamlessly integrating neural network weight pruning and compiler code generation techniques. |
Wei Niu; Mengshu Sun; Zhengang Li; Jou-An Chen; Jiexiong Guan; Xipeng Shen; Yanzhi Wang; Sijia Liu; Xue Lin; Bin Ren; |
| 87 | Warm Starting CMA-ES for Hyperparameter Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose to transfer prior knowledge on similar HPO tasks through the initialization of the CMA-ES, leading to significantly shortening the adaptation time. |
Masahiro Nomura; Shuhei Watanabe; Youhei Akimoto; Yoshihiko Ozaki; Masaki Onishi; |
| 88 | Inverse Reinforcement Learning From Like-Minded Teachers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of learning a policy in a Markov decision process (MDP) based on observations of the actions taken by multiple teachers. |
Ritesh Noothigattu; Tom Yan; Ariel D. Procaccia; |
| 89 | Multinomial Logit Contextual Bandits: Provable Optimality and Practicality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose upper confidence bound based algorithms for this MNL contextual bandit. |
Min-hwan Oh; Garud Iyengar; |
| 90 | Learning Deep Generative Models for Queuing Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel data-driven approach towards queuing systems: the Deep Generative Service Times. |
Cesar Ojeda; Kostadin Cvejoski; Bodgan Georgiev; Christian Bauckhage; Jannis Schuecker; Ramses J. Sanchez; |
| 91 | OT-Flow: Fast and Accurate Continuous Normalizing Flows Via Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our proposed OT-Flow approach tackles two critical computational challenges that limit a more widespread use of CNFs. |
Derek Onken; Samy Wu Fung; Xingjian Li; Lars Ruthotto; |
| 92 | FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a novel learning architecture that achieves performance competitive with or better than the best existing algorithms, without requiring knowledge of the graph. |
Boris N. Oreshkin; Arezou Amini; Lucy Coyle; Mark Coates; |
| 93 | Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. |
Boris N. Oreshkin; Dmitri Carpov; Nicolas Chapados; Yoshua Bengio; |
| 94 | Augmented Experiment in Material Engineering Using Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an approach combining empirical data and domain analytical models to reduce the number of real experiments required to obtain the desired synthesis. |
Aomar Osmani; Massinissa Hamidi; Salah Bouhouche; |
| 95 | Second Order Techniques for Learning Time-series with Structural Breaks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study fundamental problems in learning nonstationary time-series: how to effectively regularize time-series models and how to adaptively tune forgetting rates. |
Takayuki Osogami; |
| 96 | Defending Against Backdoors in Federated Learning with Robust Learning Rate Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To prevent backdoor attacks, we propose a lightweight defense that requires minimal change to the FL protocol. |
Mustafa Safa Ozdayi; Murat Kantarcioglu; Yulia R. Gel; |
| 97 | Robustness Guarantees for Mode Estimation with An Application to Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give precise robustness guarantees as well as privacy guarantees under simple randomization. |
Aldo Pacchiano; Heinrich Jiang; Michael I. Jordan; |
| 98 | Disentangled Information Bottleneck Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we implement the IB method from the perspective of supervised disentangling. |
Ziqi Pan; Li Niu; Jianfu Zhang; Liqing Zhang; |
| 99 | NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K. |
Rameswar Panda; Michele Merler; Mayoore S Jaiswal; Hui Wu; Kandan Ramakrishnan; Ulrich Finkler; Chun-Fu Richard Chen; Minsik Cho; Rogerio Feris; David Kung; Bishwaranjan Bhattacharjee; |
| 100 | Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we revisit the benchmark problem of discrete-time linear quadratic regulation (LQR) and study the long-standing open question: Under what conditions is the policy iteration method robustly stable from a dynamical systems perspective? |
Bo Pang; Zhong-Ping Jiang; |
| 101 | Tempered Sigmoid Activations for Deep Learning with Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve these tradeoffs, prior work introduces variants of differential privacy that weaken the privacy guarantee proved to increase model utility. |
Nicolas Papernot; Abhradeep Thakurta; Shuang Song; Steve Chien; Úlfar Erlingsson; |
| 102 | Vector Quantized Bayesian Neural Network Inference for Data Streams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a novel model VQ-BNN, which approximates BNN inference for data streams. |
Namuk Park; Taekyu Lee; Songkuk Kim; |
| 103 | Maximum Roaming Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a novel way to partition the parameter space without weakening the inductive bias. |
Lucas Pascal; Pietro Michiardi; Xavier Bost; Benoit Huet; Maria A. Zuluaga; |
| 104 | Fast PCA in 1-D Wasserstein Spaces Via B-splines Representation and Metric Projection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel representation of the 2-Wasserstein space, based on a well known isometric bijection and a B-spline expansion. |
Matteo Pegoraro; Mario Beraha; |
| 105 | AutoDropout: Learning Dropout Patterns to Regularize Deep Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we propose to learn the dropping patterns. |
Hieu Pham; Quoc Le; |
| 106 | Fast Multi-view Discrete Clustering with Anchor Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose Fast Multi-view Discrete Clustering (FMDC) with anchor graphs, focusing on directly solving the spectral clustering problem with a small time cost. |
Qianyao Qiang; Bin Zhang; Fei Wang; Feiping Nie; |
| 107 | Relation-aware Graph Attention Model with Adaptive Self-adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. |
Xiao Qin; Nasrullah Sheikh; Berthold Reinwald; Lingfei Wu; |
| 108 | Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop techniques to control the uncertainty introduced by these estimates. |
James Queeney; Ioannis Ch. Paschalidis; Christos G. Cassandras; |
| 109 | Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length T. |
Edward Raff; William Fleshman; Richard Zak; Hyrum S. Anderson; Bobby Filar; Mark McLean; |
| 110 | Online DR-Submodular Maximization: Minimizing Regret and Constraint Violation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider online continuous DR-submodular maximization with linear stochastic long-term constraints. |
Prasanna Raut; Omid Sadeghi; Maryam Fazel; |
| 111 | Improving Generative Moment Matching Networks with Distribution Partition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new strategy to train GMMN with a low sample complexity while retaining the theoretical soundness. |
Yong Ren; Yucen Luo; Jun Zhu; |
| 112 | Multiple Kernel Clustering with Kernel K-Means Coupled Graph Tensor Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel method, kernel k-means coupled graph tensor (KCGT), is proposed to graciously couple KKM and SC for seizing their merits and evading their demerits simultaneously. |
Zhenwen Ren; Quansen Sun; Dong Wei; |
| 113 | Robust Fairness Under Covariate Shift Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. |
Ashkan Rezaei; Anqi Liu; Omid Memarrast; Brian D. Ziebart; |
| 114 | Shuffling Recurrent Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel recurrent neural network model, where the hidden state hₜ is obtained by permuting the vector elements of the previous hidden state hₜ₋₁ and adding the output of a learned function β(xₜ) of the input xₜ at time t. |
Michael Rotman; Lior Wolf; |
| 115 | Why Adversarial Interaction Creates Non-Homogeneous Patterns: A Pseudo-Reaction-Diffusion Model for Turing Instability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we establish the involvement of Turing instability to create such patterns. |
Litu Rout; |
| 116 | Adversarial Permutation Guided Node Representations for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we propose PermGNN, which aggregates neighbor features using a recurrent, order-sensitive aggregator and directly minimizes an LP loss while it is `attacked’ by adversarial generator of neighbor permutations. |
Indradyumna Roy; Abir De; Soumen Chakrabarti; |
| 117 | Visual Transfer For Reinforcement Learning Via Wasserstein Domain Confusion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Wasserstein Adversarial Proximal Policy Optimization (WAPPO), a novel algorithm for visual transfer in Reinforcement Learning that explicitly learns to align the distributions of extracted features between a source and target task. |
Josh Roy; George D. Konidaris; |
| 118 | Anytime Inference with Distilled Hierarchical Neural Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks in a hierarchical tree structure, sharing intermediate layers. |
Adria Ruiz; Jakob Verbeek; |
| 119 | Inverse Reinforcement Learning with Explicit Policy Estimates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we make previously unknown connections between these related methods from both fields. |
Navyata Sanghvi; Shinnosuke Usami; Mohit Sharma; Joachim Groeger; Kris Kitani; |
| 120 | A Deeper Look at The Hessian Eigenspectrum of Deep Neural Networks and Its Applications to Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a layerwise loss landscape analysis where the loss surface at every layer is studied independently and also on how each correlates to the overall loss surface. |
Adepu Ravi Sankar; Yash Khasbage; Rahul Vigneswaran; Vineeth N Balasubramanian; |
| 121 | AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel search strategy for one-shot and sparse propagation NAS, namely AdvantageNAS, which further reduces the time complexity of NAS by reducing the number of search iterations. |
Rei Sato; Jun Sakuma; Youhei Akimoto; |
| 122 | Active Feature Selection for The Mutual Information Criterion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explain and experimentally study the choices that we make in the algorithm, and show that they lead to a successful algorithm, compared to other more naive approaches. |
Shachar Schnapp; Sivan Sabato; |
| 123 | Learning Precise Temporal Point Event Detection with Misaligned Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in an attempt to overcome these shortcomings, we introduce a simple and versatile training paradigm combining soft localization learning with counting-based sparsity regularization. |
Julien Schroeter; Kirill Sidorov; David Marshall; |
| 124 | Multi-type Disentanglement Without Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a unified distribution-controlling method, which provides each specific style value (the value of style types, e.g., positive sentiment, or past tense) with a unique representation. |
Lei Sha; Thomas Lukasiewicz; |
| 125 | Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Uncertainty Matching GNN (UM-GNN), that is aimed at improving the robustness of GNN models, particularly against poisoning attacks to the graph structure, by leveraging epistemic uncertainties from the message passing framework. |
Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Andreas Spanias; |
| 126 | Right for Better Reasons: Training Differentiable Models By Constraining Their Influence Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Explaining black-box models such as deep neural networks is becoming increasingly important as it helps to boost trust and debugging. |
Xiaoting Shao; Arseny Skryagin; Wolfgang Stammer; Patrick Schramowski; Kristian Kersting; |
| 127 | Meta-Learning Effective Exploration Strategies for Contextual Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a meta-learning algorithm, Mêlée, that learns an exploration policy based on simulated, synthetic con- textual bandit tasks. |
Amr Sharaf; Hal Daumé III; |
| 128 | Membership Privacy for Machine Learning Models Through Knowledge Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work proposes a new defense, called distillation for membership privacy (DMP), against MIAs that preserves the utility of the resulting models significantly better than prior defenses. |
Virat Shejwalkar; Amir Houmansadr; |
| 129 | Theoretically Principled Deep RL Acceleration Via Nearest Neighbor Function Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present (1) Nearest Neighbor Actor-Critic (NNAC), an online policy gradient algorithm that demonstrates the practicality of combining function approximation with deep RL, and (2) a plug-and-play NN update module that aids the training of existing deep RL methods. |
Junhong Shen; Lin F. Yang; |
| 130 | Time Series Anomaly Detection with Multiresolution Ensemble Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple yet efficient recurrent network ensemble called Recurrent Autoencoder with Multiresolution Ensemble Decoding (RAMED). |
Lifeng Shen; Zhongzhong Yu; Qianli Ma; James T. Kwok; |
| 131 | STL-SGD: Speeding Up Local SGD with Stagewise Communication Period Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to accelerate the convergence by reducing the communication complexity, we propose STagewise Local SGD (STL-SGD), which increases the communication period gradually along with decreasing learning rate. |
Shuheng Shen; Yifei Cheng; Jingchang Liu; Linli Xu; |
| 132 | PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use partial differential operators (PDOs) to design a spherical equivariant CNN, PDO-eS2CNN, which is exactly rotation equivariant in the continuous domain. |
Zhengyang Shen; Tiancheng Shen; Zhouchen Lin; Jinwen Ma; |
| 133 | Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to transfer partial knowledge by freezing or fine-tuning particular layer(s) in the base model. |
Zhiqiang Shen; Zechun Liu; Jie Qin; Marios Savvides; Kwang-Ting Cheng; |
| 134 | Federated Multi-Armed Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite local models are perfectly known. |
Chengshuai Shi; Cong Shen; |
| 135 | Raven’s Progressive Matrices Completion with Latent Gaussian Process Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables. |
Fan Shi; Bin Li; Xiangyang Xue; |
| 136 | Improved Penalty Method Via Doubly Stochastic Gradients for Bilevel Hyperparameter Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, in this paper, we propose a doubly stochastic gradient descent algorithm (DSGPHO) to improve the efficiency of the penalty method. |
Wanli Shi; Bin Gu; |
| 137 | Online Class-Incremental Continual Learning with Adversarial Shapley Value Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. |
Dongsub Shim; Zheda Mai; Jihwan Jeong; Scott Sanner; Hyunwoo Kim; Jongseong Jang; |
| 138 | Scalable Affinity Propagation for Massive Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose a novel fast algorithm, ScaleAP, which outputs the same clusters as AP but within a shorter computation time. |
Hiroaki Shiokawa; |
| 139 | Interpretable Sequence Classification Via Discrete Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we learn sequence classifiers that favour early classification from an evolving observation trace. |
Maayan Shvo; Andrew C. Li; Rodrigo Toro Icarte; Sheila A. McIlraith; |
| 140 | Towards Domain Invariant Single Image Dehazing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we utilize an encoder-decoder based network architecture to perform the task of dehazing and integrate an spatially aware channel attention mechanism to enhance features of interest beyond the receptive field of traditional conventional kernels. |
Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim; |
| 141 | DIBS: Diversity Inducing Information Bottleneck in Model Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. |
Samarth Sinha; Homanga Bharadhwaj; Anirudh Goyal; Hugo Larochelle; Animesh Garg; Florian Shkurti; |
| 142 | Differential Spectral Normalization (DSN) for PDE Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel and robust regularization method tailored for moment-constrained convolutional filters, namely, Differential Spectral Normalization (DSN), to allow accurate estimation of coefficient functions and stable prediction of dynamics in a long time horizon. |
Chi Chiu So; Tsz On Li; Chufang Wu; Siu Pang Yung; |
| 143 | UNIPoint: Universally Approximating Point Processes Intensities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using these insights, we design and implement UNIPoint, a novel neural point process model, using recurrent neural networks to parameterise sums of basis function upon each event. |
Alexander Soen; Alexander Mathews; Daniel Grixti-Cheng; Lexing Xie; |
| 144 | Solving Common-Payoff Games with Approximate Policy Iteration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes CAPI, a novel algorithm which, like BAD, combines common knowledge with deep reinforcement learning. |
Samuel Sokota; Edward Lockhart; Finbarr Timbers; Elnaz Davoodi; Ryan D’Orazio; Neil Burch; Martin Schmid; Michael Bowling; Marc Lanctot; |
| 145 | Improving Gradient Flow with Unrolled Highway Expectation Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose Highway Expectation Maximization Networks (HEMNet), which is comprised of unrolled iterations of the generalized EM (GEM) algorithm based on the Newton-Rahpson method. |
Chonghyuk Song; Eunseok Kim; Inwook Shim; |
| 146 | Implicit Kernel Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: From this decomposition, we generalize the attention in three ways. |
Kyungwoo Song; Yohan Jung; Dongjun Kim; Il-Chul Moon; |
| 147 | Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an end-to-end training method for our ECNN, which allows further improvement of the diversity between binary classifiers. |
Yang Song; Qiyu Kang; Wee Peng Tay; |
| 148 | Hierarchical Relational Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a novel approach to physical reasoning that models objects as hierarchies of parts that may locally behave separately, but also act more globally as a single whole. |
Aleksandar Stanić; Sjoerd van Steenkiste; Jürgen Schmidhuber; |
| 149 | `Less Than One’-Shot Learning: Learning N Classes From M < N Samples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the ‘less than one’-shot learning task where models must learn N new classes given only M |
Ilia Sucholutsky; Matthias Schonlau; |
| 150 | HiABP: Hierarchical Initialized ABP for Unsupervised Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose Hierarchical Initialized Alternating Back-propagation (HiABP) for efficient Bayesian inference. |
Jiankai Sun; Rui Liu; Bolei Zhou; |
| 151 | Stability and Generalization of Decentralized Stochastic Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a novel formulation of the decentralized stochastic gradient descent. |
Tao Sun; Dongsheng Li; Bao Wang; |
| 152 | TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present two algorithms for an “online” and a “finite-model” setting respectively. |
Yanchao Sun; Xiangyu Yin; Furong Huang; |
| 153 | PAC Learning of Causal Trees with Latent Variables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a polynomial-time algorithm that PAC learns the structure and parameters of a rooted tree-structured causal network of bounded degree where the internal nodes of the tree cannot be observed or manipulated. |
Prasad Tadepalli; Stuart J. Russell; |
| 154 | Learning Dynamics Models with Stable Invariant Sets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method to ensure that a dynamics model has a stable invariant set of general classes such as limit cycles and line attractors. |
Naoya Takeishi; Yoshinobu Kawahara; |
| 155 | Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we fill the gap by improving the upper and lower bounds. |
Kei Takemura; Shinji Ito; Daisuke Hatano; Hanna Sumita; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi; |
| 156 | Explicitly Modeled Attention Maps for Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this problem, we propose a novel self-attention module with explicitly modeled attention-maps using only a single learnable parameter for low computational overhead. |
Andong Tan; Duc Tam Nguyen; Maximilian Dax; Matthias Nießner; Thomas Brox; |
| 157 | Proxy Graph Matching with Proximal Matching Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these issues, we propose a new learning-based matching framework, which is designed to be rotationally invariant. |
Hao-Ru Tan; Chuang Wang; Si-Tong Wu; Tie-Qiang Wang; Xu-Yao Zhang; Cheng-Lin Liu; |
| 158 | Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a robust representation of the firing rate to reduce the error during the conversion process. |
Weihao Tan; Devdhar Patel; Robert Kozma; |
| 159 | Empowering Adaptive Early-Exit Inference with Latency Awareness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Empirically, on top of various models across multiple datasets (CIFAR-10, CIFAR-100, ImageNet and two time-series datasets), we show that our method can well handle the average latency requirements, and consistently finds good threshold settings in negligible time. |
Xinrui Tan; Hongjia Li; Liming Wang; Xueqing Huang; Zhen Xu; |
| 160 | Foresee Then Evaluate: Decomposing Value Estimation with Latent Future Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Value Decomposition with Future Prediction (VDFP), providing an explicit two-step understanding of the value estimation process: 1) first foresee the latent future, 2) and then evaluate it. |
Hongyao Tang; Zhaopeng Meng; Guangyong Chen; Pengfei Chen; Chen Chen; Yaodong Yang; Luo Zhang; Wulong Liu; Jianye Hao; |
| 161 | Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We prove that GDA yields the optimal convergence rate in terms of output averaging, while SC-PDA derives the optimal individual convergence. |
Wei Tao; Wei Li; Zhisong Pan; Qing Tao; |
| 162 | Evolutionary Approach for AutoAugment Using The Thermodynamical Genetic Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we solved these problems by introducing evolutionary computation to previous methods. |
Akira Terauchi; Naoki Mori; |
| 163 | Semi-Supervised Knowledge Amalgamation for Sequence Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve this, KA methods combine the knowledge of multiple pre-trained teacher models (trained on different classification tasks and proprietary datasets) into one student model that becomes an expert on the union of all teachers’ classes. |
Jidapa Thadajarassiri; Thomas Hartvigsen; Xiangnan Kong; Elke A Rundensteiner; |
| 164 | Online Non-Monotone DR-Submodular Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study fundamental problems of maximizing DR-submodular continuous functions that have real-world applications in the domain of machine learning, economics, operations research and communication systems. |
Nguyễn Kim Thắng; Abhinav Srivastav; |
| 165 | Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we reveal that normal examples (NEs) are insensitive to the fluctuations occurring at the highly-curved region of the decision boundary, while AEs typically designed over one single domain (mostly spatial domain) exhibit exorbitant sensitivity on such fluctuations. |
Jinyu Tian; Jiantao Zhou; Yuanman Li; Jia Duan; |
| 166 | Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose an efficient yet general modelling approach for obtaining well-calibrated, trustworthy probabilities for samples obtained after a domain shift. |
Christian Tomani; Florian Buettner; |
| 167 | Meta Learning for Causal Direction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. |
Jean-François Ton; Dino Sejdinovic; Kenji Fukumizu; |
| 168 | Learning Compositional Sparse Gaussian Processes with A Shrinkage Prior Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast to the search-based approach, we present a novel probabilistic algorithm to learn a kernel composition by handling the sparsity in the kernel selection with Horseshoe prior. |
Anh Tong; Toan M Tran; Hung Bui; Jaesik Choi; |
| 169 | Characterizing Deep Gaussian Processes Via Nonlinear Recurrence Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new analysis in DGPs by studying its corresponding nonlinear dynamic systems to explain the issue. |
Anh Tong; Jaesik Choi; |
| 170 | Iterative Bounding MDPs: Learning Interpretable Policies Via Non-Interpretable Methods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we propose a novel Markov Decision Process (MDP) type for learning decision tree policies: Iterative Bounding MDPs (IBMDPs). |
Nicholay Topin; Stephanie Milani; Fei Fang; Manuela Veloso; |
| 171 | Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, this paper studies a model that protects the privacy of the individuals’ sensitive information while also allowing it to learn non-discriminatory predictors. |
Cuong Tran; Ferdinando Fioretto; Pascal Van Hentenryck; |
| 172 | Learning Adjustment Sets from Observational and Limited Experimental Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects for a target population. |
Sofia Triantafillou; Greg Cooper; |
| 173 | *-CFQ: Analyzing The Scalability of Machine Learning on A Compositional Task Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. |
Dmitry Tsarkov; Tibor Tihon; Nathan Scales; Nikola Momchev; Danila Sinopalnikov; Nathanael Schärli; |
| 174 | Toward Robust Long Range Policy Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method, which leverages the hierarchical structure to train the combination function and adapt the set of diverse primitive polices alternatively, to efficiently produce a range of complex behaviors on challenging new tasks. |
Wei-Cheng Tseng; Jin-Siang Lin; Yao-Min Feng; Min Sun; |
| 175 | Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find that unlike some previously studied neural network kernels, these new kernels exhibit non-trivial fixed-point dynamics which are mirrored in finite-width neural networks. |
Russell Tsuchida; Tim Pearce; Chris van der Heide; Fred Roosta; Marcus Gallagher; |
| 176 | Deep Fusion Clustering Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). |
Wenxuan Tu; Sihang Zhou; Xinwang Liu; Xifeng Guo; Zhiping Cai; En Zhu; Jieren Cheng; |
| 177 | ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework. |
Ali Burak Ünal; Mete Akgün; Nico Pfeifer; |
| 178 | Expected Eligibility Traces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce expected eligibility traces. |
Hado van Hasselt; Sephora Madjiheurem; Matteo Hessel; David Silver; André Barreto; Diana Borsa; |
| 179 | Continual General Chunking Problem and SyncMap Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal and causal structures and their continual variations. |
Danilo Vasconcellos Vargas; Toshitake Asabuki; |
| 180 | Gated Linear Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). |
Joel Veness; Tor Lattimore; David Budden; Avishkar Bhoopchand; Christopher Mattern; Agnieszka Grabska-Barwinska; Eren Sezener; Jianan Wang; Peter Toth; Simon Schmitt; Marcus Hutter; |
| 181 | GraphMix: Improved Training of GNNs for Semi-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization. |
Vikas Verma; Meng Qu; Kenji Kawaguchi; Alex Lamb; Yoshua Bengio; Juho Kannala; Jian Tang; |
| 182 | PID-Based Approach to Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the classic proportional-integral-derivative (PID) controller in the field of automatic control, we propose a new PID-based approach for generating adversarial examples. |
Chen Wan; Biaohua Ye; Fangjun Huang; |
| 183 | Nearest Neighbor Classifier Embedded Network for Active Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we attempt to replace the softmax classifier in deep neural network with a nearest neighbor classifier, considering its progressive generalization ability within the unknown sub-space. |
Fang Wan; Tianning Yuan; Mengying Fu; Xiangyang Ji; Qingming Huang; Qixiang Ye; |
| 184 | Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To accommodate this issue, this paper presents a novel GCN-based SSL algorithm which aims to enrich the supervision signals by utilizing both data similarities and graph structure. |
Sheng Wan; Shirui Pan; Jian Yang; Chen Gong; |
| 185 | Approximate Multiplication of Sparse Matrices with Limited Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to reduce the time complexity by exploiting the sparsity of the input matrices. |
Yuanyu Wan; Lijun Zhang; |
| 186 | Projection-free Online Learning in Dynamic Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, without the condition of the smoothness, we propose a novel projection-free online algorithm, and achieve an O(max{T^{2/3}V_T^{1/3},T^{1/2}}) dynamic regret bound for convex functions and an O(max{(TV_Tlog T)^{1/2},log T}) dynamic regret bound for strongly convex functions, where T is the time horizon and V_T denotes the variation of loss functions. |
Yuanyu Wan; Bo Xue; Lijun Zhang; |
| 187 | Projection-free Online Learning Over Strongly Convex Sets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that it achieves a regret bound of O(T^{2/3}) over general convex sets and a better regret bound of O(T^{1/2}) over strongly convex sets. |
Yuanyu Wan; Lijun Zhang; |
| 188 | Multi-View Information-Bottleneck Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel and flexible unsupervised multi-view representation learning model termed Collaborative Multi-View Information Bottleneck Networks (CMIB-Nets), which comprehensively explores the common latent structure and the view-specific intrinsic information, and discards the superfluous information in the data significantly improving the generalization capability of the model. |
Zhibin Wan; Changqing Zhang; Pengfei Zhu; Qinghua Hu; |
| 189 | Semi-Supervised Node Classification on Graphs: Markov Random Fields Vs. Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to address the key limitation of existing pMRF-based methods. |
Binghui Wang; Jinyuan Jia; Neil Zhenqiang Gong; |
| 190 | Quantum Exploration Algorithms for Multi-Armed Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we provide an algorithm to find the best arm with fixed confidence based on variable-time amplitude amplification and estimation. |
Daochen Wang; Xuchen You; Tongyang Li; Andrew M. Childs; |
| 191 | Learning from Noisy Labels with Complementary Loss Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a general framework to learn robust deep neural networks with complementary loss functions. |
Deng-Bao Wang; Yong Wen; Lujia Pan; Min-Ling Zhang; |
| 192 | Debiasing Evaluations That Are Biased By Evaluations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we call these external factors the "outcome" experienced by people, and consider the problem of mitigating these outcome-induced biases in the given ratings when some information about the outcome is available. |
Jingyan Wang; Ivan Stelmakh; Yuting Wei; Nihar B. Shah; |
| 193 | Enhancing Unsupervised Video Representation Learning By Decoupling The Scene and The Motion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to tackle this problem, we propose to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid. |
Jinpeng Wang; Yuting Gao; Ke Li; Jianguo Hu; Xinyang Jiang; Xiaowei Guo; Rongrong Ji; Xing Sun; |
| 194 | Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. |
Kaihong Wang; Chenhongyi Yang; Margrit Betke; |
| 195 | Embedding Heterogeneous Networks Into Hyperbolic Space Without Meta-path Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space. |
Lili Wang; Chongyang Gao; Chenghan Huang; Ruibo Liu; Weicheng Ma; Soroush Vosoughi; |
| 196 | Adversarial Linear Contextual Bandits with Graph-Structured Side Observations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: contexts and side observations. |
Lingda Wang; Bingcong Li; Huozhi Zhou; Georgios B. Giannakis; Lav R. Varshney; Zhizhen Zhao; |
| 197 | Addressing Class Imbalance in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a monitoring scheme that can infer the composition of training data for each FL round, and design a new loss function — Ratio Loss to mitigate the impact of the imbalance. |
Lixu Wang; Shichao Xu; Xiao Wang; Qi Zhu; |
| 198 | Contrastive Transformation for Self-supervised Correspondence Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. |
Ning Wang; Wengang Zhou; Houqiang Li; |
| 199 | Tackling Instance-Dependent Label Noise Via A Universal Probabilistic Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. |
Qizhou Wang; Bo Han; Tongliang Liu; Gang Niu; Jian Yang; Chen Gong; |
| 200 | Learning with Group Noise Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this issue, we propose a novel Max-Matching method for learning with group noise. |
Qizhou Wang; Jiangchao Yao; Chen Gong; Tongliang Liu; Mingming Gong; Hongxia Yang; Bo Han; |
| 201 | Adaptive Verifiable Training Using Pairwise Class Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new approach that utilizes inter-class similarity to improve the performance of verifiable training and create robust models with respect to multiple adversarial criteria. |
Shiqi Wang; Kevin Eykholt; Taesung Lee; Jiyong Jang; Ian Molloy; |
| 202 | Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose adaptive algorithms for both the stochastic and the adversarial cases, without requiring any prior information about the reward interval. |
Siwei Wang; Haoyun Wang; Longbo Huang; |
| 203 | Harmonized Dense Knowledge Distillation Training for Multi-Exit Architectures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel Harmonized Dense Knowledge Distillation (HDKD) training method for multi-exit architecture is designed to encourage each exit to flexibly learn from all its later exits. |
Xinglu Wang; Yingming Li; |
| 204 | Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Tied Block Convolution (TBC) that shares the same thinner filter over equal blocks of channels and produces multiple responses with a single filter. |
Xudong Wang; Stella X. Yu; |
| 205 | Deep Recurrent Belief Propagation Network for POMDPs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new method that lies somewhere in the middle of the spectrum of research methodology identified above and combines the strength of both approaches. |
Yuhui Wang; Xiaoyang Tan; |
| 206 | Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a novel data-free KD approach by modeling the intermediate feature space of the teacher with a multivariate normal distribution and leveraging the soft targeted labels generated by the distribution to synthesize pseudo samples as the transfer set. |
Zi Wang; |
| 207 | Incremental Embedding Learning Via Zero-Shot Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI), which leverages zero-shot translation to estimate and compensate the semantic gap without any exemplars. |
Kun Wei; Cheng Deng; Xu Yang; Maosen Li; |
| 208 | Gene Regulatory Network Inference As Relaxed Graph Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. |
Deborah Weighill; Marouen Ben Guebila; Camila Lopes-Ramos; Kimberly Glass; John Quackenbush; John Platig; Rebekka Burkholz; |
| 209 | Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method, referred to as incomplete multi-view tensor spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging multi-view clustering problem with missing views. |
Jie Wen; Zheng Zhang; Zhao Zhang; Lei Zhu; Lunke Fei; Bob Zhang; Yong Xu; |
| 210 | Learning Set Functions That Are Sparse in Non-Orthogonal Fourier Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a new family of algorithms for learning Fourier-sparse set functions. |
Chris Wendler; Andisheh Amrollahi; Bastian Seifert; Andreas Krause; Markus Püschel; |
| 211 | BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we give a thorough analysis of the "BO + neural predictor framework" by identifying five main components: the architecture encoding, neural predictor, uncertainty calibration method, acquisition function, and acquisition function optimization. |
Colin White; Willie Neiswanger; Yash Savani; |
| 212 | Peer Collaborative Learning for Online Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Peer Collaborative Learning method for online knowledge distillation, which integrates online ensembling and network collaboration into a unified framework. |
Guile Wu; Shaogang Gong; |
| 213 | Self-Supervised Attention-Aware Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use visual attention as an inductive bias for RL agents. |
Haiping Wu; Khimya Khetarpal; Doina Precup; |
| 214 | Training Spiking Neural Networks with Accumulated Spiking Flow Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new backpropagation method for SNNs based on the accumulated spiking flow (ASF), i.e. ASF-BP. |
Hao Wu; Yueyi Zhang; Wenming Weng; Yongting Zhang; Zhiwei Xiong; Zheng-Jun Zha; Xiaoyan Sun; Feng Wu; |
| 215 | Fast and Scalable Adversarial Training of Kernel SVM Via Doubly Stochastic Gradients Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim at kernel SVM and propose adv-SVM to improve its adversarial robustness via adversarial training, which has been demonstrated to be the most promising defense techniques. |
Huimin Wu; Zhengmian Hu; Bin Gu; |
| 216 | Fine-grained Generalization Analysis of Vector-Valued Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. |
Liang Wu; Antoine Ledent; Yunwen Lei; Marius Kloft; |
| 217 | Frugal Optimization for Cost-related Hyperparameters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we develop a new cost-frugal HPO solution. |
Qingyun Wu; Chi Wang; Silu Huang; |
| 218 | Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we focus on two possible areas of improvement of the state of the art. |
Ruiyuan Wu; Anna Scaglione; Hoi-To Wai; Nurullah Karakoc; Kari Hreinsson; Wing-Kin Ma; |
| 219 | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction. |
Tongtong Wu; Xuekai Li; Yuan-Fang Li; Gholamreza Haffari; Guilin Qi; Yujin Zhu; Guoqiang Xu; |
| 220 | Fractal Autoencoders for Feature Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). |
Xinxing Wu; Qiang Cheng; |
| 221 | Neural Architecture Search As Sparse Supernet Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. |
Yan Wu; Aoming Liu; Zhiwu Huang; Siwei Zhang; Luc Van Gool; |
| 222 | Learning to Purify Noisy Labels Via Meta Soft Label Corrector Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a meta-learning model, aiming at attaining an automatic scheme which can estimate soft labels through meta-gradient descent step under the guidance of a small amount of noise-free meta data. |
Yichen Wu; Jun Shu; Qi Xie; Qian Zhao; Deyu Meng; |
| 223 | Near-Optimal MNL Bandits Under Risk Criteria Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We design algorithms for a broad class of risk criteria, including but not limited to the well-known conditional value-at-risk, Sharpe ratio, and entropy risk, and prove that they suffer a near-optimal regret. |
Guangyu Xi; Chao Tao; Yuan Zhou; |
| 224 | Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to fill the gap of decentralized quantized constrained optimization, we propose a novel communication-efficient Decentralized Quantized Stochastic Frank-Wolfe (DQSFW) algorithm for non-convex constrained learning models. |
Wenhan Xian; Feihu Huang; Heng Huang; |
| 225 | Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of interest. |
Ziyu Xiang; Mingzhou Fan; Guillermo Vázquez Tovar; William Trehern; Byung-Jun Yoon; Xiaofeng Qian; Raymundo Arroyave; Xiaoning Qian; |
| 226 | Distant Transfer Learning Via Deep Random Walk Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study distant transfer learning by proposing a DeEp Random Walk basEd distaNt Transfer (DERWENT) method. |
Qiao Xiao; Yu Zhang; |
| 227 | Learning Cycle-Consistent Cooperative Networks Via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy-based model and a latent variable model. |
Jianwen Xie; Zilong Zheng; Xiaolin Fang; Song-Chun Zhu; Ying Nian Wu; |
| 228 | Learning Energy-Based Model with Variational Auto-Encoder As Amortized Sampler Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to learn a variational auto-encoder (VAE) to initialize the finite-step MCMC, such as Langevin dynamics that is derived from the energy function, for efficient amortized sampling of the EBM. |
Jianwen Xie; Zilong Zheng; Ping Li; |
| 229 | Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage. |
Jinwei Xing; Takashi Nagata; Kexin Chen; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar; |
| 230 | Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms Under Markovian Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our study develops new techniques for analyzing the Adam-type RL algorithms under Markovian sampling. |
Huaqing Xiong; Tengyu Xu; Yingbin Liang; Wei Zhang; |
| 231 | Variational Disentanglement for Rare Event Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. |
Zidi Xiu; Chenyang Tao; Michael Gao; Connor Davis; Benjamin A. Goldstein; Ricardo Henao; |
| 232 | Step-Ahead Error Feedback for Distributed Training with Compressed Gradient Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this critical problem, we propose two novel techniques, 1) step ahead and 2) error averaging, with rigorous theoretical analysis. |
An Xu; Zhouyuan Huo; Heng Huang; |
| 233 | Isolation Graph Kernel Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces an alternative called Isolation Graph Kernel (IGK) that measures the similarity between two attributed graphs. |
Bi-Cun Xu; Kai Ming Ting; Yuan Jiang; |
| 234 | Multi-Task Recurrent Modular Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose multi-task recurrent modular networks (MT-RMN) that can be incorporated in any multi-task recurrent models to address the above drawbacks. |
Dongkuan Xu; Wei Cheng; Xin Dong; Bo Zong; Wenchao Yu; Jingchao Ni; Dongjin Song; Xuchao Zhang; Haifeng Chen; Xiang Zhang; |
| 235 | Learning Graphons Via Structured Gromov-Wasserstein Barycenters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. |
Hongteng Xu; Dixin Luo; Lawrence Carin; Hongyuan Zha; |
| 236 | Towards Generalized Implementation of Wasserstein Distance in GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the strong Lipschitz constraint might be unnecessary for optimization. |
Minkai Xu; |
| 237 | Towards Feature Space Adversarial Attack By Style Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new adversarial attack to Deep Neural Networks for image classification. |
Qiuling Xu; Guanhong Tao; Siyuan Cheng; Xiangyu Zhang; |
| 238 | MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extend state-of-the-art neural architecture search (NAS) methods and propose MUltimodal Fusion Architecture SeArch (MUFASA) to simultaneously search across multimodal fusion strategies and modality-specific architectures for the first time. |
Zhen Xu; David R. So; Andrew M. Dai; |
| 239 | Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we utilize the Fourier analysis to empirically provide a promising mechanism to understand why feedforward deeper learning is faster. |
Zhiqin John Xu; Hanxu Zhou; |
| 240 | Rethinking Bi-Level Optimization in Neural Architecture Search: A Gibbs Sampling Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, one-shot neural architecture search is addressed by adopting a directed probabilistic graphical model to represent the joint probability distribution over data and model. |
Chao Xue; Xiaoxing Wang; Junchi Yan; Yonggang Hu; Xiaokang Yang; Kewei Sun; |
| 241 | Toward Understanding The Influence of Individual Clients in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. |
Yihao Xue; Chaoyue Niu; Zhenzhe Zheng; Shaojie Tang; Chengfei Lyu; Fan Wu; Guihai Chen; |
| 242 | Adversarial Partial Multi-Label Learning with Label Disambiguation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial multi-label learning. |
Yan Yan; Yuhong Guo; |
| 243 | Near Lossless Transfer Learning for Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose CQ training (Clamped and Quantized training), an SNN-compatible CNN training algorithm with clamp and quantization that achieves near-zero conversion accuracy loss. |
Zhanglu Yan; Jun Zhou; Weng-Fai Wong; |
| 244 | DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning Via Adversarial Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel deep hidden backdoor (DeHiB) attack scheme for SSL-based systems. |
Zhicong Yan; Gaolei Li; Yuan TIan; Jun Wu; Shenghong Li; Mingzhe Chen; H. Vincent Poor; |
| 245 | Robust Bandit Learning with Imperfect Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study a novel contextual bandit setting in which only imperfect context is available for arm selection while the true context is revealed at the end of each round. |
Jianyi Yang; Shaolei Ren; |
| 246 | Hierarchical Graph Capsule Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we innovatively propose hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies. |
Jinyu Yang; Peilin Zhao; Yu Rong; Chaochao Yan; Chunyuan Li; Hehuan Ma; Junzhou Huang; |
| 247 | FracBits: Mixed Precision Quantization Via Fractional Bit-Widths Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. |
Linjie Yang; Qing Jin; |
| 248 | On Convergence of Gradient Expected Sarsa(λ) Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the convergence of Expected Sarsa(λ) with function approximation. |
Long Yang; Gang Zheng; Yu Zhang; Qian Zheng; Pengfei Li; Gang Pan; |
| 249 | Sample Complexity of Policy Gradient Finding Second-Order Stationary Points Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead of FOSP, we consider SOSP as the convergence criteria to characterize the sample complexity of policy gradient. |
Long Yang; Qian Zheng; Gang Pan; |
| 250 | WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel reinforcement learning algorithm called Worst-Case Soft Actor Critic, which extends the Soft Actor Critic algorithm with a safety critic to achieve risk control. |
Qisong Yang; Thiago D. Simão; Simon H Tindemans; Matthijs T. J. Spaan; |
| 251 | Characterizing The Evasion Attackability of Multi-label Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the … |
Zhuo Yang; Yufei Han; Xiangliang Zhang; |
| 252 | SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives. |
Ting Yao; Yiheng Zhang; Zhaofan Qiu; Yingwei Pan; Tao Mei; |
| 253 | ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we introduce ADAHESSIAN, a new stochastic optimization algorithm. |
Zhewei Yao; Amir Gholami; Sheng Shen; Mustafa Mustafa; Kurt Keutzer; Michael Mahoney; |
| 254 | Improving Sample Efficiency in Model-Free Reinforcement Learning from Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Following these findings, we propose effective techniques to improve training stability. |
Denis Yarats; Amy Zhang; Ilya Kostrikov; Brandon Amos; Joelle Pineau; Rob Fergus; |
| 255 | Task Cooperation for Semi-Supervised Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the idea that unlabeled data can be utilized to smooth the model space in traditional semi-supervised learning, we propose TAsk COoperation (TACO) which takes advantage of unsupervised tasks to smooth the meta-model space. |
Han-Jia Ye; Xin-Chun Li; De-Chuan Zhan; |
| 256 | Amata: An Annealing Mechanism for Adversarial Training Acceleration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to reduce the computational cost, we propose an annealing mechanism, Amata, to reduce the overhead associated with adversarial training. |
Nanyang Ye; Qianxiao Li; Xiao-Yun Zhou; Zhanxing Zhu; |
| 257 | Sequential Generative Exploration Model for Partially Observable Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel reward shaping approach to infer the intrinsic rewards for the agent from a sequential generative model. |
Haiyan Yin; Jianda Chen; Sinno Jialin Pan; Sebastian Tschiatschek; |
| 258 | Enhanced Audio Tagging Via Multi- to Single-Modal Teacher-Student Mutual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the challenges, we present a novel visual-assisted teacher-student mutual learning framework for robust sound event detection from audio recordings. |
Yifang Yin; Harsh Shrivastava; Ying Zhang; Zhenguang Liu; Rajiv Ratn Shah; Roger Zimmermann; |
| 259 | Image-to-Image Retrieval By Learning Similarity Between Scene Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks. |
Sangwoong Yoon; Woo Young Kang; Sungwook Jeon; SeongEun Lee; Changjin Han; Jonghun Park; Eun-Sol Kim; |
| 260 | Learning Interpretable Models for Coupled Networks Under Domain Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the idea of coupled networks through an optimization framework by focusing on interactions between structural edges and functional edges of brain networks. |
Hongyuan You; Sikun Lin; Ambuj Singh; |
| 261 | Identity-aware Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. |
Jiaxuan You; Jonathan M Gomes-Selman; Rex Ying; Jure Leskovec; |
| 262 | How Does Data Augmentation Affect Privacy in Machine Learning? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We establish the optimal membership inference when the model is trained with augmented data, which inspires us to formulate the MI attack as a set classification problem, i.e., classifying a set of augmented instances instead of a single data point, and design input permutation invariant features. |
Da Yu; Huishuai Zhang; Wei Chen; Jian Yin; Tie-Yan Liu; |
| 263 | DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we follow the trend to propose a novel method to reduce the domain shift using strategies of discriminator attention and self-training. |
Fei Yu; Mo Zhang; Hexin Dong; Sheng Hu; Bin Dong; Li Zhang; |
| 264 | Any-Precision Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. |
Haichao Yu; Haoxiang Li; Humphrey Shi; Thomas S. Huang; Gang Hua; |
| 265 | Personalized Adaptive Meta Learning for Cold-start User Preference Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users’ features for fast search. |
Runsheng Yu; Yu Gong; Xu He; Yu Zhu; Qingwen Liu; Wenwu Ou; Bo An; |
| 266 | Measuring Dependence with Matrix-based Entropy Functional Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we summarize and generalize the main idea of existing information-theoretic dependence measures into a higher-level perspective by the Shearer’s inequality. |
Shujian Yu; Francesco Alesiani; Xi Yu; Robert Jenssen; Jose Principe; |
| 267 | Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a la- bel generation module based on the self-supervised learning strategy to acquire independent unimodal supervisions. |
Wenmeng Yu; Hua Xu; Ziqi Yuan; Jiele Wu; |
| 268 | Knowledge-Guided Object Discovery with Acquired Deep Impressions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a framework called Acquired Deep Impressions (ADI) which continuously learns knowledge of objects as “impressions” for compositional scene understanding. |
Jinyang Yuan; Bin Li; Xiangyang Xue; |
| 269 | Curse or Redemption? How Data Heterogeneity Affects The Robustness of Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks. |
Syed Zawad; Ahsan Ali; Pin-Yu Chen; Ali Anwar; Yi Zhou; Nathalie Baracaldo; Yuan Tian; Feng Yan; |
| 270 | Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness Under Non-uniform Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a weighted minimax risk optimization that defends against non-uniform attacks, achieving robustness against adversarial examples under perturbed test data distributions. |
Huimin Zeng; Chen Zhu; Tom Goldstein; Furong Huang; |
| 271 | Contrastive Self-supervised Learning for Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose two approaches based on contrastive self-supervised learning (CSSL) to alleviate overfitting. |
Jiaqi Zeng; Pengtao Xie; |
| 272 | Data-driven Competitive Algorithms for Online Knapsack and Set Cover Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop an approach for data-driven design of online algorithms that maintain near-optimal worst-case guarantees while also performing learning in order to perform well for typical inputs. |
Ali Zeynali; Bo Sun; Mohammad Hajiesmaili; Adam Wierman; |
| 273 | A Hybrid Stochastic Gradient Hamiltonian Monte Carlo Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel SG-MCMC algorithm, called Hybrid Stochastic Gradient Hamiltonian Monte Carlo (HSG-HMC) method, which needs merely one sample per iteration and possesses a simple structure with only one hyperparameter. |
Chao Zhang; Zhijian Li; Zebang Shen; Jiahao Xie; Hui Qian; |
| 274 | CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. |
Chaoyun Zhang; Marco Fiore; Iain Murray; Paul Patras; |
| 275 | Exploration By Maximizing Renyi Entropy for Reward-Free RL Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the exploration phase, we propose to maximize the Renyi entropy over the state-action space and justify this objective theoretically. |
Chuheng Zhang; Yuanying Cai; Longbo Huang; Jian Li; |
| 276 | Efficient Folded Attention for Medical Image Reconstruction and Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images. |
Hang Zhang; Jinwei Zhang; Rongguang Wang; Qihao Zhang; Pascal Spincemaille; Thanh D. Nguyen; Yi Wang; |
| 277 | Interpreting Multivariate Shapley Interactions in DNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN. |
Hao Zhang; Yichen Xie; Longjie Zheng; Die Zhang; Quanshi Zhang; |
| 278 | Sample Efficient Reinforcement Learning with REINFORCE Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider classical policy gradient methods that compute an approximate gradient with a single trajectory or a fixed size mini-batch of trajectories under soft-max parametrization and log-barrier regularization, along with the widely-used REINFORCE gradient estimation procedure. |
Junzi Zhang; Jongho Kim; Brendan O’Donoghue; Stephen Boyd; |
| 279 | Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenging problems, we propose a novel VFL framework integrated with new backward updating mechanism and bilevel asynchronous parallel architecture (VFB^2), under which three new algorithms, including VFB^2-SGD, -SVRG, and -SAGA, are proposed. |
Qingsong Zhang; Bin Gu; Cheng Deng; Heng Huang; |
| 280 | Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variable. |
Shangtong Zhang; Bo Liu; Shimon Whiteson; |
| 281 | Deep Wasserstein Graph Discriminant Learning for Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a deep Wasserstein graph discriminant learning (WGDL) framework to learn discriminative embeddings of graphs in Wasserstein-metric (W-metric) matching space. |
Tong Zhang; Yun Wang; Zhen Cui; Chuanwei Zhou; Baoliang Cui; Haikuan Huang; Jian Yang; |
| 282 | Treatment Effect Estimation with Disentangled Latent Factors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain … |
Weijia Zhang; Lin Liu; Jiuyong Li; |
| 283 | Regret Bounds for Online Kernel Selection in Continuous Kernel Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to represent different learning frameworks of online kernel selection, we divide online kernel selection approaches in a continuous kernel space into two categories according to the order of selection and training at each round. |
Xiao Zhang; Shizhong Liao; Jun Xu; Ji-Rong Wen; |
| 284 | The Sample Complexity of Teaching By Reinforcement on Q-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on a specific family of reinforcement learning algorithms, Q-learning, and characterize the TDim under different teachers with varying control power over the environment, and present matching optimal teaching algorithms. |
Xuezhou Zhang; Shubham Bharti; Yuzhe Ma; Adish Singla; Xiaojin Zhu; |
| 285 | Partial-Label and Structure-constrained Deep Coupled Factorization Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we technically propose an enriched prior guided framework, called Dual-constrained Deep Semi-Supervised Coupled Factorization Network (DS2CF-Net), for discovering hierarchical coupled data representation. |
Yan Zhang; Zhao Zhang; Yang Wang; Zheng Zhang; Li Zhang; Shuicheng Yan; Meng Wang; |
| 286 | Memory-Gated Recurrent Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Because of the multivariate complexity in the evolution of the joint distribution that underlies the data generating process, we take a data-driven approach and construct a novel recurrent network architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates explicitly regulating two distinct types of memories: the marginal memory and the joint memory. |
Yaquan Zhang; Qi Wu; Nanbo Peng; Min Dai; Jing Zhang; Hu Wang; |
| 287 | Towards Enabling Learnware to Handle Unseen Jobs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a novel scheme that works can effectively reuse the learnwares even when the user’s job involves unseen parts. |
Yu-Jie Zhang; Yu-Hu Yan; Peng Zhao; Zhi-Hua Zhou; |
| 288 | Exploiting Unlabeled Data Via Partial Label Assignment for Multi-Class Semi-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, an intermediate unlabeled data exploitation strategy is investigated via partial label assignment, i.e. a set of candidate labels other than a single pseudo-label are assigned to the unlabeled data. |
Zhen-Ru Zhang; Qian-Wen Zhang; Yunbo Cao; Min-Ling Zhang; |
| 289 | Looking Wider for Better Adaptive Representation in Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these problems, we propose the Cross Non-Local Neural Network (CNL) for capturing the long-range dependency of the samples and the current task. |
Jiabao Zhao; Yifan Yang; Xin Lin; Jing Yang; Liang He; |
| 290 | Distilling Localization for Self-Supervised Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This is due to the fact that view generation process considers pixels in an image uniformly.To address this problem, we propose a data-driven approach for learning invariance to backgrounds. |
Nanxuan Zhao; Zhirong Wu; Rynson W.H. Lau; Stephen Lin; |
| 291 | Exploratory Machine Learning with Unknown Unknowns Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we point out that there is an important category of failure, which owes to the fact that there are unknown classes in the training data misperceived as other labels, and thus their existence was unknown from the given supervision. |
Peng Zhao; Yu-Jie Zhang; Zhi-Hua Zhou; |
| 292 | Efficient Classification with Adaptive KNN Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an adaptive kNN method for classification, in which different k are selected for different test samples. |
Puning Zhao; Lifeng Lai; |
| 293 | Data Augmentation for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction. |
Tong Zhao; Yozen Liu; Leonardo Neves; Oliver Woodford; Meng Jiang; Neil Shah; |
| 294 | Augmenting Policy Learning with Routines Discovered from A Single Demonstration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose routine-augmented policy learning (RAPL), which discovers routines composed of primitive actions from a single demonstration and uses discovered routines to augment policy learning. |
Zelin Zhao; Chuang Gan; Jiajun Wu; Xiaoxiao Guo; Joshua B. Tenenbaum; |
| 295 | Improved Consistency Regularization for GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We improve on this technique in several ways. |
Zhengli Zhao; Sameer Singh; Honglak Lee; Zizhao Zhang; Augustus Odena; Han Zhang; |
| 296 | Flow-based Generative Models for Learning Manifold to Manifold Mappings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: On the theoretical side, we introduce three kinds of invertible layers for manifold-valued data, which are not only analogous to their functionality in flow-based generative models (e.g., GLOW) but also preserve the key benefits (determinants of the Jacobian are easy to calculate). |
Xingjian Zhen; Rudrasis Chakraborty; Liu Yang; Vikas Singh; |
| 297 | Meta Label Correction for Noisy Label Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend this approach via posing the problem as a label correction problem within a meta-learning framework. |
Guoqing Zheng; Ahmed Hassan Awadallah; Susan Dumais; |
| 298 | Going Deeper With Directly-Trained Larger Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed “STBP-tdBN”, enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware. |
Hanle Zheng; Yujie Wu; Lei Deng; Yifan Hu; Guoqi Li; |
| 299 | Fully-Connected Tensor Network Decomposition and Its Application to Higher-Order Tensor Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a generalized tensor decomposition, which decomposes an Nth-order tensor into a set of Nth-order factors and establishes an operation between any two factors. |
Yu-Bang Zheng; Ting-Zhu Huang; Xi-Le Zhao; Qibin Zhao; Tai-Xiang Jiang; |
| 300 | How Does The Combined Risk Affect The Performance of Unsupervised Domain Adaptation Approaches? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this key challenge, we propose a method named E-MixNet. |
Li Zhong; Zhen Fang; Feng Liu; Jie Lu; Bo Yuan; Guangquan Zhang; |
| 301 | Multi-task Learning By Leveraging The Semantic Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. |
Fan Zhou; Brahim Chaib-draa; Boyu Wang; |
| 302 | MetaAugment: Sample-Aware Data Augmentation Policy Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we learn a sample-aware data augmentation policy efficiently by formulating it as a sample reweighting problem. |
Fengwei Zhou; Jiawei Li; Chuanlong Xie; Fei Chen; Lanqing Hong; Rui Sun; Zhenguo Li; |
| 303 | Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a ProbSparse self-attention mechanism, which achieves O(L log L) in time complexity and memory usage, and has comparable performance on sequences’ dependency alignment. |
Haoyi Zhou; Shanghang Zhang; Jieqi Peng; Shuai Zhang; Jianxin Li; Hui Xiong; Wancai Zhang; |
| 304 | Inverse Reinforcement Learning with Natural Language Goals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a language-conditioned policy and reward function. |
Li Zhou; Kevin Small; |
| 305 | Tri-level Robust Clustering Ensemble with Multiple Graph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to address this problem, we propose a novel Tri-level Robust Clustering Ensemble (TRCE) method by transforming the clustering ensemble problem to a multiple graph learning problem. |
Peng Zhou; Liang Du; Yi-Dong Shen; Xuejun Li; |
| 306 | Fairness in Forecasting and Learning Linear Dynamical Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce two natural notions of subgroup fairness and instantaneous fairness to address such under-representation bias in time-series forecasting problems. |
Quan Zhou; Jakub Marecek; Robert N. Shorten; |
| 307 | Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop an energy-efficient phase-domain signal processing circuit for the neuron and propose a direct-train deep SNN framework. |
Shibo Zhou; Xiaohua Li; Ying Chen; Sanjeev T. Chandrasekaran; Arindam Sanyal; |
| 308 | Local Differential Privacy for Bayesian Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee. |
Xingyu Zhou; Jian Tan; |
| 309 | A Primal-Dual Online Algorithm for Online Matching Problem in Dynamic Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the online matching problem in dynamic environments in which the dual optimal prices are allowed to vary over time. |
Yu-Hang Zhou; Peng Hu; Chen Liang; Huan Xu; Guangda Huzhang; Yinfu Feng; Qing Da; Xinshang Wang; An-Xiang Zeng; |
| 310 | Graph Neural Networks with Heterophily Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel framework called CPGNN that generalizes GNNs for graphs with either homophily or heterophily. |
Jiong Zhu; Ryan A. Rossi; Anup Rao; Tung Mai; Nedim Lipka; Nesreen K. Ahmed; Danai Koutra; |
| 311 | Bias and Variance of Post-processing in Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper takes a first step towards understanding the properties of post-processing. |
Keyu Zhu; Pascal Van Hentenryck; Ferdinando Fioretto; |
| 312 | Self-correcting Q-learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a new way to address the maximization bias in the form of a "self-correcting algorithm" for approximating the maximum of an expected value. |
Rong Zhu; Mattia Rigotti; |
| 313 | An Efficient Algorithm for Deep Stochastic Contextual Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate the SCB that uses a DNN reward function as a non-convex stochastic optimization problem, and design a stage-wised stochastic gradient descent algorithm to optimize the problem and determine the action policy. |
Tan Zhu; Guannan Liang; Chunjiang Zhu; Haining Li; Jinbo Bi; |
| 314 | Variational Fair Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based. |
Imtiaz Masud Ziko; Jing Yuan; Eric Granger; Ismail Ben Ayed; |
| 315 | Learning Task-Distribution Reward Shaping with Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide insights into optimal reward shaping, and propose a novel meta-learning framework to automatically learn such reward shaping to apply on newly sampled tasks. |
Haosheng Zou; Tongzheng Ren; Dong Yan; Hang Su; Jun Zhu; |
| 316 | Improving Continuous-time Conflict Based Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. |
Anton Andreychuk; Konstantin Yakovlev; Eli Boyarski; Roni Stern; |
| 317 | Inference-Based Deterministic Messaging For Multi-Agent Communication Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Communication is essential for coordination among humans and animals. Therefore, with the introduction of intelligent agents into the world, agent-to-agent and agent-to-human … |
Varun Bhatt; Michael Buro; |
| 318 | Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances. |
Jingkai Chen; Jiaoyang Li; Chuchu Fan; Brian C. Williams; |
| 319 | Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose a machine-learning (ML) framework for conflict selection that observes the decisions made by the oracle and learns a conflict-selection strategy represented by a linear ranking function that imitates the oracle’s decisions accurately and quickly. |
Taoan Huang; Sven Koenig; Bistra Dilkina; |
| 320 | The Influence of Memory in Multi-Agent Consensus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a framework to study what we call `memory consensus protocol’. |
David Kohan Marzagão; Luciana Basualdo Bonatto; Tiago Madeira; Marcelo Matheus Gauy; Peter McBurney; |
| 321 | Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To make progress in this direction, we study a smooth analogue of Q-learning. |
Stefanos Leonardos; Georgios Piliouras; |
| 322 | Lifelong Multi-Agent Path Finding in Large-Scale Warehouses Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. |
Jiaoyang Li; Andrew Tinka; Scott Kiesel; Joseph W. Durham; T. K. Satish Kumar; Sven Koenig; |
| 323 | Dec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a novel multi-agent coordination protocol based on subgoal intentions, defined as the probability distribution over feasible subgoal sequences. |
Minglong Li; Zhongxuan Cai; Wenjing Yang; Lixia Wu; Yinghui Xu; Ji Wang; |
| 324 | Expected Value of Communication for Planning in Ad Hoc Teamwork Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. |
William Macke; Reuth Mirsky; Peter Stone; |
| 325 | Time-Independent Planning for Multiple Moving Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an alternative approach, called time-independent planning, which is both online and distributed. |
Keisuke Okumura; Yasumasa Tamura; Xavier Défago; |
| 326 | Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose Resilient Adversarial value Decomposition with Antagonist-Ratios (RADAR). |
Thomy Phan; Lenz Belzner; Thomas Gabor; Andreas Sedlmeier; Fabian Ritz; Claudia Linnhoff-Popien; |
| 327 | Anytime Heuristic and Monte Carlo Methods for Large-Scale Simultaneous Coalition Structure Generation and Assignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In light of this, and to make it possible to generate better feasible solutions for difficult large-scale problems efficiently, we present and benchmark several different anytime algorithms that use general-purpose heuristics and Monte Carlo techniques to guide search. |
Fredrik Präntare; Herman Appelgren; Fredrik Heintz; |
| 328 | Newton Optimization on Helmholtz Decomposition for Continuous Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose NOHD (Newton Optimization on Helmholtz Decomposition), a Newton-like algorithm for multi-agent learning problems based on the decomposition of the dynamics of the system in its irrotational (Potential) and solenoidal (Hamiltonian) component. |
Giorgia Ramponi; Marcello Restelli; |
| 329 | Synchronous Dynamical Systems on Directed Acyclic Graphs: Complexity and Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that computational intractability results for reachability problems hold even for dynamical systems on directed acyclic graphs (dags). |
Daniel J. Rosenkrantz; Madhav Marathe; S. S. Ravi; Richard E. Stearns; |
| 330 | Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we move away from the artificial divide between dynamic agents and static games, to introduce and analyze a large class of competitive settings where both the agents and the games they play evolve strategically over time. |
Stratis Skoulakis; Tanner Fiez; Ryann Sim; Georgios Piliouras; Lillian Ratliff; |
| 331 | Value-Decomposition Multi-Agent Actor-Critics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critic methods that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critic (VDAC). |
Jianyu Su; Stephen Adams; Peter Beling; |
| 332 | Contract-based Inter-user Usage Coordination in Free-floating Car Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. |
Kentaro Takahira; Shigeo Matsubara; |
| 333 | Maintenance of Social Commitments in Multiagent Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by states whose truthhood an agent commits to maintain. |
Pankaj Telang; Munindar P. Singh; Neil Yorke-Smith; |
| 334 | Efficient Querying for Cooperative Probabilistic Commitments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully-selected commitment choices. |
Qi Zhang; Edmund H. Durfee; Satinder Singh; |
| 335 | Coordination Between Individual Agents in Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper we propose an agent-level coordination based MARL method. |
Yang Zhang; Qingyu Yang; Dou An; Chengwei Zhang; |
| 336 | Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the variational information bottleneck for interpretation, VIBI, a system-agnostic interpretable method that provides a brief but comprehensive explanation. |
Seojin Bang; Pengtao Xie; Heewook Lee; Wei Wu; Eric Xing; |
| 337 | Is The Most Accurate AI The Best Teammate? Optimizing AI for Teamwork Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We discuss the shortcoming of current optimization approaches beyond well-studied loss functions such as log-loss, and encourage future work on AI optimization problems motivated by human-AI collaboration. |
Gagan Bansal; Besmira Nushi; Ece Kamar; Eric Horvitz; Daniel S. Weld; |
| 338 | TripleTree: A Versatile Interpretable Representation of Black Box Agents and Their Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We create such a representation using a novel variant of the CART decision tree algorithm, and demonstrate how it facilitates practical understanding of black box agents through prediction, visualisation and rule-based explanation. |
Tom Bewley; Jonathan Lawry; |
| 339 | Bayes-TrEx: A Bayesian Sampling Approach to Model Transparency By Example Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. |
Serena Booth; Yilun Zhou; Ankit Shah; Julie Shah; |
| 340 | FIMAP: Feature Importance By Minimal Adversarial Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Feature Importance by Minimal Adversarial Perturbation (FIMAP), a neural network based approach that unifies feature importance and counterfactual explanations. |
Matt Chapman-Rounds; Umang Bhatt; Erik Pazos; Marc-Andre Schulz; Konstantinos Georgatzis; |
| 341 | Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption.The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN). |
Pengfei Chen; Junjie Ye; Guangyong Chen; Jingwei Zhao; Pheng-Ann Heng; |
| 342 | Robustness of Accuracy Metric and Its Inspirations in Learning with Noisy Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We concretize this finding’s inspiration in two essential aspects: training and validation, with which we address critical issues in learning with noisy labels. |
Pengfei Chen; Junjie Ye; Guangyong Chen; Jingwei Zhao; Pheng-Ann Heng; |
| 343 | A Unified Taylor Framework for Revisiting Attribution Methods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge the gap, in this paper, we propose a Taylor attribution framework and reformulate seven mainstream attribution methods into the framework. |
Huiqi Deng; Na Zou; Mengnan Du; Weifu Chen; Guocan Feng; Xia Hu; |
| 344 | Verifiable Machine Ethics in Changing Contexts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the use of a reasoning cycle, in which information about the ethical reasoner’s context is imported in a logical form, and we propose that context-specific aspects of an ethical encoding be prefaced by a guard formula. |
Louise A. Dennis; Martin Mose Bentzen; Felix Lindner; Michael Fisher; |
| 345 | Epistemic Logic of Know-Who Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper suggests a definition of "know who" as a modality using Grove-Halpern semantics of names. |
Sophia Epstein; Pavel Naumov; |
| 346 | Agent Incentives: A Causal Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a framework for analysing agent incentives using causal influence diagrams. |
Tom Everitt; Ryan Carey; Eric D. Langlois; Pedro A. Ortega; Shane Legg; |
| 347 | Individual Fairness in Kidney Exchange Programs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. |
Golnoosh Farnadi; William St-Arnaud; Behrouz Babaki; Margarida Carvalho; |
| 348 | Fair Representations By Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel unsupervised approach to map data into a compressed binary representation independent of sensitive attributes. |
Xavier Gitiaux; Huzefa Rangwala; |
| 349 | Amnesiac Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy. |
Laura Graves; Vineel Nagisetty; Vijay Ganesh; |
| 350 | On The Verification of Neural ODEs with Stochastic Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states over a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. |
Sophie Grunbacher; Ramin Hasani; Mathias Lechner; Jacek Cyranka; Scott A. Smolka; Radu Grosu; |
| 351 | PenDer: Incorporating Shape Constraints Via Penalized Derivatives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We notice that many such common shapes are related to derivatives, and propose a new approach, PenDer (Penalizing Derivatives), which incorporates these shape constraints by penalizing the derivatives. |
Akhil Gupta; Lavanya Marla; Ruoyu Sun; Naman Shukla; Arinbjörn Kolbeinsson; |
| 352 | Visualization of Supervised and Self-Supervised Neural Networks Via Attribution Guided Factorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we integrate two lines of research: gradient-based methods and attribution-based methods, and develop an algorithm that provides per-class explainability. |
Shir Gur; Ameen Ali; Lior Wolf; |
| 353 | Differentially Private Clustering Via Maximum Coverage Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. |
Matthew Jones; Huy L. Nguyen; Thy D Nguyen; |
| 354 | Ordered Counterfactual Explanation By Mixed-Integer Linear Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). |
Kentaro Kanamori; Takuya Takagi; Ken Kobayashi; Yuichi Ike; Kento Uemura; Hiroki Arimura; |
| 355 | On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper advances a novel method for generating plausible counterfactuals and semi-factuals for black-box CNN classifiers doing computer vision. |
Eoin M. Kenny; Mark T Keane; |
| 356 | How RL Agents Behave When Their Actions Are Modified Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the Modified-Action Markov Decision Process, an extension of the MDP model that allows actions to differ from the policy. |
Eric D. Langlois; Tom Everitt; |
| 357 | Outlier Impact Characterization for Time Series Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, for the first time, we study the characteristics of such outliers through the lens of the influence functional from robust statistics. |
Jianbo Li; Lecheng Zheng; Yada Zhu; Jingrui He; |
| 358 | Interpreting Deep Neural Networks with Relative Sectional Propagation By Analyzing Comparative Gradients and Hostile Activations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new attribution method, Relative Sectional Propagation (RSP), for fully decomposing the output predictions with the characteristics of class-discriminative attributions and clear objectness. |
Woo-Jeoung Nam; Jaesik Choi; Seong-Whan Lee; |
| 359 | Ethical Dilemmas in Strategic Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes to capture ethical dilemmas as a modality in strategic game settings with and without limit on sacrifice and for perfect and imperfect information games. |
Pavel Naumov; Rui-Jie Yew; |
| 360 | Comprehension and Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper suggests to interpret comprehension as a modality and proposes a complete bimodal logical system that describes an interplay between comprehension and knowledge modalities. |
Pavel Naumov; Kevin Ros; |
| 361 | Fair Influence Maximization: A Welfare Optimization Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we propose a framework based on social welfare theory, wherein the cardinal utilities derived by each community are aggregated using the isoelastic social welfare functions. |
Aida Rahmattalabi; Shahin Jabbari; Himabindu Lakkaraju; Phebe Vayanos; Max Izenberg; Ryan Brown; Eric Rice; Milind Tambe; |
| 362 | Explaining Convolutional Neural Networks Through Attribution-Based Input Sampling and Block-Wise Feature Aggregation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. |
Sam Sattarzadeh; Mahesh Sudhakar; Anthony Lem; Shervin Mehryar; Konstantinos N Plataniotis; Jongseong Jang; Hyunwoo Kim; Yeonjeong Jeong; Sangmin Lee; Kyunghoon Bae; |
| 363 | Exploring The Vulnerability of Deep Neural Networks: A Study of Parameter Corruption Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an indicator to measure the robustness of neural network parameters by exploiting their vulnerability via parameter corruption. |
Xu Sun; Zhiyuan Zhang; Xuancheng Ren; Ruixuan Luo; Liangyou Li; |
| 364 | Ethically Compliant Sequential Decision Making Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel approach for building ethically compliant autonomous systems that optimize completing a task while following an ethical framework. |
Justin Svegliato; Samer B. Nashed; Shlomo Zilberstein; |
| 365 | Improving Robustness to Model Inversion Attacks Via Mutual Information Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Mutual Information Regularization based Defense (MID) against MI attacks. |
Tianhao Wang; Yuheng Zhang; Ruoxi Jia; |
| 366 | Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a tighter linear approximation approach for the robustness verification of Convolutional Neural Networks (CNNs). |
Yiting Wu; Min Zhang; |
| 367 | Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we rethink the ACE algorithm of Ghorbani et~al., proposing an alternative invertible concept-based explanation (ICE) framework to overcome its shortcomings. |
Ruihan Zhang; Prashan Madumal; Tim Miller; Krista A. Ehinger; Benjamin I. P. Rubinstein; |
| 368 | I-Algebra: Towards Interactive Interpretability of Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that DNN interpretability should be implemented as the interactions between users and models. |
Xinyang Zhang; Ren Pang; Shouling Ji; Fenglong Ma; Ting Wang; |
| 369 | Decision-Guided Weighted Automata Extraction from Recurrent Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel approach to extracting weighted automata with the guidance of a target RNN’s decision and context information. |
Xiyue Zhang; Xiaoning Du; Xiaofei Xie; Lei Ma; Yang Liu; Meng Sun; |
| 370 | Computing Plan-Length Bounds Using Lengths of Longest Paths Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We devise a method to exactly compute the length of the longest simple path in factored state spaces, like state spaces encountered in classical planning. |
Mohammad Abdulaziz; Dominik Berger; |
| 371 | Constrained Risk-Averse Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies that lower-bound the constrained risk-averse problem. |
Mohamadreza Ahmadi; Ugo Rosolia; Michel D. Ingham; Richard M. Murray; Aaron D. Ames; |
| 372 | Contract Scheduling With Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the setting in which there is a potentially erroneous prediction concerning the interruption. |
Spyros Angelopoulos; Shahin Kamali; |
| 373 | Responsibility Attribution in Parameterized Markovian Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of responsibility attribution in the setting of parametric Markov chains. |
Christel Baier; Florian Funke; Rupak Majumdar; |
| 374 | Symbolic Search for Optimal Total-Order HTN Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. |
Gregor Behnke; David Speck; |
| 375 | A Multivariate Complexity Analysis of The Material Consumption Scheduling Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Roughly speaking, the problem deals with minimizing the makespan when scheduling jobs that consume non-renewable resources. |
Matthias Bentert; Robert Bredereck; Péter Györgyi; Andrzej Kaczmarczyk; Rolf Niedermeier; |
| 376 | General Policies, Representations, and Planning Width Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this question by relating bounded width and serialized width to ideas of generalized planning, where general policies aim to solve multiple instances of a planning problem all at once. |
Blai Bonet; Hector Geffner; |
| 377 | Successor Feature Sets: Generalizing Successor Representations Across Policies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these limitations, we bring together ideas from predictive state representations, belief space value iteration, successor features, and convex analysis: we develop a new, general successor-style representation, together with a Bellman equation that connects multiple sources of information within this representation, including different latent states, policies, and reward functions. |
Kianté Brantley; Soroush Mehri; Geoff J. Gordon; |
| 378 | GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning Via Goal-Literal Babbling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. |
Rohan Chitnis; Tom Silver; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Tomás Lozano-Pérez; |
| 379 | Robust Finite-State Controllers for Uncertain POMDPs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop an algorithm to compute finite-memory policies for uPOMDPs that robustly satisfy specifications against any admissible distribution. |
Murat Cubuktepe; Nils Jansen; Sebastian Junges; Ahmadreza Marandi; Marnix Suilen; Ufuk Topcu; |
| 380 | Learning General Planning Policies from Small Examples Without Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce an alternative approach for computing more expressive general policies which does not require sample plans or a QNP planner. |
Guillem Francès; Blai Bonet; Hector Geffner; |
| 381 | Revisiting Dominance Pruning in Decoupled Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contribution is a stronger variant of dominance checking for optimal planning, where efficiency and pruning power are most crucial. |
Daniel Gnad; |
| 382 | Equitable Scheduling on A Single Machine Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a natural but seemingly yet unstudied generalization of the problem of scheduling jobs on a single machine so as to minimize the number of tardy jobs. |
Klaus Heeger; Dan Hermelin; George B. Mertzios; Hendrik Molter; Rolf Niedermeier; Dvir Shabtay; |
| 383 | Landmark Generation in HTN Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel LM generation method for Hierarchical Task Network (HTN) planning and show that it is sound and incomplete. |
Daniel Höller; Pascal Bercher; |
| 384 | Endomorphisms of Classical Planning Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we employ structure-preserving maps on labeled transition systems (LTSs), namely endomorphisms well known from model theory, in order to detect redundancy. |
Rostislav Horčík; Daniel Fišer; |
| 385 | Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method that can be used to deploy the volunteers in the system, based on the real time distribution of the bikes in different stations. |
Jinjia Huang; Mabel C. Chou; Chung-Piaw Teo; |
| 386 | Branch and Price for Bus Driver Scheduling with Complex Break Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a Branch and Price approach for a real-life Bus Driver Scheduling problem with a complex set of break constraints. |
Lucas Kletzander; Nysret Musliu; Pascal Van Hentenryck; |
| 387 | On-line Learning of Planning Domains from Sensor Data in PAL: Scaling Up to Large State Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an approach to learn an extensional representation of a discrete deterministic planning domain from observations in a continuous space navigated by the agent actions. |
Leonardo Lamanna; Alfonso Emilio Gerevini; Alessandro Saetti; Luciano Serafini; Paolo Traverso; |
| 388 | Progression Heuristics for Planning with Probabilistic LTL Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present novel admissible heuristics to guide the search for cost-optimal policies for these problems. |
Ian Mallett; Sylvie Thiebaux; Felipe Trevizan; |
| 389 | Bayesian Optimized Monte Carlo Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a general method for efficient action sampling based on Bayesian optimization. |
John Mern; Anil Yildiz; Zachary Sunberg; Tapan Mukerji; Mykel J. Kochenderfer; |
| 390 | Improved POMDP Tree Search Planning with Prioritized Action Branching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method called PA-POMCPOW to sample a subset of the action space that provides varying mixtures of exploitation and exploration for inclusion in a search tree. |
John Mern; Anil Yildiz; Lawrence Bush; Tapan Mukerji; Mykel J. Kochenderfer; |
| 391 | Synthesis of Search Heuristics for Temporal Planning Via Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim at exploiting recent advances in reinforcement learning, for the synthesis of heuristics for temporal planning. |
Andrea Micheli; Alessandro Valentini; |
| 392 | Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks – A Complexity Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. |
Conny Olz; Susanne Biundo; Pascal Bercher; |
| 393 | Faster and Better Simple Temporal Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we give a structural characterization and extend the tractability frontier of the Simple Temporal Problem (STP) by defining the class of the Extended Simple Temporal Problem (ESTP), which augments STP with strict inequalities and monotone Boolean formulae on inequations (i.e., formulae involving the operations of conjunction, disjunction and parenthesization). |
Dario Ostuni; Alice Raffaele; Romeo Rizzi; Matteo Zavatteri; |
| 394 | Latent Independent Excitation for Generalizable Sensor-based Cross-Person Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel method named Generalizable Independent Latent Excitation (GILE) for human activity recognition, which greatly enhances the cross-person generalization capability of the model. |
Hangwei Qian; Sinno Jialin Pan; Chunyan Miao; |
| 395 | Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on planning for Stochastic Shortest Path (SSP) UMDPs with uncertain cost and transition functions. |
Marc Rigter; Bruno Lacerda; Nick Hawes; |
| 396 | An LP-Based Approach for Goal Recognition As Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. |
Luísa R. A. Santos; Felipe Meneguzzi; Ramon Fraga Pereira; André Grahl Pereira; |
| 397 | Saturated Post-hoc Optimization for Classical Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show how to apply this idea to post-hoc optimization and obtain a heuristic that dominates the original both in theory and on the IPC benchmarks. |
Jendrik Seipp; Thomas Keller; Malte Helmert; |
| 398 | Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe a planner that uses richer information about agents’ knowledge to improve upon QDec-FP. |
Shashank Shekhar; Ronen I. Brafman; Guy Shani; |
| 399 | Planning with Learned Object Importance in Large Problem Instances Using Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. |
Tom Silver; Rohan Chitnis; Aidan Curtis; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling; |
| 400 | Symbolic Search for Oversubscription Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the use of symbolic search for optimal oversubscription planning. |
David Speck; Michael Katz; |
| 401 | Online Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we introduce the problem of Online Action Recognition. |
Alejandro Suárez-Hernández; Javier Segovia-Aguas; Carme Torras; Guillem Alenyà; |
| 402 | A Complexity-theoretic Analysis of Green Pickup-and-Delivery Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Nevertheless, we demonstrate in this paper an inherent intractability of these green components themselves. |
Xing Tan; Jimmy Xiangji Huang; |
| 403 | Faster Stackelberg Planning Via Symbolic Search and Information Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we introduce new methods to tackle this source of complexity, through sharing information across follower tasks. |
Álvaro Torralba; Patrick Speicher; Robert Künnemann; Marcel Steinmetz; Jörg Hoffmann; |
| 404 | On The Optimal Efficiency of A* with Dominance Pruning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We extend this analysis for A* with dominance pruning, which exploits a dominance relation to eliminate some nodes during the search. |
Álvaro Torralba; |
| 405 | Dynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we mitigate this by representing objectives as automata in order to define novel reward shaping functions over this structured representation. |
Alvaro Velasquez; Brett Bissey; Lior Barak; Andre Beckus; Ismail Alkhouri; Daniel Melcer; George Atia; |
| 406 | Asking The Right Questions: Learning Interpretable Action Models Through Query Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent’s internal model in a user-interpretable vocabulary. |
Pulkit Verma; Shashank Rao Marpally; Siddharth Srivastava; |
| 407 | Competitive Analysis for Two-Level Ski-Rental Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study a two-level ski-rental problem. |
Binghan Wu; Wei Bao; Dong Yuan; |
| 408 | Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. |
Liang Xin; Wen Song; Zhiguang Cao; Jie Zhang; |
| 409 | Group Fairness By Probabilistic Modeling with Latent Fair Decisions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to closely model the data distribution, we employ probabilistic circuits, an expressive and tractable probabilistic model, and propose an algorithm to learn them from incomplete data. |
YooJung Choi; Meihua Dang; Guy Van den Broeck; |
| 410 | GO Hessian for Expectation-Based Objectives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the GO gradient, we present for E_q_γ(y) [f(y)] an unbiased low-variance Hessian estimator, named GO Hessian, which contains the deterministic Hessian as a special case. |
Yulai Cong; Miaoyun Zhao; Jianqiao Li; Junya Chen; Lawrence Carin; |
| 411 | Better Bounds on The Adaptivity Gap of Influence Maximization Under Full-adoption Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To prove our bounds, we introduce new techniques to relate adaptive policies with non-adaptive ones that might be of their own interest. |
Gianlorenzo D’Angelo; Debashmita Poddar; Cosimo Vinci; |
| 412 | Uncertainty Quantification in CNN Through The Bootstrap of Convex Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel bootstrap based framework for the estimation of prediction uncertainty. |
Hongfei Du; Emre Barut; Fang Jin; |
| 413 | Scalable First-Order Methods for Robust MDPs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes the first first-order framework for solving robust MDPs. |
Julien Grand-Clément; Christian Kroer; |
| 414 | High Dimensional Level Set Estimation with Bayesian Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes novel methods to solve the high dimensional LSE problems using Bayesian Neural Networks. |
Huong Ha; Sunil Gupta; Santu Rana; Svetha Venkatesh; |
| 415 | A Generative Adversarial Framework for Bounding Confounded Causal Effects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounding based on Pearl’s structural causal model. |
Yaowei Hu; Yongkai Wu; Lu Zhang; Xintao Wu; |
| 416 | Estimating Identifiable Causal Effects Through Double Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. |
Yonghan Jung; Jin Tian; Elias Bareinboim; |
| 417 | Relational Boosted Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Relational Boosted Bandits (RB2), a contextual bandits algorithm for relational domains based on (relational) boosted trees. |
Ashutosh Kakadiya; Sriraam Natarajan; Balaraman Ravindran; |
| 418 | Instrumental Variable-based Identification for Causal Effects Using Covariate Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Different from existing studies, we propose novel identification conditions of joint probabilities of potential outcomes, which allow us to derive a consistent estimator of the causal effect. |
Yuta Kawakami; |
| 419 | Learning Continuous High-Dimensional Models Using Mutual Information and Copula Bayesian Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new framework to learn non-parametric graphical models from continuous observational data. |
Marvin Lasserre; Régis Lebrun; Pierre-Henri Wuillemin; |
| 420 | Submodel Decomposition Bounds for Influence Diagrams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a model decomposition framework in both IDs and LIMIDs, which we call submodel decomposition that generates a tree of single-stage decision problems through a tree clustering scheme. |
Junkyu Lee; Radu Marinescu; Rina Dechter; |
| 421 | A New Bounding Scheme for Influence Diagrams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a new bounding scheme for MEU that applies partitioning based approximations on top of the decomposition scheme called a multi-operator cluster DAG for influence diagrams that is more sensitive to the underlying structure of the model than the classical join-tree decomposition of influence diagrams. |
Radu Marinescu; Junkyu Lee; Rina Dechter; |
| 422 | Estimation of Spectral Risk Measures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of estimating a spectral risk measure (SRM) from i.i.d. samples, and propose a novel method that is based on numerical integration. |
Ajay Kumar Pandey; Prashanth L.A.; Sanjay P. Bhat; |
| 423 | Probabilistic Dependency Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. |
Oliver Richardson; Joseph Y Halpern; |
| 424 | Robust Contextual Bandits Via Bootstrapping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To demonstrate the versatility of the estimator, we apply it to design a BootLinUCB algorithm for the contextual bandit. |
Qiao Tang; Hong Xie; Yunni Xia; Jia Lee; Qingsheng Zhu; |
| 425 | Learning The Parameters of Bayesian Networks from Uncertain Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an approach for learning Bayesian network parameters that explicitly incorporates such uncertainty, and which is a natural extension of the Bayesian network formalism. |
Segev Wasserkrug; Radu Marinescu; Sergey Zeltyn; Evgeny Shindin; Yishai A Feldman; |
| 426 | Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this area. |
Marcel Wienöbst; Max Bannach; Maciej Liskiewicz; |
| 427 | Bounding Causal Effects on Continuous Outcome Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present novel non-parametric methods to bound causal effects on the continuous outcome from studies with imperfect compliance. |
Junzhe Zhang; Elias Bareinboim; |
| 428 | A Fast Exact Algorithm for The Resource Constrained Shortest Path Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces several heuristics in the resource constrained path finding context that significantly improve the algorithmic performance of the initialisation phase and the core search. |
Saman Ahmadi; Guido Tack; Daniel D. Harabor; Philip Kilby; |
| 429 | Generalization in Portfolio-Based Algorithm Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide the first provable guarantees for portfolio-based algorithm selection. |
Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik; |
| 430 | Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. |
Nawal Benabbou; Cassandre Leroy; Thibaut Lust; Patrice Perny; |
| 431 | F-Aware Conflict Prioritization & Improved Heuristics For Conflict-Based Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we introduce an expanded categorization of conflicts, which first resolves conflicts where the f-values of the child nodes are larger than the f-value of the node to be split, and present a method for identifying such conflicts. |
Eli Boyarski; Ariel Felner; Pierre Le Bodic; Daniel D. Harabor; Peter J. Stuckey; Sven Koenig; |
| 432 | Parameterized Algorithms for MILPs with Small Treedepth Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend this line of work to the mixed case, by showing an algorithm solving MILP in time f(a,d)poly(n), where a is the largest coefficient of the constraint matrix, d is its treedepth, and n is the number of variables. |
Cornelius Brand; Martin Koutecký; Sebastian Ordyniak; |
| 433 | NuQClq: An Effective Local Search Algorithm for Maximum Quasi-Clique Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper develops an efficient local search algorithm named NuQClq for the MQCP, which has two main ideas. |
Jiejiang Chen; Shaowei Cai; Shiwei Pan; Yiyuan Wang; Qingwei Lin; Mengyu Zhao; Minghao Yin; |
| 434 | Symmetry Breaking for K-Robust Multi-Agent Path Finding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In thiswork we introducing a variety of pairwise symmetry break-ing constraints, specific tok-robust planning, that can effi-ciently find compatible and optimal paths for pairs of con-flicting agents. |
Zhe Chen; Daniel D. Harabor; Jiaoyang Li; Peter J. Stuckey; |
| 435 | Escaping Local Optima with Non-Elitist Evolutionary Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We solve this open problem through rigorous runtime analysis of elitist and non-elitist population-based EAs on a class of multi-modal problems. |
Duc-Cuong Dang; Anton Eremeev; Per Kristian Lehre; |
| 436 | Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic. |
Anh Viet Do; Frank Neumann; |
| 437 | Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the OneJumpZeroJump problem, a bi-objective problem whose single objectives are isomorphic to the classic jump functions benchmark. |
Benjamin Doerr; Weijie Zheng; |
| 438 | Multi-Objective Submodular Maximization By Regret Ratio Minimization with Theoretical Guarantee Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the problem of minimizing the regret ratio in multi-objective submodular maximization, which is to find at most k solutions to approximate the whole Pareto set as well as possible. |
Chao Feng; Chao Qian; |
| 439 | Choosing The Initial State for Online Replanning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show how such ad hoc solutions can be avoided by integrating the choice of the appropriate initial state into the search process itself. |
Maximilian Fickert; Ivan Gavran; Ivan Fedotov; Jörg Hoffmann; Rupak Majumdar; Wheeler Ruml; |
| 440 | OpEvo: An Evolutionary Method for Tensor Operator Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel evolutionary method, OpEvo, which efficiently explores the search spaces of tensor operators by introducing a topology-aware mutation operation based on q-random walk to leverage the topological structures over the search spaces. |
Xiaotian Gao; Wei Cui; Lintao Zhang; Mao Yang; |
| 441 | Efficient Bayesian Network Structure Learning Via Parameterized Local Search on Topological Orderings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study ordering-based local search, where a solution is described via a topological ordering of the variables. |
Niels Grüttemeier; Christian Komusiewicz; Nils Morawietz; |
| 442 | Enhancing Balanced Graph Edge Partition with Effective Local Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study local search algorithms for this problem to further improve the partition results from existing methods. |
Zhenyu Guo; Mingyu Xiao; Yi Zhou; Dongxiang Zhang; Kian-Lee Tan; |
| 443 | Submodular Span, with Applications to Conditional Data Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As an extension to the matroid span problem, we propose the submodular span problem that involves finding a large set of elements with small gain relative to a given query set. |
Lilly Kumari; Jeff Bilmes; |
| 444 | EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study how to decrease its runtime even further using inadmissible heuristics. |
Jiaoyang Li; Wheeler Ruml; Sven Koenig; |
| 445 | Correlation-Aware Heuristic Search for Intelligent Virtual Machine Provisioning in Cloud Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. |
Chuan Luo; Bo Qiao; Wenqian Xing; Xin Chen; Pu Zhao; Chao Du; Randolph Yao; Hongyu Zhang; Wei Wu; Shaowei Cai; Bing He; Saravanakumar Rajmohan; Qingwei Lin; |
| 446 | Single Player Monte-Carlo Tree Search Based on The Plackett-Luce Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Plackett-Luce MCTS (PL-MCTS), a path search algorithm based on a probabilistic model over the qualities of successor nodes. |
Felix Mohr; Viktor Bengs; Eyke Hüllermeier; |
| 447 | Policy-Guided Heuristic Search with Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we introduce Policy-guided Heuristic Search (PHS), a novel search algorithm that uses both a heuristic function and a policy and has theoretical guarantees on the search loss that relates to both the quality of the heuristic and of the policy. |
Laurent Orseau; Levi H. S. Lelis; |
| 448 | Deep Innovation Protection: Confronting The Credit Assignment Problem in Training Heterogeneous Neural Architectures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a method called Deep Innovation Protection (DIP) that addresses the credit assignment problem in training complex heterogenous neural network models end-to-end for such environments. |
Sebastian Risi; Kenneth O. Stanley; |
| 449 | Weighting-based Variable Neighborhood Search for Optimal Camera Placement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a weighting-based variable neighborhood search (WVNS) algorithm for solving OCP. |
Zhouxing Su; Qingyun Zhang; Zhipeng Lü; Chu-Min Li; Weibo Lin; Fuda Ma; |
| 450 | Multi-Goal Multi-Agent Path Finding Via Decoupled and Integrated Goal Vertex Ordering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. |
Pavel Surynek; |
| 451 | Bayes DistNet – A Robust Neural Network for Algorithm Runtime Distribution Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend RTD prediction models into the Bayesian setting for the first time. |
Jake Tuero; Michael Buro; |
| 452 | Learning Branching Heuristics for Propositional Model Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Neuro#, an approach for learning branching heuristics to improve the performance of exact #SAT solvers on instances from a given family of problems. |
Pashootan Vaezipoor; Gil Lederman; Yuhuai Wu; Chris Maddison; Roger B Grosse; Sanjit A. Seshia; Fahiem Bacchus; |
| 453 | Accelerated Combinatorial Search for Outlier Detection with Provable Bound on Sub-Optimality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we are concerned with their influence on the accuracy of Principal Component Analysis (PCA). |
Guihong Wan; Haim Schweitzer; |
| 454 | Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for The Traveling Salesman Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. |
Jiongzhi Zheng; Kun He; Jianrong Zhou; Yan Jin; Chu-Min Li; |
| 455 | Improving Maximum K-plex Solver Via Second-Order Reduction and Graph Color Bounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the paper, we develop an exact algorithm, Maplex, for solving this problem in real world graphs practically. |
Yi Zhou; Shan Hu; Mingyu Xiao; Zhang-Hua Fu; |
| 456 | GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose to utilize the self-attention mechanism where we explicitly fuse structural information to learn the dependencies between words with different syntactic distances. |
Wasi Uddin Ahmad; Nanyun Peng; Kai-Wei Chang; |
| 457 | Empirical Regularization for Synthetic Sentence Pairs in Unsupervised Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we empirically study the core training procedure of UNMT to analyze the synthetic sentence pairs obtained from back-translation. |
Xi Ai; Bin Fang; |
| 458 | Segmentation of Tweets with URLs and Its Applications to Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the structure of tweets with URLs relative to the content of the Web documents pointed to by the URLs. |
Abdullah Aljebreen; Weiyi Meng; Eduard Dragut; |
| 459 | Unsupervised Opinion Summarization with Content Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that explicitly incorporating content planning in a summarization model not only yields output of higher quality, but also allows the creation of synthetic datasets which are more natural, resembling real world document-summary pairs. |
Reinald Kim Amplayo; Stefanos Angelidis; Mirella Lapata; |
| 460 | Enhancing Scientific Papers Summarization with Citation Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. |
Chenxin An; Ming Zhong; Yiran Chen; Danqing Wang; Xipeng Qiu; Xuanjing Huang; |
| 461 | Multi-Dimensional Explanation of Target Variables from Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Multi-Target Masker (MTM) to address these shortcomings. |
Diego Antognini; Claudiu Musat; Boi Faltings; |
| 462 | Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a neural network architecture for joint coreference resolution and semantic role labeling for English, and train graph neural networks to model the ‘coherence’ of the combined shallow semantic graph. |
Rahul Aralikatte; Mostafa Abdou; Heather C Lent; Daniel Hershcovich; Anders Søgaard; |
| 463 | Segatron: Segment-Aware Transformer for Language Modeling and Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. |
He Bai; Peng Shi; Jimmy Lin; Yuqing Xie; Luchen Tan; Kun Xiong; Wen Gao; Ming Li; |
| 464 | Learning to Copy Coherent Knowledge for Response Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, this paper proposes a Goal-Oriented Knowledge Copy network, GOKC. |
Jiaqi Bai; Ze Yang; Xinnian Liang; Wei Wang; Zhoujun Li; |
| 465 | Contextualized Rewriting for Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate contextualized rewriting, which ingests the entire original document. |
Guangsheng Bao; Yue Zhang; |
| 466 | Knowledge-driven Natural Language Understanding of English Text and Its Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. |
Kinjal Basu; Sarat Chandra Varanasi; Farhad Shakerin; Joaquín Arias; Gopal Gupta; |
| 467 | One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation Without A Complex Pipeline Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we cast Text-to-AMR and AMR-to-Text as a symmetric transduction task and show that by devising a careful graph linearization and extending a pretrained encoder-decoder model, it is possible to obtain state-of-the-art performances in both tasks using the very same seq2seq approach, i.e., SPRING (Symmetric PaRsIng aNd Generation). |
Michele Bevilacqua; Rexhina Blloshmi; Roberto Navigli; |
| 468 | Benchmarking Knowledge-Enhanced Commonsense Question Answering Via Knowledge-to-Text Transformation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In recent years, many knowledge-enhanced Commonsense Question Answering (CQA) approaches have been proposed. |
Ning Bian; Xianpei Han; Bo Chen; Le Sun; |
| 469 | Multilingual Transfer Learning for QA Using Translation As Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semantic space. |
Mihaela Bornea; Lin Pan; Sara Rosenthal; Radu Florian; Avirup Sil; |
| 470 | Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions, relying only on distant supervision with no additional annotation cost for human-written rationales. |
Faeze Brahman; Vered Shwartz; Rachel Rudinger; Yejin Choi; |
| 471 | Brain Decoding Using FNIRS Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate brain decoding tasks under the help of fNIRS and empirically compare fNIRS with fMRI. |
Lu Cao; Dandan Huang; Yue Zhang; Xiaowei Jiang; Yanan Chen; |
| 472 | Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit the problem of extracting the values of a given set of key fields from form-like documents. |
Rongyu Cao; Ping Luo; |
| 473 | Simple or Complex? Learning to Predict Readability of Bengali Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a readability analysis tool capable of analyzing text written in the Bengali language to provide in-depth information on its readability and complexity. |
Susmoy Chakraborty; Mir Tafseer Nayeem; Wasi Uddin Ahmad; |
| 474 | Lexically Constrained Neural Machine Translation with Explicit Alignment Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate Att-Input and Att-Output, two alignment-based constrained decoding methods. |
Guanhua Chen; Yun Chen; Victor O.K. Li; |
| 475 | Aspect-Level Sentiment-Controllable Review Generation with Mutual Learning Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a mutual learning framework to take advantage of unlabeled data to assist the aspect-level sentiment-controllable review generation. |
Huimin Chen; Yankai Lin; Fanchao Qi; Jinyi Hu; Peng Li; Jie Zhou; Maosong Sun; |
| 476 | Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce a large-scale multi-domain text corpus for modeling persuasive strategies in good-faith text requests. |
Jiaao Chen; Diyi Yang; |
| 477 | A Lightweight Neural Model for Biomedical Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose a lightweight neural method for biomedical entity linking, which needs just a fraction of the parameters of a BERT model and much less computing resources. |
Lihu Chen; Gaël Varoquaux; Fabian M. Suchanek; |
| 478 | Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task and propose a bidirectional MRC (BMRC) framework to address this challenge. |
Shaowei Chen; Yu Wang; Jie Liu; Yuelin Wang; |
| 479 | Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. |
Tao Chen; Haochen Shi; Liyuan Liu; Siliang Tang; Jian Shao; Zhigang Chen; Yueting Zhuang; |
| 480 | Reasoning in Dialog: Improving Response Generation By Context Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, in this paper, we propose to improve the response generation performance by examining the model’s ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog. |
Xiuying Chen; Zhi Cui; Jiayi Zhang; Chen Wei; Jianwei Cui; Bin Wang; Dongyan Zhao; Rui Yan; |
| 481 | Meta-Transfer Learning for Low-Resource Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to utilize two knowledge-rich sources to tackle this problem, which are large pre-trained models and diverse existing corpora. |
Yi-Syuan Chen; Hong-Han Shuai; |
| 482 | Adaptive Prior-Dependent Correction Enhanced Reinforcement Learning for Natural Language Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, we propose a technique called adaptive prior-dependent correction (APDC) to enhance RL. |
Wei Cheng; Ziyan Luo; Qiyue Yin; |
| 483 | How Linguistically Fair Are Multilingual Pre-Trained Language Models? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As we discuss in this paper, this is often the case, and the choices are usually made without a clear articulation of reasons or underlying fairness assumptions. |
Monojit Choudhury; Amit Deshpande; |
| 484 | DirectQE: Direct Pretraining for Machine Translation Quality Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel framework called DirectQE that provides a direct pretraining for QE tasks. |
Qu Cui; Shujian Huang; Jiahuan Li; Xiang Geng; Zaixiang Zheng; Guoping Huang; Jiajun Chen; |
| 485 | We Can Explain Your Research in Layman’s Terms: Towards Automating Science Journalism at Scale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to study Automating Science Journalism (ASJ), the process of producing a layman’s terms summary of a research article, as a new benchmark for long neural abstractive summarization and story generation. |
Rumen Dangovski; Michelle Shen; Dawson Byrd; Li Jing; Desislava Tsvetkova; Preslav Nakov; Marin Soljačić; |
| 486 | Consecutive Decoding for Speech-to-text Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT), an integral approach for speech-to-text translation. |
Qianqian Dong; Mingxuan Wang; Hao Zhou; Shuang Xu; Bo Xu; Lei Li; |
| 487 | Listen, Understand and Translate: Triple Supervision Decouples End-to-end Speech-to-text Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Listen-Understand-Translate, (LUT), a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task. |
Qianqian Dong; Rong Ye; Mingxuan Wang; Hao Zhou; Shuang Xu; Bo Xu; Lei Li; |
| 488 | MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to evaluate a diverse set of generations, we propose a simple scoring algorithm, based on bipartite graph matching, to optimally incorporate a set of diverse references. |
Yao Dou; Maxwell Forbes; Ari Holtzman; Yejin Choi; |
| 489 | Knowledge-aware Leap-LSTM: Integrating Prior Knowledge Into Leap-LSTM Towards Faster Long Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Knowledge-AwareLeap-LSTM (KALL), a novel architecture which integrates prior human knowledge (created either manually or automatically) like in-domain keywords, terminologies or lexicons into Leap-LSTM to partially supervise the skipping process. |
Jinhua Du; Yan Huang; Karo Moilanen; |
| 490 | FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. |
Yuwei Fang; Shuohang Wang; Zhe Gan; Siqi Sun; Jingjing Liu; |
| 491 | Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present an innovative model for discontinuous NER based on pointer networks, where the pointer simultaneously decides whether a token at each decoding frame constitutes an entity mention and where the next constituent token is. |
Hao Fei; Donghong Ji; Bobo Li; Yijiang Liu; Yafeng Ren; Fei Li; |
| 492 | Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate a novel unified SRL framework based on the sequence-to-sequence architecture with double enhancement in both the encoder and decoder sides. |
Hao Fei; Fei Li; Bobo Li; Donghong Ji; |
| 493 | End-to-end Semantic Role Labeling with Neural Transition-based Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first work of transition-based neural models for end-to-end SRL. |
Hao Fei; Meishan Zhang; Bobo Li; Donghong Ji; |
| 494 | Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel training framework, where the learning of general knowledge is more in line with the idea of reaching consensus, i.e., finding common knowledge that is beneficial to different yet all datasets through diversified learning partners. |
Shaoxiong Feng; Xuancheng Ren; Kan Li; Xu Sun; |
| 495 | More The Merrier: Towards Multi-Emotion and Intensity Controllable Response Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To infuse human-like behaviour in the agents, we introduce the task of multi-emotion controllable response generation with the ability to express different emotions with varying levels of intensity in an open-domain dialogue system. |
Mauajama Firdaus; Hardik Chauhan; Asif Ekbal; Pushpak Bhattacharyya; |
| 496 | LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods. |
Hao Fu; Shaojun Zhou; Qihong Yang; Junjie Tang; Guiquan Liu; Kaikui Liu; Xiaolong Li; |
| 497 | Nested Named Entity Recognition with Partially-Observed TreeCRFs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we view nested NER as constituency parsing with partially-observed trees and model it with partially-observed TreeCRFs. |
Yao Fu; Chuanqi Tan; Mosha Chen; Songfang Huang; Fei Huang; |
| 498 | A Theoretical Analysis of The Repetition Problem in Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new framework for theoretical analysis for the repetition problem. |
Zihao Fu; Wai Lam; Anthony Man-Cho So; Bei Shi; |
| 499 | Paragraph-level Commonsense Transformers with Recurrent Memory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the task of discourse-aware commonsense inference. |
Saadia Gabriel; Chandra Bhagavatula; Vered Shwartz; Ronan Le Bras; Maxwell Forbes; Yejin Choi; |
| 500 | Judgment Prediction Via Injecting Legal Knowledge Into Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. |
Leilei Gan; Kun Kuang; Yi Yang; Fei Wu; |
| 501 | Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. |
Lei Gao; Yulong Wang; Tongcun Liu; Jingyu Wang; Lei Zhang; Jianxin Liao; |
| 502 | Analogy Training Multilingual Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that analogy training not only improves the global consistency of mBERT, as well as the isomorphism of language-specific subspaces, but also leads to consistent gains on downstream tasks such as bilingual dictionary induction and sentence retrieval. |
Nicolas Garneau; Mareike Hartmann; Anders Sandholm; Sebastian Ruder; Ivan Vulić; Anders Søgaard; |
| 503 | Fake It Till You Make It: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To that end, we propose a self-supervised approach to model lexical semantic change based on the perturbation of word vectors in the input corpora. |
Maurício Gruppi; Pin-Yu Chen; Sibel Adali; |
| 504 | Perception Score: A Learned Metric for Open-ended Text Generation Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a learned evaluation metric: Perception Score. |
Jing Gu; Qingyang Wu; Zhou Yu; |
| 505 | DialogBERT: Discourse-Aware Response Generation Via Learning to Recover and Rank Utterances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To efficiently capture the discourse-level coherence among utterances, we propose two training objectives, including masked utterance regression and distributed utterance order ranking in analogy to the original BERT training. |
Xiaodong Gu; Kang Min Yoo; Jung-Woo Ha; |
| 506 | Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework called Multi-turn Multiple-choice Machine reading comprehension (M3) to solve the short text EL from a new perspective: a query is generated for each ambiguous mention exploiting its surrounding context, and an option selection module is employed to identify the golden entity from candidates using the query. |
Yingjie Gu; Xiaoye Qu; Zhefeng Wang; Baoxing Huai; Nicholas Jing Yuan; Xiaolin Gui; |
| 507 | Label Confusion Learning to Enhance Text Classification Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Label Confusion Model (LCM) as an enhancement component to current popular text classification models. |
Biyang Guo; Songqiao Han; Xiao Han; Hailiang Huang; Ting Lu; |
| 508 | Iterative Utterance Segmentation for Neural Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. |
Yinuo Guo; Zeqi Lin; Jian-Guang Lou; Dongmei Zhang; |
| 509 | BERT & Family Eat Word Salad: Experiments with Text Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. |
Ashim Gupta; Giorgi Kvernadze; Vivek Srikumar; |
| 510 | Sketch and Customize: A Counterfactual Story Generator Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a sketch-and-customize generation model guided by the causality implicated in the conditions and endings. |
Changying Hao; Liang Pang; Yanyan Lan; Yan Wang; Jiafeng Guo; Xueqi Cheng; |
| 511 | Self-Attention Attribution: Interpreting Information Interactions Inside Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a self-attention attribution method to interpret the information interactions inside Transformer. |
Yaru Hao; Li Dong; Furu Wei; Ke Xu; |
| 512 | Humor Knowledge Enriched Transformer for Understanding Multimodal Humor Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Humor Knowledge enriched Transformer (HKT) that can capture the gist of a multimodal humorous expression by integrating the preceding context and external knowledge. |
Md Kamrul Hasan; Sangwu Lee; Wasifur Rahman; Amir Zadeh; Rada Mihalcea; Louis-Philippe Morency; Ehsan Hoque; |
| 513 | Synchronous Interactive Decoding for Multilingual Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new approach for synchronous interactive multilingual neural machine translation (SimNMT), which predicts each target language output simultaneously and interactively using historical and future information of all target languages. |
Hao He; Qian Wang; Zhipeng Yu; Yang Zhao; Jiajun Zhang; Chengqing Zong; |
| 514 | Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We compared our proposed models with many strong baselines on two tasks, generating sentences with lexical constraints and text infilling. |
Xingwei He; Victor O.K. Li; |
| 515 | Towards Fully Automated Manga Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make the following four contributions that establishes the foundation of manga translation research. |
Ryota Hinami; Shonosuke Ishiwatari; Kazuhiko Yasuda; Yusuke Matsui; |
| 516 | SMART: A Situation Model for Algebra Story Problems Via Attributed Grammar Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such limits of neural solvers, we introduce the concept of a situation model, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose SMART, which adopts attributed grammar as the representation of situation models for algebra story problems. |
Yining Hong; Qing Li; Ran Gong; Daniel Ciao; Siyuan Huang; Song-Chun Zhu; |
| 517 | It Takes Two to Empathize: One to Seek and One to Provide Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We analyze user messages to identify the direction of empathy at a fine-grained level: seeking or providing empathy. |
Mahshid Hosseini; Cornelia Caragea; |
| 518 | C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this, we propose a novel Cluster-to-Cluster generation framework for Data Augmentation (DA), named C2C-GenDA. |
Yutai Hou; Sanyuan Chen; Wanxiang Che; Cheng Chen; Ting Liu; |
| 519 | Few-shot Learning for Multi-label Intent Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the few-shot multi-label classification for user intent detection. |
Yutai Hou; Yongkui Lai; Yushan Wu; Wanxiang Che; Ting Liu; |
| 520 | HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To take argument structure information into account, this paper proposes an approach to persuasiveness prediction with a novel graph-based neural network model, called heterogeneous argument attention network (HARGAN). |
Kuo-Yu Huang; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 521 | SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the incomplete utterance restoration which has brought general improvement over multi-turn dialogue systems in recent studies. |
Mengzuo Huang; Feng Li; Wuhe Zou; Weidong Zhang; |
| 522 | Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these issues, we propose an entity guided question generation model with contextual structure information and sequence information capturing. |
Qingbao Huang; Mingyi Fu; Linzhang Mo; Yi Cai; Jingyun Xu; Pijian Li; Qing Li; Ho-fung Leung; |
| 523 | Story Ending Generation with Multi-Level Graph Convolutional Networks Over Dependency Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this issue, we propose a context-aware Multi-level Graph Convolutional Networks over Dependency Parse (MGCN-DP) trees to capture dependency relations and context clues more effectively. |
Qingbao Huang; Linzhang Mo; Pijian Li; Yi Cai; Qingguang Liu; Jielong Wei; Qing Li; Ho-fung Leung; |
| 524 | Adaptive Beam Search Decoding for Discrete Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an adaptive generation model-AdaGM, which is mainly inspired by the importance of the first words in keyphrase generation. |
Xiaoli Huang; Tongge Xu; Lvan Jiao; Yueran Zu; Youmin Zhang; |
| 525 | Distribution Matching for Rationalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel rationalization method that matches the distributions of rationales and input text in both the feature space and output space. |
Yongfeng Huang; Yujun Chen; Yulun Du; Zhilin Yang; |
| 526 | Audio-Oriented Multimodal Machine Comprehension Via Dynamic Inter- and Intra-modality Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we target the problem of Audio-Oriented Multimodal Machine Comprehension, and its goal is to answer questions based on the given audio and textual information. |
Zhiqi Huang; Fenglin Liu; Xian Wu; Shen Ge; Helin Wang; Wei Fan; Yuexian Zou; |
| 527 | Unsupervised Learning of Discourse Structures Using A Tree Autoencoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we are inferring general tree structures of natural text in multiple domains, showing promising results on a diverse set of tasks. |
Patrick Huber; Giuseppe Carenini; |
| 528 | Dynamic Hybrid Relation Exploration Network for Cross-Domain Context-Dependent Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. |
Binyuan Hui; Ruiying Geng; Qiyu Ren; Binhua Li; Yongbin Li; Jian Sun; Fei Huang; Luo Si; Pengfei Zhu; Xiaodan Zhu; |
| 529 | DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the task of relation classification of interlocutors based on their dialogues. |
Qi Jia; Hongru Huang; Kenny Q. Zhu; |
| 530 | Flexible Non-Autoregressive Extractive Summarization with Threshold: How to Extract A Non-Fixed Number of Summary Sentences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a more flexible and accurate non-autoregressive method for single document extractive summarization, extracting a non-fixed number of summary sentences without the sorting step. |
Ruipeng Jia; Yanan Cao; Haichao Shi; Fang Fang; Pengfei Yin; Shi Wang; |
| 531 | EQG-RACE: Examination-Type Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. |
Xin Jia; Wenjie Zhou; Xu Sun; Yunfang Wu; |
| 532 | Hierarchical Macro Discourse Parsing Based on Topic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this issue, we introduce a topic segmentation mechanism to detect implicit topic boundaries and then help the document-level macro discourse parser to construct better discourse trees hierarchically. |
Feng Jiang; Yaxin Fan; Xiaomin Chu; Peifeng Li; Qiaoming Zhu; Fang Kong; |
| 533 | FIXMYPOSE: Pose Correctional Captioning and Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. |
Hyounghun Kim; Abhay Zala; Graham Burri; Mohit Bansal; |
| 534 | Self-supervised Pre-training and Contrastive Representation Learning for Multiple-choice Video QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in the main stage as an auxiliary learning. |
Seonhoon Kim; Seohyeong Jeong; Eunbyul Kim; Inho Kang; Nojun Kwak; |
| 535 | The Gap on Gap: Tackling The Problem of Differing Data Distributions in Bias-Measuring Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a theoretically grounded method for weighting test samples to cope with such patterns in the test data. |
Vid Kocijan; Oana-Maria Camburu; Thomas Lukasiewicz; |
| 536 | SALNet: Semi-supervised Few-Shot Text Classification with Attention-based Lexicon Construction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a semi-supervised bootstrap learning framework for few-shot text classification. |
Ju-Hyoung Lee; Sang-Ki Ko; Yo-Sub Han; |
| 537 | Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Multi-SpectroGAN (MSG), which can train the multi-speaker model with only the adversarial feedback by conditioning a self-supervised hidden representation of the generator to a conditional discriminator. |
Sang-Hoon Lee; Hyun-Wook Yoon; Hyeong-Rae Noh; Ji-Hoon Kim; Seong-Whan Lee; |
| 538 | Have We Solved The Hard Problem? It’s Not Easy! Contextual Lexical Contrast As A Means to Probe Neural Coherence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take initial steps to address contextual lexical relations by focusing on the contrast relation, as it is a well-known relation though it is more subtle and relatively less resourced. |
Wenqiang Lei; Yisong Miao; Runpeng Xie; Bonnie Webber; Meichun Liu; Tat-Seng Chua; Nancy F. Chen; |
| 539 | Learning Light-Weight Translation Models from Deep Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a natural step towards learning strong but light-weight NMT systems. |
Bei Li; Ziyang Wang; Hui Liu; Quan Du; Tong Xiao; Chunliang Zhang; Jingbo Zhu; |
| 540 | Improving The Efficiency and Effectiveness for BERT-based Entity Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this, we introduce a siamese network structure that independently encodes tuples using BERT but delays the pair-wise interaction via an enhanced alignment network. |
Bing Li; Yukai Miao; Yaoshu Wang; Yifang Sun; Wei Wang; |
| 541 | Multi-view Inference for Relation Extraction with Uncertain Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes to exploit uncertain knowledge to improve relation extraction. |
Bo Li; Wei Ye; Canming Huang; Shikun Zhang; |
| 542 | Towards Topic-Aware Slide Generation For Academic Papers With Unsupervised Mutual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to automatically generate slides for academic papers. |
Da-Wei Li; Danqing Huang; Tingting Ma; Chin-Yew Lin; |
| 543 | The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines Via Disentanglement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. |
Mingzhe Li; Xiuying Chen; Min Yang; Shen Gao; Dongyan Zhao; Rui Yan; |
| 544 | ACT: An Attentive Convolutional Transformer for Efficient Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce an Attentive Convolutional Transformer (ACT) that takes the advantages of both Transformer and CNN for efficient text classification. |
Pengfei Li; Peixiang Zhong; Kezhi Mao; Dongzhe Wang; Xuefeng Yang; Yunfeng Liu; Jianxiong Yin; Simon See; |
| 545 | Quantum-inspired Neural Network for Conversational Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a novel perspective on conversational emotion recognition by drawing an analogy between the task and a complete span of quantum measurement. |
Qiuchi Li; Dimitris Gkoumas; Alessandro Sordoni; Jian-Yun Nie; Massimo Melucci; |
| 546 | HopRetriever: Retrieve Hops Over Wikipedia to Answer Complex Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering. |
Shaobo Li; Xiaoguang Li; Lifeng Shang; Xin Jiang; Qun Liu; Chengjie Sun; Zhenzhou Ji; Bingquan Liu; |
| 547 | Merging Statistical Feature Via Adaptive Gate for Improved Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Adaptive Gate Network (AGN) to consolidate semantic representation with statistical features selectively. |
Xianming Li; Zongxi Li; Haoran Xie; Qing Li; |
| 548 | TSQA: Tabular Scenario Based Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator. |
Xiao Li; Yawei Sun; Gong Cheng; |
| 549 | Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input dialogue act (DA) into an utterance. |
Yangming Li; Kaisheng Yao; |
| 550 | An Efficient Transformer Decoder with Compressed Sub-layers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We thereby propose Compressed Attention Network, whose decoder layer consists of only one sub-layer instead of three. |
Yanyang Li; Ye Lin; Tong Xiao; Jingbo Zhu; |
| 551 | An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-level Structural Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the problem of unsupervised image-sentence matching. |
Zejun Li; Zhongyu Wei; Zhihao Fan; Haijun Shan; Xuanjing Huang; |
| 552 | Finding Sparse Structures for Domain Specific Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate the issue, we propose Prune-Tune, a novel domain adaptation method via gradual pruning. |
Jianze Liang; Chengqi Zhao; Mingxuan Wang; Xipeng Qiu; Lei Li; |
| 553 | Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a heterogeneous graph-based model for emotional conversation generation. |
Yunlong Liang; Fandong Meng; Ying Zhang; Yufeng Chen; Jinan Xu; Jie Zhou; |
| 554 | Hierarchical Coherence Modeling for Document Quality Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a hierarchical coherence model for document quality assessment. |
Dongliang Liao; Jin Xu; Gongfu Li; Yiru Wang; |
| 555 | Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. |
Shuai Lin; Pan Zhou; Xiaodan Liang; Jianheng Tang; Ruihui Zhao; Ziliang Chen; Liang Lin; |
| 556 | Neural Sentence Simplification with Semantic Dependency Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we leverage semantic dependency graph to aid neural sentence simplification system. |
Zhe Lin; Xiaojun Wan; |
| 557 | Converse, Focus and Guess – Towards Multi-Document Driven Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. |
Han Liu; Caixia Yuan; Xiaojie Wang; Yushu Yang; Huixing Jiang; Zhongyuan Wang; |
| 558 | Natural Language Inference in Context – Investigating Contextual Reasoning Over Long Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce ConTRoL, a new dataset for ConTextual Reasoning over Long texts. |
Hanmeng Liu; Leyang Cui; Jian Liu; Yue Zhang; |
| 559 | How to Train Your Agent to Read and Write Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text Writer that generates a novel paragraph, and a Reviewer that reviews the generated paragraph from three different aspects. |
Li Liu; Mengge He; Guanghui Xu; Mingkui Tan; Qi Wu; |
| 560 | Filling The Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history. |
Longxiang Liu; Zhuosheng Zhang; Hai Zhao; Xi Zhou; Xiang Zhou; |
| 561 | Towards Faithfulness in Open Domain Table-to-text Generation from An Entity-centric View Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by these findings, we propose two methods for faithful generation: 1) augmented training by incorporating the auxiliary entity information, including both an augmented plan-based model and an unsupervised model and 2) training instance selection based on faithfulness ranking. |
Tianyu Liu; Xin Zheng; Baobao Chang; Zhifang Sui; |
| 562 | Faster Depth-Adaptive Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. |
Yijin Liu; Fandong Meng; Jie Zhou; Yufeng Chen; Jinan Xu; |
| 563 | A Graph Reasoning Network for Multi-turn Response Selection Via Customized Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a graph- reasoning network (GRN) to address the problem. |
Yongkang Liu; Shi Feng; Daling Wang; Kaisong Song; Feiliang Ren; Yifei Zhang; |
| 564 | Generating CCG Categories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to generate categories rather than classify them: each category is decomposed into a sequence of smaller atomic tags, and the tagger aims to generate the correct sequence. |
Yufang Liu; Tao Ji; Yuanbin Wu; Man Lan; |
| 565 | CrossNER: Evaluating Cross-Domain Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. |
Zihan Liu; Yan Xu; Tiezheng Yu; Wenliang Dai; Ziwei Ji; Samuel Cahyawijaya; Andrea Madotto; Pascale Fung; |
| 566 | On The Importance of Word Order Information in Cross-lingual Sequence Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we hypothesize that reducing the word order information fitted into the models can improve the adaptation performance in target languages. |
Zihan Liu; Genta I Winata; Samuel Cahyawijaya; Andrea Madotto; Zhaojiang Lin; Pascale Fung; |
| 567 | SCRUPLES: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new method to estimate the best possible performance on such tasks with inherently diverse label distributions, and explore likelihood functions that separate intrinsic from model uncertainty. |
Nicholas Lourie; Ronan Le Bras; Yejin Choi; |
| 568 | UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on A New Multitask Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two new ways to evaluate commonsense models, emphasizing their generality on new tasks and building on diverse, recently introduced benchmarks. |
Nicholas Lourie; Ronan Le Bras; Chandra Bhagavatula; Yejin Choi; |
| 569 | Span-Based Event Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event coreference; (2) whether exploiting the dependency between event coreference and the related subtask of trigger detection; and (3) whether automatically computed entity coreference information can benefit span-based event coreference resolution. |
Jing Lu; Vincent Ng; |
| 570 | LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we introduce HowNet as an external knowledge base and propose a Linguistic knowledge Enhanced graph Transformer (LET) to deal with word ambiguity. |
Boer Lyu; Lu Chen; Su Zhu; Kai Yu; |
| 571 | Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. |
Kaixin Ma; Filip Ilievski; Jonathan Francis; Yonatan Bisk; Eric Nyberg; Alessandro Oltramari; |
| 572 | Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented, and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding \texttt{condition} such as named-entity tag, semantic role label, or sentiment. |
Nishtha Madaan; Inkit Padhi; Naveen Panwar; Diptikalyan Saha; |
| 573 | Generating Natural Language Attacks in A Hard Label Black Box Setting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. |
Rishabh Maheshwary; Saket Maheshwary; Vikram Pudi; |
| 574 | Bridging Towers of Multi-task Learning with A Gating Mechanism for Aspect-based Sentiment Analysis and Sequential Metaphor Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel gating mechanism for the bridging of MTL towers. |
Rui Mao; Xiao Li; |
| 575 | A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a complete solution for ABSA. |
Yue Mao; Yi Shen; Chao Yu; Longjun Cai; |
| 576 | Variational Inference for Learning Representations of Natural Language Edits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. |
Edison Marrese-Taylor; Machel Reid; Yutaka Matsuo; |
| 577 | How Robust Are Model Rankings : A Leaderboard Customization Approach for Equitable Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a task-agnostic method to probe leaderboards by weighting samples based on their ‘difficulty’ level. |
Swaroop Mishra; Anjana Arunkumar; |
| 578 | Continual Learning for Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we introduce a novel Continual Learning approach for NER, which requires new training material to be annotated only for the new entities. |
Natawut Monaikul; Giuseppe Castellucci; Simone Filice; Oleg Rokhlenko; |
| 579 | MASKER: Masked Keyword Regularization for Reliable Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In light of this observation, we propose a simple yet effective fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. |
Seung Jun Moon; Sangwoo Mo; Kimin Lee; Jaeho Lee; Jinwoo Shin; |
| 580 | Disentangled Motif-aware Graph Learning for Phrase Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel graph learning framework for phrase grounding in the image. |
Zongshen Mu; Siliang Tang; Jie Tan; Qiang Yu; Yueting Zhuang; |
| 581 | Knowledge-aware Named Entity Recognition with Alleviating Heterogeneity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better incorporate and denoise the abundant knowledge in KBs, we propose a new KB-aware NER framework (KaNa), which utilizes type-heterogeneous knowledge to improve NER. |
Binling Nie; Ruixue Ding; Pengjun Xie; Fei Huang; Chen Qian; Luo Si; |
| 582 | Dialog Policy Learning for Joint Clarification and Active Learning Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we train a hierarchical dialog policy to jointly perform {\it both} clarification and active learning in the context of an interactive language-based image retrieval task motivated by an online shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions. |
Aishwarya Padmakumar; Raymond J. Mooney; |
| 583 | The Heads Hypothesis: A Unifying Statistical Approach Towards Understanding Multi-Headed Attention in BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formalize a simple yet effective score that generalizes to all the roles of attention heads and employs hypothesis testing on this score for robust inference. |
Madhura Pande; Aakriti Budhraja; Preksha Nema; Pratyush Kumar; Mitesh M. Khapra; |
| 584 | Copy That! Editing Sequences By Copying Spans Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that common seq2seq models (with a facility to copy single tokens) are not a natural fit for such tasks, as they have to explicitly copy each unchanged token. |
Sheena Panthaplackel; Miltiadis Allamanis; Marc Brockschmidt; |
| 585 | Movie Summarization Via Sparse Graph Construction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a model that identifies TP scenes by building a sparse movie graph that represents relations between scenes and is constructed using multimodal information. |
Pinelopi Papalampidi; Frank Keller; Mirella Lapata; |
| 586 | On The Softmax Bottleneck of Recurrent Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show via an extensive empirical study that such a correlation is fairly weak and that the high-rank of the log P matrix is neither necessary nor sufficient for better test perplexity. |
Dwarak Govind Parthiban; Yongyi Mao; Diana Inkpen; |
| 587 | XL-WSD: An Extra-Large and Cross-Lingual Evaluation Framework for Word Sense Disambiguation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we untangle this situation by proposing XL-WSD, a cross-lingual evaluation benchmark for the WSD task featuring sense-annotated development and test sets in 18 languages from six different linguistic families, together with language-specific silver training data. |
Tommaso Pasini; Alessandro Raganato; Roberto Navigli; |
| 588 | ALP-KD: Attention-Based Layer Projection for Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This shortcoming directly impacts quality, so we instead propose a combinatorial technique which relies on attention. |
Peyman Passban; Yimeng Wu; Mehdi Rezagholizadeh; Qun Liu; |
| 589 | Data Augmentation for Abstractive Query-Focused Multi-Document Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present two QMDS training datasets, which we construct using two data augmentation methods: (1) transferring the commonly used single-document CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2) mining search-query logs to create the QMDSIR dataset. |
Ramakanth Pasunuru; Asli Celikyilmaz; Michel Galley; Chenyan Xiong; Yizhe Zhang; Mohit Bansal; Jianfeng Gao; |
| 590 | Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We evaluate multiple contextual encoders and methods, proven to be efficient, on three common datasets for intent classification, expanded with out-of-domain utterances. |
Alexander Podolskiy; Dmitry Lipin; Andrey Bout; Ekaterina Artemova; Irina Piontkovskaya; |
| 591 | Conceptualized and Contextualized Gaussian Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Word embedding can represent a word as a point vector or a Gaussian distribution in high-dimensional spaces. Gaussian distribution is innately more expressive than point vector … |
Chen Qian; Fuli Feng; Lijie Wen; Tat-Seng Chua; |
| 592 | A Student-Teacher Architecture for Dialog Domain Adaptation Under The Meta-Learning Setting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an efficient domain adaptive task-oriented dialog system model, which incorporates a meta-teacher model to emphasize the different impacts between generated tokens with respect to the context. |
Kun Qian; Wei Wei; Zhou Yu; |
| 593 | Exploring Auxiliary Reasoning Tasks for Task-oriented Dialog Systems with Meta Cooperative Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Meta Cooperative Learning (MCL) framework for task-oriented dialog systems (TDSs). |
Bowen Qin; Min Yang; Lidong Bing; Qingshan Jiang; Chengming Li; Ruifeng Xu; |
| 594 | Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two tasks. |
Libo Qin; Zhouyang Li; Wanxiang Che; Minheng Ni; Ting Liu; |
| 595 | Reinforced History Backtracking for Conversational Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these problems, we propose a reinforcement learning based method to capture and backtrack the related conversation history to boost model performance in this paper. |
Minghui Qiu; Xinjing Huang; Cen Chen; Feng Ji; Chen Qu; Wei Wei; Jun Huang; Yin Zhang; |
| 596 | Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a novel NAT framework ReorderNAT which explicitly models the reordering information to guide the decoding of NAT. |
Qiu Ran; Yankai Lin; Peng Li; Jie Zhou; |
| 597 | Towards Semantics-Enhanced Pre-Training: Can Lexicon Definitions Help Learning Sentence Meanings? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we try to inform the pre-trained masked language models of word meanings for semantics-enhanced pre-training. |
Xuancheng Ren; Xu Sun; Houfeng Wang; Qun Liu; |
| 598 | Automated Cross-prompt Scoring of Essay Traits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, to address this need, we introduce a new task named Automated Cross-prompt Scoring of Essay Traits, which requires the model to be trained solely on non-target-prompt essays and to predict the holistic, overall score as well as scores for a number of specific traits for target-prompt essays. |
Robert Ridley; Liang He; Xin-yu Dai; Shujian Huang; Jiajun Chen; |
| 599 | Exploring Transfer Learning For End-to-End Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an E2E system that is designed to jointly train on multiple speech-to-text tasks, such as ASR (speech-transcription) and SLU (speech-hypothesis), and text-to-text tasks, such as NLU (text-hypothesis). |
Subendhu Rongali; Beiye Liu; Liwei Cai; Konstantine Arkoudas; Chengwei Su; Wael Hamza; |
| 600 | Semantics Altering Modifications for Evaluating Comprehension in Machine Reading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method to automatically generate and align challenge sets featuring original and altered examples. |
Viktor Schlegel; Goran Nenadic; Riza Batista-Navarro; |
| 601 | Learning from The Best: Rationalizing Predictions By Adversarial Information Calibration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to squeeze more information from the predictor via an information calibration method. |
Lei Sha; Oana-Maria Camburu; Thomas Lukasiewicz; |
| 602 | Nutri-bullets: Summarizing Health Studies By Composing Segments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Nutri-bullets, a multi-document summarization task for health and nutrition. |
Darsh J Shah; Lili Yu; Tao Lei; Regina Barzilay; |
| 603 | DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose an all-in-one XLNet model, namely DialogXL, with enhanced memory to store longer historical context and dialog-aware self-attention to deal with the multi-party structures. |
Weizhou Shen; Junqing Chen; Xiaojun Quan; Zhixian Xie; |
| 604 | SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose SongMASS to address the above challenges, which leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling for lyric-to-melody and melody-to-lyric generation. |
Zhonghao Sheng; Kaitao Song; Xu Tan; Yi Ren; Wei Ye; Shikun Zhang; Tao Qin; |
| 605 | Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate these issues, we present a model pretraining framework, Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterance and table schemas, by leveraging generation models to generate high-quality pre-train data. |
Peng Shi; Patrick Ng; Zhiguo Wang; Henghui Zhu; Alexander Hanbo Li; Jun Wang; Cicero Nogueira dos Santos; Bing Xiang; |
| 606 | A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. … |
Tian Shi; Liuqing Li; Ping Wang; Chandan K. Reddy; |
| 607 | Fact-Enhanced Synthetic News Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand the potential threats of synthetic news, we develop a novel generation method FACTGEN to generate high-quality news content. |
Kai Shu; Yichuan Li; Kaize Ding; Huan Liu; |
| 608 | Improving Commonsense Causal Reasoning By Adversarial Training and Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a number of techniques for making models more robust in the domain of causal reasoning. |
Ieva Staliūnaitė; Philip John Gorinski; Ignacio Iacobacci; |
| 609 | Re-TACRED: Addressing Shortcomings of The TACRED Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. |
George Stoica; Emmanouil Antonios Platanios; Barnabas Poczos; |
| 610 | Progressive Multi-task Learning with Controlled Information Flow for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve this objective, in this paper we design a multitask learning architecture based on the observation that correlations exist between outputs of some related tasks (e.g. entity recognition and relation extraction tasks), and they reflect the relevant features that need to be extracted from the input. |
Kai Sun; Richong Zhang; Samuel Mensah; Yongyi Mao; Xudong Liu; |
| 611 | RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a method of text-image relation propagation into the multimodal BERT model. |
Lin Sun; Jiquan Wang; Kai Zhang; Yindu Su; Fangsheng Weng; |
| 612 | Unsupervised Learning of Deterministic Dialogue Structure with Edge-Enhanced Graph Auto-Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a conversational graph (CG) to represent deterministic dialogue structure where nodes and edges represent the utterance and context information respectively. |
Yajing Sun; Yong Shan; Chengguang Tang; Yue Hu; Yinpei Dai; Jing Yu; Jian Sun; Fei Huang; Luo Si; |
| 613 | VisualMRC: Machine Reading Comprehension on Document Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we introduce a new visual machine reading comprehension dataset, named VisualMRC, wherein given a question and a document image, a machine reads and comprehends texts in the image to answer the question in natural language. |
Ryota Tanaka; Kyosuke Nishida; Sen Yoshida; |
| 614 | A Bidirectional Multi-paragraph Reading Model for Zero-shot Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To leverage more textual information and enhance text understanding capability, we propose a bidirectional multi-paragraph reading model for the zero-shot entity linking task. |
Hongyin Tang; Xingwu Sun; Beihong Jin; Fuzheng Zhang; |
| 615 | Ideography Leads Us to The Field of Cognition: A Radical-Guided Associative Model for Chinese Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we draw inspirations from cognitive principles between ideography and human associative behavior to propose a novel Radical-guided Associative Model (RAM) for Chinese text classification. |
Hanqing Tao; Shiwei Tong; Kun Zhang; Tong Xu; Qi Liu; Enhong Chen; Min Hou; |
| 616 | Learning from My Friends: Few-Shot Personalized Conversation Systems Via Social Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a few-shot personalized conversation task with an auxiliary social network. |
Zhiliang Tian; Wei Bi; Zihan Zhang; Dongkyu Lee; Yiping Song; Nevin L. Zhang; |
| 617 | FL-MSRE: A Few-Shot Learning Based Approach to Multimodal Social Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the success of BERT, we propose a strong BERT based baseline to extract social relation from text only. |
Hai Wan; Manrong Zhang; Jianfeng Du; Ziling Huang; Yufei Yang; Jeff Z. Pan; |
| 618 | KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Knowledge-Enriched Meta-Learning (KEML) framework to address lexical relation classification. |
Chengyu Wang; Minghui Qiu; Jun Huang; Xiaofeng He; |
| 619 | Exploring Explainable Selection to Control Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, to begin prying open the black box and to inject a level of control into the substance of the final summary, we developed a novel select-and-generate framework that focuses on explainability. |
Haonan Wang; Yang Gao; Yu Bai; Mirella Lapata; Heyan Huang; |
| 620 | Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. |
Jixuan Wang; Kai Wei; Martin Radfar; Weiwei Zhang; Clement Chung; |
| 621 | Effective Slot Filling Via Weakly-Supervised Dual-Model Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By using some particular weakly labeled data, namely the plain phrases included in sentences, we propose a weakly-supervised slot filling approach. |
Jue Wang; Ke Chen; Lidan Shou; Sai Wu; Gang Chen; |
| 622 | Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. |
Jun Wang; Max W. Y. Lam; Dan Su; Dong Yu; |
| 623 | Bridging The Domain Gap: Improve Informal Language Translation Via Counterfactual Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a counterfactual domain adaptation method to better leverage both large-scale source-domain data (formal texts) and small-scale target-domain data (informal texts). |
Ke Wang; Guandan Chen; Zhongqiang Huang; Xiaojun Wan; Fei Huang; |
| 624 | Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, two kinds of interaction states are defined based on schema items and SQL keywords separately. |
Run-Ze Wang; Zhen-Hua Ling; Jingbo Zhou; Yu Hu; |
| 625 | Generating Diversified Comments Via Reader-Aware Topic Modeling and Saliency Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a unified reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments. |
Wei Wang; Piji Li; Hai-Tao Zheng; |
| 626 | Adversarial Training with Fast Gradient Projection Method Against Synonym Substitution Based Text Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thereby, we propose a fast text adversarial attack method called Fast Gradient Projection Method (FGPM) based on synonym substitution, which is about 20 times faster than existing text attack methods and could achieve similar attack performance. |
Xiaosen Wang; Yichen Yang; Yihe Deng; Kun He; |
| 627 | NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. |
Xiaoyang Wang; Chen Li; Jianqiao Zhao; Dong Yu; |
| 628 | Code Completion By Modeling Flattened Abstract Syntax Trees As Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these problems, we propose a new code completion approach named CCAG, which models the flattened sequence of a partial AST as an AST graph. |
Yanlin Wang; Hui Li; |
| 629 | Robustness to Spurious Correlations in Text Classification Via Automatically Generated Counterfactuals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to train a robust text classifier by augmenting the training data with automatically generated counterfactual data. |
Zhao Wang; Aron Culotta; |
| 630 | MLE-Guided Parameter Search for Task Loss Minimization in Neural Sequence Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop an alternative method based on random search in the parameter space that leverages access to the maximum likelihood gradient. |
Sean Welleck; Kyunghyun Cho; |
| 631 | Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. |
Taesun Whang; Dongyub Lee; Dongsuk Oh; Chanhee Lee; Kijong Han; Dong-hun Lee; Saebyeok Lee; |
| 632 | On Scalar Embedding of Relative Positions in Attention Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the scalar relative positional encoding (SRPE) proposed in the T5 transformer. |
Junshuang Wu; Richong Zhang; Yongyi Mao; Junfan Chen; |
| 633 | Evidence Inference Networks for Interpretable Claim Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose evidence inference networks (EVIN), which focus on the conflicts questioning the core semantics of claims and serve as evidence for interpretable claim verification. |
Lianwei Wu; Yuan Rao; Ling Sun; Wangbo He; |
| 634 | TextGAIL: Generative Adversarial Imitation Learning for Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. |
Qingyang Wu; Lei Li; Zhou Yu; |
| 635 | MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. |
Te-Lin Wu; Shikhar Singh; Sayan Paul; Gully Burns; Nanyun Peng; |
| 636 | A Controllable Model of Grounded Response Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a framework that we call controllable grounded response generation (CGRG), in which lexical control phrases are either provided by a user or automatically extracted by a control phrase predictor from dialogue context and grounding knowledge. |
Zeqiu Wu; Michel Galley; Chris Brockett; Yizhe Zhang; Xiang Gao; Chris Quirk; Rik Koncel-Kedziorski; Jianfeng Gao; Hannaneh Hajishirzi; Mari Ostendorf; Bill Dolan; |
| 637 | Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate whether adding context to self-attention models improves performance on (T)ABSA. |
Zhengxuan Wu; Desmond C. Ong; |
| 638 | Does Head Label Help for Long-Tailed Multi-Label Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. |
Lin Xiao; Xiangliang Zhang; Liping Jing; Chi Huang; Mingyang Song; |
| 639 | Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we solve this problem by developing a novel adversarial meta sampling (AMS) approach to improve MML-ASR. |
Yubei Xiao; Ke Gong; Pan Zhou; Guolin Zheng; Xiaodan Liang; Liang Lin; |
| 640 | Improving Tree-Structured Decoder Training for Code Generation Via Mutual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Under this framework, we continuously enhance both two models via mutual distillation, which involves synchronous executions of two one-to-one knowledge transfers at each training step. |
Binbin Xie; Jinsong Su; Yubin Ge; Xiang Li; Jianwei Cui; Junfeng Yao; Bin Wang; |
| 641 | Enabling Fast and Universal Audio Adversarial Attack Using Generative Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. |
Yi Xie; Zhuohang Li; Cong Shi; Jian Liu; Yingying Chen; Bo Yuan; |
| 642 | Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we propose Nyströmformer – a model that exhibits favorable scalability as a function of sequence length. |
Yunyang Xiong; Zhanpeng Zeng; Rudrasis Chakraborty; Mingxing Tan; Glenn Fung; Yin Li; Vikas Singh; |
| 643 | Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate such entity structure as distinctive dependencies between mention pairs. |
Benfeng Xu; Quan Wang; Yajuan Lyu; Yong Zhu; Zhendong Mao; |
| 644 | Learning An Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models. |
Ruijian Xu; Chongyang Tao; Daxin Jiang; Xueliang Zhao; Dongyan Zhao; Rui Yan; |
| 645 | Document-Level Relation Extraction with Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE. |
Wang Xu; Kehai Chen; Tiejun Zhao; |
| 646 | Topic-Aware Multi-turn Dialogue Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead of taking topic-agnostic n-gram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. |
Yi Xu; Hai Zhao; Zhuosheng Zhang; |
| 647 | A Supervised Multi-Head Self-Attention Network for Nested Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we treat named entity recognition as a multi-class classification of word pairs and design a simple neural model to handle this issue. |
Yongxiu Xu; Heyan Huang; Chong Feng; Yue Hu; |
| 648 | GDPNet: Refining Latent Multi-View Graph for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. |
Fuzhao Xue; Aixin Sun; Hao Zhang; Eng Siong Chng; |
| 649 | Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes human-interpretable learning of aspect-based sentiment analysis (ABSA), employing the recently introduced Tsetlin Machines (TMs). |
Rohan K Yadav; Lei Jiao; Ole-Christoffer Granmo; Morten Goodwin; |
| 650 | Style-transfer and Paraphrase: Looking for A Sensible Semantic Similarity Metric Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper provides a comprehensive analysis for more than a dozen of such methods. |
Ivan P. Yamshchikov; Viacheslav Shibaev; Nikolay Khlebnikov; Alexey Tikhonov; |
| 651 | Multi-Document Transformer for Personality Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-document Transformer, namely Transformer-MD, to tackle the above issues. |
Feifan Yang; Xiaojun Quan; Yunyi Yang; Jianxing Yu; |
| 652 | UBAR: Towards Fully End-to-End Task-Oriented Dialog System with GPT-2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. |
Yunyi Yang; Yunhao Li; Xiaojun Quan; |
| 653 | Open Domain Dialogue Generation with Latent Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that the visual scene information at the time of a conversation can be represented by an image, and trying to recover the latent images of the textual dialogues through text-to-image generation techniques. |
Ze Yang; Wei Wu; Huang Hu; Can Xu; Wei Wang; Zhoujun Li; |
| 654 | Adversarial Language Games for Advanced Natural Language Intelligence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. |
Yuan Yao; Haoxi Zhong; Zhengyan Zhang; Xu Han; Xiaozhi Wang; Kai Zhang; Chaojun Xiao; Guoyang Zeng; Zhiyuan Liu; Maosong Sun; |
| 655 | Contrastive Triple Extraction with Generative Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit the end-to-end triple extraction task for sequence generation. |
Hongbin Ye; Ningyu Zhang; Shumin Deng; Mosha Chen; Chuanqi Tan; Fei Huang; Huajun Chen; |
| 656 | Unanswerable Question Correction in Question Answering Over Personal Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For refining question, we propose a question generation model based on the reinforcement learning (RL) with question editing mechanism. |
An-Zi Yen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 657 | Simpson’s Bias in NLP Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we systematically investigate the above assumption in several NLP tasks. |
Fei Yuan; Longtu Zhang; Huang Bojun; Yaobo Liang; |
| 658 | Reinforced Multi-Teacher Selection for Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we observe that, due to the complexity of training examples and the differences in student model capability, learning differentially from teacher models can lead to better performance of student models distilled. |
Fei Yuan; Linjun Shou; Jian Pei; Wutao Lin; Ming Gong; Yan Fu; Daxin Jiang; |
| 659 | What’s The Best Place for An AI Conference, Vancouver or _______: Why Completing Comparative Questions Is Difficult Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we study using such LMs to fill in entities in human-authored comparative questions, like “Which country is older, India or _____?” |
Avishai Zagoury; Einat Minkov; Idan Szpektor; William W. Cohen; |
| 660 | Probing Product Description Generation Via Posterior Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. |
Haolan Zhan; Hainan Zhang; Hongshen Chen; Lei Shen; Zhuoye Ding; Yongjun Bao; Weipeng Yan; Yanyan Lan; |
| 661 | Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to alleviate this issue by proposing a novel meta-curriculum learning for domain adaptation in NMT. |
Runzhe Zhan; Xuebo Liu; Derek F. Wong; Lidia S. Chao; |
| 662 | UWSpeech: Speech to Speech Translation for Unwritten Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method called XL-VAE, which enhances vector quantized variational autoencoder (VQ-VAE) with cross-lingual (XL) speech recognition, to train the converter and inverter of UWSpeech jointly. |
Chen Zhang; Xu Tan; Yi Ren; Tao Qin; Kejun Zhang; Tie-Yan Liu; |
| 663 | Building Interpretable Interaction Trees for Deep NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. |
Die Zhang; Hao Zhang; Huilin Zhou; Xiaoyi Bao; Da Huo; Ruizhao Chen; Xu Cheng; Mengyue Wu; Quanshi Zhang; |
| 664 | Multi-modal Multi-label Emotion Recognition with Heterogeneous Hierarchical Message Passing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on multi-modal emotion recognition in a multi-label scenario. |
Dong Zhang; Xincheng Ju; Wei Zhang; Junhui Li; Shoushan Li; Qiaoming Zhu; Guodong Zhou; |
| 665 | Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with this issue, we propose a unified multi-modal graph fusion (UMGF) approach for MNER. |
Dong Zhang; Suzhong Wei; Shoushan Li; Hanqian Wu; Qiaoming Zhu; Guodong Zhou; |
| 666 | Accelerating Neural Machine Translation with Partial Word Embedding Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Partial Vector Quantization (P-VQ) for NMT models, which can both compress the word embedding matrix and accelerate word probability prediction in the softmax layer. |
Fan Zhang; Mei Tu; Jinyao Yan; |
| 667 | Discovering New Intents with Deep Aligned Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data. |
Hanlei Zhang; Hua Xu; Ting-En Lin; Rui Lyu; |
| 668 | Deep Open Intent Classification with Adaptive Decision Boundary Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. |
Hanlei Zhang; Hua Xu; Ting-En Lin; |
| 669 | Writing Polishment with Simile: Task, Dataset and A Neural Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. |
Jiayi Zhang; Zhi Cui; Xiaoqiang Xia; Yalong Guo; Yanran Li; Chen Wei; Jianwei Cui; |
| 670 | Continuous Self-Attention Models with Neural ODE Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response to this issue, we propose a lightweight architecture named Continuous Self-Attention models with neural ODE networks (CSAODE). |
Jing Zhang; Peng Zhang; Baiwen Kong; Junqiu Wei; Xin Jiang; |
| 671 | TaLNet: Voice Reconstruction from Tongue and Lip Articulation with Transfer Learning from Text-to-Speech Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents TaLNet, a model for voice reconstruction with ultrasound tongue and optical lip videos as inputs. |
Jing-Xuan Zhang; Korin Richmond; Zhen-Hua Ling; Lirong Dai; |
| 672 | Making The Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. |
Kun Zhang; Le Wu; Guangyi Lv; Meng Wang; Enhong Chen; Shulan Ruan; |
| 673 | MERL: Multimodal Event Representation Learning in Heterogeneous Embedding Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Multimodal Event Representation Learning framework (MERL) to learn event representations based on both text and image modalities simultaneously. |
Linhai Zhang; Deyu Zhou; Yulan He; Zeng Yang; |
| 674 | Future-Guided Incremental Transformer for Simultaneous Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For the low training speed, we propose an incremental Transformer with an average embedding layer (AEL) to accelerate the speed of calculation of the hidden states during training. |
Shaolei Zhang; Yang Feng; Liangyou Li; |
| 675 | Semantics-Aware Inferential Network for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. |
Shuiliang Zhang; Hai Zhao; Junru Zhou; Xi Zhou; Xiang Zhou; |
| 676 | Learning to Check Contract Inconsistencies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate a novel Contract Inconsistency Checking (CIC) problem, and design an end-to-end framework, called Pair-wise Blank Resolution (PBR), to solve the CIC problem with high accuracy. |
Shuo Zhang; Junzhou Zhao; Pinghui Wang; Nuo Xu; Yang Yang; Yiting Liu; Yi Huang; Junlan Feng; |
| 677 | Self-supervised Bilingual Syntactic Alignment for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows the first attempt of a source-target bilingual syntactic alignment approach SyntAligner by mutual information maximization-based self-supervised neural deep modeling. |
Tianfu Zhang; Heyan Huang; Chong Feng; Longbing Cao; |
| 678 | Graph-Based Tri-Attention Network for Answer Ranking in CQA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the limitations, we devise a novel graph-based tri-attention network, namely GTAN, which has two innovations. |
Wei Zhang; Zeyuan Chen; Chao Dong; Wen Wang; Hongyuan Zha; Jianyong Wang; |
| 679 | Circles Are Like Ellipses, or Ellipses Are Like Circles? Measuring The Degree of Asymmetry of Static and Contextual Word Embeddings and The Implications to Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we use three well-known evocation datasets for the purpose and study both static embedding as well as contextual embedding, such as BERT. |
Wei Zhang; Murray Campbell; Yang Yu; Sadhana Kumaravel; |
| 680 | Denoising Distantly Supervised Named Entity Recognition Via A Hypergeometric Probabilistic Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. |
Wenkai Zhang; Hongyu Lin; Xianpei Han; Le Sun; Huidan Liu; Zhicheng Wei; Nicholas Yuan; |
| 681 | Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). |
Xinyuan Zhang; Ruiyi Zhang; Manzil Zaheer; Amr Ahmed; |
| 682 | News Content Completion with Location-Aware Image Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel end-to-end two-stage framework to address these issues comprehensively. |
Zhengkun Zhang; Jun Wang; Adam Jatowt; Zhe Sun; Shao-Ping Lu; Zhenglu Yang; |
| 683 | Retrospective Reader for Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yields an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction. |
Zhuosheng Zhang; Junjie Yang; Hai Zhao; |
| 684 | Dynamic Modeling Cross- and Self-Lattice Attention Network for Chinese NER Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DCSAN, a Dynamic Cross- and Self-lattice Attention Network that aims to model dense interactions over word-character lattice structure for Chinese NER. |
Shan Zhao; Minghao Hu; Zhiping Cai; Haiwen Chen; Fang Liu; |
| 685 | A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these challenges, in this paper, we propose a unified multi-task learning framework to divide the task into three interacted sub-tasks. |
Tianyang Zhao; Zhao Yan; Yunbo Cao; Zhoujun Li; |
| 686 | LIREx: Augmenting Language Inference with Relevant Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these limitations, we propose a novel framework, LIREx, that incorporates both a rationale-enabled explanation generator and an instance selector to select only relevant, plausible NLEs to augment NLI models. |
Xinyan Zhao; V.G.Vinod Vydiswaran; |
| 687 | Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which replaces the traditional random sampling method with a teacher policy model to realize the dialogue policy for automatic curriculum learning. |
Yangyang Zhao; Zhenyu Wang; Zhenhua Huang; |
| 688 | Interactive Speech and Noise Modeling for Speech Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel idea to model speech and noise simultaneously in a two-branch convolutional neural network, namely SN-Net. |
Chengyu Zheng; Xiulian Peng; Yuan Zhang; Sriram Srinivasan; Yan Lu; |
| 689 | Stylized Dialogue Response Generation Using Stylized Unpaired Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a stylized dialogue generation method that can capture stylistic features embedded in unpaired texts. |
Yinhe Zheng; Zikai Chen; Rongsheng Zhang; Shilei Huang; Xiaoxi Mao; Minlie Huang; |
| 690 | Keyword-Guided Neural Conversational Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. |
Peixiang Zhong; Yong Liu; Hao Wang; Chunyan Miao; |
| 691 | CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. |
Peixiang Zhong; Di Wang; Pengfei Li; Chen Zhang; Hao Wang; Chunyan Miao; |
| 692 | MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these limitations, this paper proposes a multi-task adversarial active learning model for medical named entity recognition and normalization. |
Baohang Zhou; Xiangrui Cai; Ying Zhang; Wenya Guo; Xiaojie Yuan; |
| 693 | A Neural Group-wise Sentiment Analysis Model with Data Sparsity Awareness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, a neural group-wise sentiment analysis model with data sparsity awareness is proposed. |
Deyu Zhou; Meng Zhang; Linhai Zhang; Yulan He; |
| 694 | EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we are interested in knowing whether LDA models are vulnerable to adversarial perturbations of benign document examples during inference time. |
Qi Zhou; Haipeng Chen; Yitao Zheng; Zhen Wang; |
| 695 | Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems. |
Wenxuan Zhou; Kevin Huang; Tengyu Ma; Jing Huang; |
| 696 | IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze the isotropy of the pre-trained [CLS] embeddings of PTLMs with straightforward visualization, and point out two major issues: high variance in their standard deviation, and high correlation between different dimensions. |
Wenxuan Zhou; Bill Yuchen Lin; Xiang Ren; |
| 697 | An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Adaptive Hybrid Framework (AHF) for cross-domain aspect-based sentiment analysis. |
Yan Zhou; Fuqing Zhu; Pu Song; Jizhong Han; Tao Guo; Songlin Hu; |
| 698 | What The Role Is Vs. What Plays The Role: Semi-Supervised Event Argument Extraction Via Dual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DualQA, a novel framework, which models the event argument extraction task as question answering to alleviate the problem of data sparseness and leverage the duality of event argument recognition which is to ask "What plays the role", as well as event role recognition which is to ask "What the role is", to mutually improve each other.Experimental results on two datasets prove the effectiveness of our approach, especially in extremely low-resource situations. |
Yang Zhou; Yubo Chen; Jun Zhao; Yin Wu; Jiexin Xu; Jinlong Li; |
| 699 | Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. |
Yichao Zhou; Yu Yan; Rujun Han; J. Harry Caufield; Kai-Wei Chang; Yizhou Sun; Peipei Ping; Wei Wang; |
| 700 | Neural Sentence Ordering Based on Constraint Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the observation that sentence order at different distances may rely on different types of information, we devise a new approach based on multi-granular orders between sentences. |
Yutao Zhu; Kun Zhou; Jian-Yun Nie; Shengchao Liu; Zhicheng Dou; |
| 701 | Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas from dialogues. |
Yicheng Zou; Lujun Zhao; Yangyang Kang; Jun Lin; Minlong Peng; Zhuoren Jiang; Changlong Sun; Qi Zhang; Xuanjing Huang; Xiaozhong Liu; |
| 702 | Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel unsupervised framework called RankAE to perform chat summarization without employing manually labeled data. |
Yicheng Zou; Jun Lin; Lujun Zhao; Yangyang Kang; Zhuoren Jiang; Changlong Sun; Qi Zhang; Xuanjing Huang; Xiaozhong Liu; |
| 703 | The Undergraduate Games Corpus: A Dataset for Machine Perception of Interactive Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Machine perception research primarily focuses on processing static inputs (e.g. images and texts). |
Barrett R. Anderson; Adam M. Smith; |
| 704 | Efficient Poverty Mapping from High Resolution Remote Sensing Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce acquisition costs while maintaining accuracy, we propose a reinforcement learning approach in which free low-resolution imagery is used to dynamically identify where to acquire costly high-resolution images, prior to performing a deep learning task on the high-resolution images. |
Kumar Ayush; Burak Uzkent; Kumar Tanmay; Marshall Burke; David Lobell; Stefano Ermon; |
| 705 | Optimal Kidney Exchange with Immunosuppressants Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contribution is a range of efficient algorithms that provide flexibility in terms of meeting meaningful objectives. |
Haris Aziz; Ágnes Cseh; John P. Dickerson; Duncan C. McElfresh; |
| 706 | TreeCaps: Tree-Based Capsule Networks for Source Code Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We have proposed a new learning technique, named TreeCaps, by fusing together capsule networks with tree-based convolutional neural networks to achieve a learning accuracy higher than some existing graph-based techniques while it is based only on trees. |
Nghi D. Q. Bui; Yijun Yu; Lingxiao Jiang; |
| 707 | A Bottom-Up DAG Structure Extraction Model for Math Word Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Seq2DAG approach to extract the equation set directly as a DAG structure. |
Yixuan Cao; Feng Hong; Hongwei Li; Ping Luo; |
| 708 | Diagnose Like A Pathologist: Weakly-Supervised Pathologist-Tree Network for Slide-Level Immunohistochemical Scoring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the fact that pathologists jointly analyze visual fields at multiple powers of objective for diagnostic predictions, we propose a Pathologist-Tree Network (PTree-Net) to sparsely model the WSI efficiently in multi-scale manner. |
Zhen Chen; Jun Zhang; Shuanlong Che; Junzhou Huang; Xiao Han; Yixuan Yuan; |
| 709 | Modeling The Momentum Spillover Effect for Stock Prediction Via Attribute-Driven Graph Attention Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose an attribute-driven graph attention network (AD-GAT) to address both problems in modeling momentum spillovers. |
Rui Cheng; Qing Li; |
| 710 | Differentially Private Link Prediction with Protected Connections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Under a recently introduced latent node embedding model, we present a formal trade-off between privacy and LP utility. |
Abir De; Soumen Chakrabarti; |
| 711 | Graph Neural Network to Dilute Outliers for Refactoring Monolith Application Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel method to adapt the recent advancements in graph neural networks in the context of code to better understand the software and apply them in the clustering task. |
Utkarsh Desai; Sambaran Bandyopadhyay; Srikanth Tamilselvam; |
| 712 | KAN: Knowledge-aware Attention Network for Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, in this paper, we propose a novel Knowledge-aware Attention Network (KAN) that incorporates external knowledge from knowledge graph for fake news detection. |
Yaqian Dun; Kefei Tu; Chen Chen; Chunyan Hou; Xiaojie Yuan; |
| 713 | When Hashing Met Matching: Efficient Spatio-Temporal Search for Ridesharing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a principled approach to this combinatorial problem. |
Chinmoy Dutta; |
| 714 | Gene Regulatory Network Inference Using 3D Convolutional Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose 3D Co-Expression Matrix Analysis (3DCEMA), which predicts regulatory relationships by classifying 3D co-expression matrices of gene triples using a 3D convolutional neural network. |
Yue Fan; Xiuli Ma; |
| 715 | Universal Trading for Order Execution with Oracle Policy Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. |
Yuchen Fang; Kan Ren; Weiqing Liu; Dong Zhou; Weinan Zhang; Jiang Bian; Yong Yu; Tie-Yan Liu; |
| 716 | Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging has always been the subject of research. |
Chun-Mei Feng; Zhanyuan Yang; Geng Chen; Yong Xu; Ling Shao; |
| 717 | MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such challenges, we propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution. |
Tianfan Fu; Cao Xiao; Xinhao Li; Lucas M. Glass; Jimeng Sun; |
| 718 | ECG ODE-GAN: Learning Ordinary Differential Equations of ECG Dynamics Via Generative Adversarial Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By contrast, in this work we study how the dynamics can be learned by a generative adversarial network which combines both physical and data considerations. |
Tomer Golany; Daniel Freedman; Kira Radinsky; |
| 719 | Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel framework, towered actor critic (TAC), to handle multiple action types. |
Sai Krishna Gottipati; Yashaswi Pathak; Boris Sattarov; Sahir; Rohan Nuttall; Mohammad Amini; Matthew E. Taylor; Sarath Chandar; |
| 720 | Hierarchical Graph Convolution Network for Traffic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Hierarchical Graph Convolution Networks (HGCN) for traffic forecasting by operating on both the micro and macro traffic graphs. |
Kan Guo; Yongli Hu; Yanfeng Sun; Sean Qian; Junbin Gao; Baocai Yin; |
| 721 | Automated Lay Language Summarization of Biomedical Scientific Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. |
Yue Guo; Wei Qiu; Yizhong Wang; Trevor Cohen; |
| 722 | Sub-Seasonal Climate Forecasting Via Machine Learning: Challenges, Analysis, and Advances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we carefully investigate 10 Machine Learning (ML) approaches to sub-seasonal temperature forecasting over the contiguous U.S. on the SSF dataset we collect, including a variety of climate variables from the atmosphere, ocean, and land. |
Sijie He; Xinyan Li; Timothy DelSole; Pradeep Ravikumar; Arindam Banerjee; |
| 723 | Compound Word Transformer: Learning to Compose Full-Song Music Over Dynamic Directed Hypergraphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types. |
Wen-Yi Hsiao; Jen-Yu Liu; Yin-Cheng Yeh; Yi-Hsuan Yang; |
| 724 | Modeling The Compatibility of Stem Tracks to Generate Music Mashups Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we take advantage of separated stems not just for creating mashups, but for training a model that predicts the mutual compatibility of groups of excerpts, using self-supervised and semi-supervised methods. |
Jiawen Huang; Ju-Chiang Wang; Jordan B. L. Smith; Xuchen Song; Yuxuan Wang; |
| 725 | SDGNN: Learning Node Representation for Signed Directed Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Guided by related socio- logical theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks. |
Junjie Huang; Huawei Shen; Liang Hou; Xueqi Cheng; |
| 726 | The Causal Learning of Retail Delinquency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As such, we propose another approach to construct the estimators such that the error can be greatly reduced. |
Yiyan Huang; Cheuk Hang Leung; Xing Yan; Qi Wu; Nanbo Peng; Dongdong Wang; Zhixiang Huang; |
| 727 | Deep Portfolio Optimization Via Distributional Prediction of Residual Factors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. |
Kentaro Imajo; Kentaro Minami; Katsuya Ito; Kei Nakagawa; |
| 728 | Complex Coordinate-Based Meta-Analysis with Probabilistic Programming Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Coordinate-based meta-analysis (CBMA) databases are built by extracting both coordinates of reported peak activations and term associations using natural language processing techniques from neuroimaging studies. |
Valentin Iovene; Gaston E Zanitti; Demian Wassermann; |
| 729 | Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. |
Houye Ji; Junxiong Zhu; Xiao Wang; Chuan Shi; Bai Wang; Xiaoye Tan; Yanghua Li; Shaojian He; |
| 730 | Estimating Calibrated Individualized Survival Curves with Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we i) highlight the shortcomings of existing approaches in terms of calibration and ii) propose a new training scheme for optimizing deep survival analysis models that maximizes discriminative performance, subject to good calibration. |
Fahad Kamran; Jenna Wiens; |
| 731 | Deep Contextual Clinical Prediction with Reverse Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, motivated by the task of clinical prediction from insurance claims, we present a new technique called reverse distillation which pretrains deep models by using high-performing linear models for initialization. |
Rohan Kodialam; Rebecca Boiarsky; Justin Lim; Aditya Sai; Neil Dixit; David Sontag; |
| 732 | Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to achieve this goal by predicting the uncertainty of the current searching status and use the result to decide whether we should stop searching. |
Li-Cheng Lan; Ti-Rong Wu; I-Chen Wu; Cho-Jui Hsieh; |
| 733 | Predicting Livelihood Indicators from Community-Generated Street-Level Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. |
Jihyeon Lee; Dylan Grosz; Burak Uzkent; Sicheng Zeng; Marshall Burke; David Lobell; Stefano Ermon; |
| 734 | Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes an approach for latent-dynamics learning that exactly enforces physical conservation laws. |
Kookjin Lee; Kevin T. Carlberg; |
| 735 | Two-Stream Convolution Augmented Transformer for Human Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the drawbacks, we propose a novel Two-stream Convolution Augmented Human Activity Transformer (THAT) model. |
Bing Li; Wei Cui; Wei Wang; Le Zhang; Zhenghua Chen; Min Wu; |
| 736 | Traffic Flow Prediction with Vehicle Trajectories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle trajectories and incorporates that into road traffic prediction. |
Mingqian Li; Panrong Tong; Mo Li; Zhongming Jin; Jianqiang Huang; Xian-Sheng Hua; |
| 737 | RevMan: Revenue-aware Multi-task Online Insurance Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose RevMan, a Revenue-aware Multi-task Network for online insurance recommendation. |
Yu Li; Yi Zhang; Lu Gan; Gengwei Hong; Zimu Zhou; Qiang Li; |
| 738 | MeInGame: Create A Game Character Face from A Single Portrait Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an automatic character face creation method that predicts both facial shape and texture from a single portrait, and it can be integrated into most existing 3D games. |
Jiangke Lin; Yi Yuan; Zhengxia Zou; |
| 739 | Community-Aware Multi-Task Transportation Demand Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose the Multi-task Spatio-Temporal Network with Mutually-supervised Adaptive task grouping (Ada-MSTNet) for community-aware transportation demand prediction. |
Hao Liu; Qiyu Wu; Fuzhen Zhuang; Xinjiang Lu; Dejing Dou; Hui Xiong; |
| 740 | Asynchronous Stochastic Gradient Descent for Extreme-Scale Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the challenges posed by extreme-scale data, we propose: 1) Staleness normalization and data normalization to eliminate the turbulence of stale gradients when training asynchronously in hundreds and thousands of workers; 2) SWAP, a novel framework for adaptive optimizers to balance the new and historical gradients by taking sampling period into consideration. |
Lewis Liu; Kun Zhao; |
| 741 | In-game Residential Home Planning Via Visual Context-aware Global Relation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex. |
Lijuan Liu; Yin Yang; Yi Yuan; Tianjia Shao; He Wang; Kun Zhou; |
| 742 | Relational Classification of Biological Cells in Microscopy Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Relational Long Short-Term Memory (R-LSTM) algorithm, coupled with auto-encoders and convolutional neural networks, that can learn from both annotated and unlabeled microscopy images and that can utilize both the local and neighborhood information to perform an improved classification of biological cells. |
Ping Liu; Mustafa Bilgic; |
| 743 | Deep Style Transfer for Line Drawings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With this finding, we propose to formulate the style transfer problem as a centerline stylization problem and solve it via a novel style-guided image-to-image translation network. |
Xueting Liu; Wenliang Wu; Huisi Wu; Zhenkun Wen; |
| 744 | RNA Secondary Structure Representation Network for RNA-proteins Binding Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To effectively extract the structure features of RNA, we propose an RNA secondary structure representation network (RNASSR-Net) based on graph convolutional neural network (GCN) and convolution neural network (CNN) for RBP binding prediction. |
Ziyi Liu; Fulin Luo; Bo Du; |
| 745 | PANTHER: Pathway Augmented Nonnegative Tensor Factorization for HighER-order Feature Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to build more accurate and better interpretable machine learning models for genetic medicine, we introduce Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning (PANTHER). |
Yuan Luo; Chengsheng Mao; |
| 746 | Programmatic Strategies for Real-Time Strategy Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we introduce a system for synthesizing programmatic strategies for a real-time strategy (RTS) game. |
Julian R. H. Mariño; Rubens O. Moraes; Tassiana C. Oliveira; Claudio Toledo; Levi H. S. Lelis; |
| 747 | Capturing Uncertainty in Unsupervised GPS Trajectory Segmentation Using Bayesian Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To jointly address the above issues, we propose a Bayesian deep learning framework for unsupervised GPS trajectory segmentation. |
Christos Markos; James J. Q. Yu; Richard Yi Da Xu; |
| 748 | Low-Rank Registration Based Manifolds for Convection-Dominated PDEs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop an auto-encoder-type nonlinear dimensionality reduction algorithm to enable the construction of reduced order models of systems governed by convection-dominated nonlinear partial differential equations (PDEs), i.e. snapshots of solutions with large Kolmogorov n-width. |
Rambod Mojgani; Maciej Balajewicz; |
| 749 | Symbolic Music Generation with Transformer-GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We use a pre-trained Span-BERT model for the discriminator of the GAN, which in our experiments helped with training stability. |
Aashiq Muhamed; Liang Li; Xingjian Shi; Suri Yaddanapudi; Wayne Chi; Dylan Jackson; Rahul Suresh; Zachary C. Lipton; Alex J. Smola; |
| 750 | Bringing UMAP Closer to The Speed of Light with GPU Acceleration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show a number of techniques that can be used to make a faster and more faithful GPU version of UMAP, and obtain speedups of up to 100x in practice. |
Corey J. Nolet; Victor Lafargue; Edward Raff; Thejaswi Nanditale; Tim Oates; John Zedlewski; Joshua Patterson; |
| 751 | Deep Just-In-Time Inconsistency Detection Between Comments and Source Code Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code, in order to catch potential inconsistencies just-in-time, i.e., before they are committed to a code base. |
Sheena Panthaplackel; Junyi Jessy Li; Milos Gligoric; Raymond J. Mooney; |
| 752 | XraySyn: Realistic View Synthesis From A Single Radiograph Through CT Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Synthesizing novel radiographic views in a small range can assist physicians in interpreting anatomy more reliably; however, radiograph view synthesis is heavily ill-posed, lacking in paired data, and lacking in differentiable operations to leverage learning-based approaches. |
Cheng Peng; Haofu Liao; Gina Wong; Jiebo Luo; S. Kevin Zhou; Rama Chellappa; |
| 753 | Pragmatic Code Autocomplete Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to make programming languages more concise by allowing programmers to utilize a controlled level of ambiguity. |
Gabriel Poesia; Noah Goodman; |
| 754 | RareBERT: Transformer Architecture for Rare Disease Patient Identification Using Administrative Claims Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show our experiments on diagnosing X-Linked Hypophosphatemia (XLH), a genetic rare disease. |
PKS Prakash; Srinivas Chilukuri; Nikhil Ranade; Shankar Viswanathan; |
| 755 | Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a reinforcement learning-based (RL-based) service-rate controller that provides probabilistic upper-bounds on the end-to-end delay of the system, while preventing the overuse of service resources. |
Majid Raeis; Ali Tizghadam; Alberto Leon-Garcia; |
| 756 | Research Reproducibility As A Survival Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. |
Edward Raff; |
| 757 | DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper formulates CRA as a cause-specific regression problem and proposes DeepPseudo models, which use simple and effective feed-forward deep neural networks, to predict the cumulative incidence function (CIF) using Aalen-Johansen estimator-based pseudo values. |
Md Mahmudur Rahman; Koji Matsuo; Shinya Matsuzaki; Sanjay Purushotham; |
| 758 | CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to tackle this problem, we propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output. |
Pritam Sarkar; Ali Etemad; |
| 759 | Stock Selection Via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We validate our design choices through ablative and exploratory analyses over STHAN-SR’s spatial and temporal components and demonstrate its practical applicability. |
Ramit Sawhney; Shivam Agarwal; Arnav Wadhwa; Tyler Derr; Rajiv Ratn Shah; |
| 760 | Content Masked Loss: Human-Like Brush Stroke Planning in A Reinforcement Learning Painting Agent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to increase the human-like planning of the model without the use of expensive human data, we introduce a new loss function for use with the model’s reward function: Content Masked Loss. |
Peter Schaldenbrand; Jean Oh; |
| 761 | StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet. |
Eugene Seo; Rebecca A. Hutchinson; Xiao Fu; Chelsea Li; Tyler A. Hallman; John Kilbride; W. Douglas Robinson; |
| 762 | Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. |
Hoon Seo; Lodewijk Brand; Hua Wang; Feiping Nie; |
| 763 | GTA: Graph Truncated Attention for Retrosynthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Herein, we propose a novel template-free model, i.e., Graph Truncated Attention (GTA), which leverages both sequence and graph representations by inserting graphical information into a seq2seq model. |
Seung-Woo Seo; You Young Song; June Yong Yang; Seohui Bae; Hankook Lee; Jinwoo Shin; Sung Ju Hwang; Eunho Yang; |
| 764 | Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. |
Rongye Shi; Zhaobin Mo; Xuan Di; |
| 765 | The LOB Recreation Model: Predicting The Limit Order Book from TAQ History Using An Ordinary Differential Equation Recurrent Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the LOB recreation model, a first attempt from a deep learning perspective to recreate the top five price levels of the LOB for small-tick stocks using only TAQ data. |
Zijian Shi; Yu Chen; John Cartlidge; |
| 766 | Embracing Domain Differences in Fake News: Cross-domain Fake News Detection Using Multi-modal Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. |
Amila Silva; Ling Luo; Shanika Karunasekera; Christopher Leckie; |
| 767 | Oral-3D: Reconstructing The 3D Structure of Oral Cavity from Panoramic X-ray Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch. |
Weinan Song; Yuan Liang; Jiawei Yang; Kun Wang; Lei He; |
| 768 | Traffic Shaping in E-Commercial Search Engine: Multi-Objective Online Welfare Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a unified framework from the aspect of multi-objective welfare maximization where we regard all business requirements as objectives to optimize. |
Liucheng Sun; Chenwei Weng; Chengfu Huo; Weijun Ren; Guochuan Zhang; Xin Li; |
| 769 | Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. |
Xiangyun Tang; Dongliang Liao; Weijie Huang; Jin Xu; Liehuang Zhu; Meng Shen; |
| 770 | A Hierarchical Approach to Multi-Event Survival Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we introduce a novel approach for multi-event survival analysis that models the probability of event occurrence hierarchically at different time scales, using coarse predictions (e.g., monthly predictions) to iteratively guide predictions at finer and finer grained time scales (e.g., daily predictions). |
Donna Tjandra; Yifei He; Jenna Wiens; |
| 771 | DeepWriteSYN: On-Line Handwriting Synthesis Via Deep Short-Term Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. |
Ruben Tolosana; Paula Delgado-Santos; Andres Perez-Uribe; Ruben Vera-Rodriguez; Julian Fierrez; Aythami Morales; |
| 772 | Sketch Generation with Drawing Process Guided By Vector Flow and Grayscale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel image-to-pencil translation method that could not only generate high-quality pencil sketches but also offer the drawing process. |
Zhengyan Tong; Xuanhong Chen; Bingbing Ni; Xiaohang Wang; |
| 773 | PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM By Knowledge Distillation with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel PSSM-Distil framework for PSSP on low-quality PSSM, which not only enhances the PSSM feature at a lower level but also aligns the feature distribution at a higher level. |
Qin Wang; Boyuan Wang; Zhenlei Xu; Jiaxiang Wu; Peilin Zhao; Zhen Li; Sheng Wang; Junzhou Huang; Shuguang Cui; |
| 774 | Commission Fee Is Not Enough: A Hierarchical Reinforced Framework for Portfolio Management Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a hierarchical reinforced stock trading system for portfolio management (HRPM). |
Rundong Wang; Hongxin Wei; Bo An; Zhouyan Feng; Jun Yao; |
| 775 | Alternative Baselines for Low-Shot 3D Medical Image Segmentation—An Atlas Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose two alternative baselines, i.e., the Siamese-Baseline and Individual-Difference-Aware Baseline, where the former is targeted at anatomically stable structures (such as brain tissues), and the latter possesses a strong generalization ability to organs suffering large morphological variations (such as abdominal organs). |
Shuxin Wang; Shilei Cao; Dong Wei; Cong Xie; Kai Ma; Liansheng Wang; Deyu Meng; Yefeng Zheng; |
| 776 | DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DeepTrader, a deep RL method to optimize the investment policy. |
Zhicheng Wang; Biwei Huang; Shikui Tu; Kun Zhang; Lei Xu; |
| 777 | Dynamic Gaussian Mixture Based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. |
Yinjun Wu; Jingchao Ni; Wei Cheng; Bo Zong; Dongjin Song; Zhengzhang Chen; Yanchi Liu; Xuchao Zhang; Haifeng Chen; Susan B Davidson; |
| 778 | Automated Symbolic Law Discovery: A Computer Vision Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the incredible success of deep learning in computer vision, we tackle this problem by adapting various successful network architectures into the symbolic law discovery pipeline. |
Hengrui Xing; Ansaf Salleb-Aouissi; Nakul Verma; |
| 779 | Hierarchically and Cooperatively Learning Traffic Signal Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a hierarchical and cooperative reinforcement learning method-HiLight. |
Bingyu Xu; Yaowei Wang; Zhaozhi Wang; Huizhu Jia; Zongqing Lu; |
| 780 | Deep Partial Rank Aggregation for Personalized Attributes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of how to aggregate pairwise personalized attributes (PA) annotations (e.g., Shoes A is more comfortable than B) from different annotators on the crowdsourcing platforms, which is an emerging topic gaining increasing attention in recent years. |
Qianqian Xu; Zhiyong Yang; Zuyao Chen; Yangbangyan Jiang; Xiaochun Cao; Yuan Yao; Qingming Huang; |
| 781 | Towards Efficient Selection of Activity Trajectories Based on Diversity and Coverage Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the two challenges, we propose a novel solution by: (1) exploiting a deep metric learning method to speedup the similarity computation; and (2) proving that DaATS is an NP-hard problem, and developing an efficient approximation algorithm with performance guarantees. |
Chengcheng Yang; Lisi Chen; Hao Wang; Shuo Shang; |
| 782 | Minimizing Labeling Cost for Nuclei Instance Segmentation and Classification with Cross-domain Images and Weak Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a unified framework that is applicable to different level annotations: no annotations, image-level, and point-level annotations. |
Siqi Yang; Jun Zhang; Junzhou Huang; Brian C. Lovell; Xiao Han; |
| 783 | Bigram and Unigram Based Text Attack Via Adaptive Monotonic Heuristic Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Bigram and Unigram based Monotonic Heuristic Search (BU-MHS) method to examine the vulnerability of deep models. |
Xinghao Yang; Weifeng Liu; James Bailey; Dacheng Tao; Wei Liu; |
| 784 | GRASP: Generic Framework for Health Status Representation Learning Based on Incorporating Knowledge from Similar Patients Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Aiming at the above problem, we propose GRASP, a generic framework for healthcare models. |
Chaohe Zhang; Xin Gao; Liantao Ma; Yasha Wang; Jiangtao Wang; Wen Tang; |
| 785 | Window Loss for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings with no clear object-level spatial extents or boundaries. |
Xinyu Zhang; Yirui Wang; Chi-Tung Cheng; Le Lu; Adam P. Harrison; Jing Xiao; Chien-Hung Liao; Shun Miao; |
| 786 | A Spatial Regulated Patch-Wise Approach for Cervical Dysplasia Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A novel iterative algorithm is designed to refine the regulator, which is able to capture the variations in cervix center locations and shapes. |
Ying Zhang; Yifang Yin; Zhenguang Liu; Roger Zimmermann; |
| 787 | Online 3D Bin Packing with Constrained Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problem, we propose an effective and easy-to-implement constrained deep reinforcement learning (DRL) method under the actor-critic framework. |
Hang Zhao; Qijin She; Chenyang Zhu; Yin Yang; Kai Xu; |
| 788 | DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper, we propose a novel advertising strategy for the rec/ads trade-off. |
Xiangyu Zhao; Changsheng Gu; Haoshenglun Zhang; Xiwang Yang; Xiaobing Liu; Jiliang Tang; Hui Liu; |
| 789 | Towards Balanced Defect Prediction with Better Information Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DPCAG, a novel model to address the above three issues. |
Xianda Zheng; Yuan-Fang Li; Huan Gao; Yuncheng Hua; Guilin Qi; |
| 790 | Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods from two perspectives to improve a single model’s disease identification performance, rather than focusing on an ensemble of models. |
Yi Zhou; Lei Huang; Tianfei Zhou; Ling Shao; |
| 791 | Probabilistic Programming Bots in Intuitive Physics Game Play Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a framework for bots to deploy probabilistic programming tools for interacting with intuitive physics environments. |
Fahad Alhasoun; Sarah Alneghiemish; |
| 792 | Model-Agnostic Fits for Understanding Information Seeking Patterns in Humans Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We design deep learning models that replicate these biases in aggregate, while also capturing individual variation in behavior. |
Soumya Chatterjee; Pradeep Shenoy; |
| 793 | Apparently Irrational Choice As Optimal Sequential Decision Making Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a normative approach to modeling apparently human irrational decision making (cognitive biases) that makes use of inherently rational computational mechanisms. |
Haiyang Chen; Hyung Jin Chang; Andrew Howes; |
| 794 | Visual Relation Detection Using Hybrid Analogical Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a new hybrid system for visual relation detection combining deep-learning models and analogical generalization. |
Kezhen Chen; Ken Forbus; |
| 795 | Neural Analogical Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As part of a growing body of research on such an integration, we introduce the Analogical Matching Network: a neural architecture that learns to produce analogies between structured, symbolic representations that are largely consistent with the principles of Structure-Mapping Theory. |
Maxwell Crouse; Constantine Nakos; Ibrahim Abdelaziz; Ken Forbus; |
| 796 | Interpretable Self-Supervised Facial Micro-Expression Learning to Predict Cognitive State and Neurological Disorders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the success of using facial muscle movements to classify brain states and the importance of learning from small amounts of data, we propose an explainable self-supervised representation-learning paradigm that learns meaningful temporal facial muscle movement patterns from limited samples. |
Arun Das; Jeffrey Mock; Yufei Huang; Edward Golob; Peyman Najafirad; |
| 797 | Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, we propose a fundamentally new, quantum cognitively motivated fusion strategy for predicting sentiment judgments. |
Dimitris Gkoumas; Qiuchi Li; Shahram Dehdashti; Massimo Melucci; Yijun Yu; Dawei Song; |
| 798 | Towards A Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we predict the degrees of main physical symptoms affecting the overall degree of VR sickness, which are disorientation, nausea, and oculomotor. |
Hak Gu Kim; Sangmin Lee; Seongyeop Kim; Heoun-taek Lim; Yong Man Ro; |
| 799 | PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions by including social concepts such as helping another agent. |
Aviv Netanyahu; Tianmin Shu; Boris Katz; Andrei Barbu; Joshua B. Tenenbaum; |
| 800 | Riemannian Embedding Banks for Common Spatial Patterns with EEG-based SPD Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above limitations, we propose a Riemannian Embedding Banks method, which divides the problem of common spatial patterns learning in an entire embedding space into K-subproblems and builds one model for each subproblem, to be combined with SPD neural networks. |
Yoon-Je Suh; Byung Hyung Kim; |
| 801 | Plug-and-Play Domain Adaptation for Cross-Subject EEG-based Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: According to this representation partition, we propose a plug-and-play domain adaptation method for dealing with the inter-subject variability. |
Li-Ming Zhao; Xu Yan; Bao-Liang Lu; |
| 802 | Localization in The Crowd with Topological Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a topological approach targeting these semantic errors. |
Shahira Abousamra; Minh Hoai; Dimitris Samaras; Chao Chen; |
| 803 | Deep Event Stereo Leveraged By Event-to-Image Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we develop a novel deep event stereo network that reconstructs spatial intensity image features from embedded event streams and leverages the event features using the reconstructed image features to compute dense disparity maps. |
Soikat Hasan Ahmed; Hae Woong Jang; S M Nadim Uddin; Yong Ju Jung; |
| 804 | Optical Flow Estimation from A Single Motion-blurred Image Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner. |
Dawit Mureja Argaw; Junsik Kim; Francois Rameau; Jae Won Cho; In So Kweon; |
| 805 | Motion-blurred Video Interpolation and Extrapolation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a novel framework for deblurring, interpolating and extrapolating sharp frames from a motion-blurred video in an end-to-end manner. |
Dawit Mureja Argaw; Junsik Kim; Francois Rameau; In So Kweon; |
| 806 | Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we present a Disentangled Multi-Relational Graph Convolutional Network (DMRGCN) for socially entangled pedestrian trajectory prediction. |
Inhwan Bae; Hae-Gon Jeon; |
| 807 | Dense Events Grounding in Video Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Dense Events Propagation Network (DepNet) for this novel task. |
Peijun Bao; Qian Zheng; Yadong Mu; |
| 808 | Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a novel context-aware attentional pooling (CAP) that effectively captures subtle changes via sub-pixel gradients, and learns to attend informative integral regions and their importance in discriminating different subcategories without requiring the bounding-box and/or distinguishable part annotations. |
Ardhendu Behera; Zachary Wharton; Pradeep R P G Hewage; Asish Bera; |
| 809 | Appearance-Motion Memory Consistency Network for Video Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Abnormal event detection in the surveillance video is an essential but challenging task, and many methods have been proposed to deal with this problem. |
Ruichu Cai; Hao Zhang; Wen Liu; Shenghua Gao; Zhifeng Hao; |
| 810 | Rethinking Object Detection in Retail Stores Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new task, i.e., simultaneously object localization and counting, abbreviated as Locount, which requires algorithms to localize groups of objects of interest with the number of instances. |
Yuanqiang Cai; Longyin Wen; Libo Zhang; Dawei Du; Weiqiang Wang; |
| 811 | YOLObile: Real-Time Object Detection on Mobile Devices Via Compression-Compilation Co-Design Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. |
Yuxuan Cai; Hongjia Li; Geng Yuan; Wei Niu; Yanyu Li; Xulong Tang; Bin Ren; Yanzhi Wang; |
| 812 | Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Views Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Importantly, our goal is to build neural episodic memories and spatio-semantic representations of 3D spaces that enable the agent to easily learn subsequent tasks in the same space – navigating to objects seen during the tour (‘Find chair’) or answering questions about the space (‘How many chairs did you see in the house?’) |
Vincent Cartillier; Zhile Ren; Neha Jain; Stefan Lee; Irfan Essa; Dhruv Batra; |
| 813 | Understanding Deformable Alignment in Video Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we carefully investigate the relation between deformable alignment and the classic flow-based alignment. |
Kelvin C.K. Chan; Xintao Wang; Ke Yu; Chao Dong; Chen Change Loy; |
| 814 | Deep Metric Learning with Graph Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper empirically and experimentally demonstrates the effectiveness of our graph regularization idea, achieving competitive results on the popular CUB, CARS, Stanford Online Products and In-Shop datasets. |
Binghui Chen; Pengyu Li; Zhaoyi Yan; Biao Wang; Lei Zhang; |
| 815 | CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study applies CNN on satellite images to create the entire TC structure profiles, covering all the structural parameters. |
Boyo Chen; Buo-Fu Chen; Chun Min Hsiao; |
| 816 | Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we aim at increasing the diversity of the generated stories while preserving the informative content from the images. |
Hong Chen; Yifei Huang; Hiroya Takamura; Hideki Nakayama; |
| 817 | Attention-based Multi-Level Fusion Network for Light Field Depth Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel attention-based multi-level fusion network. |
Jiaxin Chen; Shuo Zhang; Youfang Lin; |
| 818 | Joint Demosaicking and Denoising in The Wild: The Case of Training Under Ground Truth Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose and study Wild-JDD, a novel learning framework for joint demosaicking and denoising in the wild. |
Jierun Chen; Song Wen; S.-H. Gary Chan; |
| 819 | Spatial-temporal Causal Inference for Partial Image-to-video Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a spatial-temporal causal inference framework for image-to-video adaptation. |
Jin Chen; Xinxiao Wu; Yao Hu; Jiebo Luo; |
| 820 | Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., expression-agnostic), hoping that the proposals contain all right instances in the expression (i.e., expression-aware). |
Long Chen; Wenbo Ma; Jun Xiao; Hanwang Zhang; Shih-Fu Chang; |
| 821 | RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we observe that the relative playback speed is more consistent with motion patterns and thus provides more effective and stable supervision for representation learning. |
Peihao Chen; Deng Huang; Dongliang He; Xiang Long; Runhao Zeng; Shilei Wen; Mingkui Tan; Chuang Gan; |
| 822 | Dual Distribution Alignment Network for Generalizable Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we identify two fundamental challenges in DG for Person Re-ID: domain-wise variations and identity-wise similarities. |
Peixian Chen; Pingyang Dai; Jianzhuang Liu; Feng Zheng; Mingliang Xu; Qi Tian; Rongrong Ji; |
| 823 | RGB-D Salient Object Detection Via 3D Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make the first attempt in addressing RGB-D SOD through 3D convolutional neural networks. |
Qian Chen; Ze Liu; Yi Zhang; Keren Fu; Qijun Zhao; Hongwei Du; |
| 824 | Mind-the-Gap! Unsupervised Domain Adaptation for Text-Video Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate this question through the lens of unsupervised domain adaptation in which the objective is to match natural language queries and video content in the presence of domain shift at query-time. |
Qingchao Chen; Yang Liu; Samuel Albanie; |
| 825 | Local Relation Learning for Face Forgery Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel perspective of face forgery detection via local relation learning. |
Shen Chen; Taiping Yao; Yang Chen; Shouhong Ding; Jilin Li; Rongrong Ji; |
| 826 | Deductive Learning for Weakly-Supervised 3D Human Pose Estimation Via Uncalibrated Cameras Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this issue, in this paper, we propose a Deductive Weakly-Supervised Learning (DWSL) for 3D human pose machine. |
Xipeng Chen; Pengxu Wei; Liang Lin; |
| 827 | A Unified Multi-Scenario Attacking Network for Visual Object Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Unified and Effective Network, named UEN, to attack visual object tracking models. |
Xuesong Chen; Canmiao Fu; Feng Zheng; Yong Zhao; Hongsheng Li; Ping Luo; Guo-Jun Qi; |
| 828 | SSD-GAN: Measuring The Realness in The Spatial and Spectral Domains Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce SSD-GAN, an enhancement of GANs to alleviate the spectral information loss in the discriminator. |
Yuanqi Chen; Ge Li; Cece Jin; Shan Liu; Thomas Li; |
| 829 | Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions. |
Zhan Chen; Sicheng Li; Bing Yang; Qinghan Li; Hong Liu; |
| 830 | Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier and estimates relative pose through classified correspondences. |
Zhi Chen; Fan Yang; Wenbing Tao; |
| 831 | Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the limitation, we propose domain dynamic adjustment meta-learning (D$^2$AM) without using domain labels, which iteratively divides mixture domains via discriminative domain representation and trains a generalizable face anti-spoofing with meta-learning. |
Zhihong Chen; Taiping Yao; Kekai Sheng; Shouhong Ding; Ying Tai; Jilin Li; Feiyue Huang; Xinyu Jin; |
| 832 | SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points. |
Mingmei Cheng; Le Hui; Jin Xie; Jian Yang; |
| 833 | Deep Feature Space Trojan Attack of Neural Networks By Controlled Detoxification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel deep feature space trojan attack with five characteristics: effectiveness, stealthiness, controllability, robustness and reliance on deep features. |
Siyuan Cheng; Yingqi Liu; Shiqing Ma; Xiangyu Zhang; |
| 834 | Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that does not require camera parameters. |
Yu Cheng; Bo Wang; Bo Yang; Robby T. Tan; |
| 835 | DramaQA: Character-Centered Video Story Understanding with Hierarchical QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. |
Seongho Choi; Kyoung-Woon On; Yu-Jung Heo; Ahjeong Seo; Youwon Jang; Minsu Lee; Byoung-Tak Zhang; |
| 836 | DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. |
Bo Cui; Guyue Hu; Shan Yu; |
| 837 | Split Then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this observation and to erase the visible watermarks blindly, we propose a novel two-stage framework with a stacked attention-guided ResUNets to simulate the process of detection, removal and refinement. |
Xiaodong Cun; Chi-Man Pun; |
| 838 | RSGNet: Relation Based Skeleton Graph Network for Crowded Scenes Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on two main problems: 1) how to design an effective pipeline for crowded scenes pose estimation; and 2) how to equip this pipeline with the ability of relation modeling for interference resolving. |
Yan Dai; Xuanhan Wang; Lianli Gao; Jingkuan Song; Heng Tao Shen; |
| 839 | Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a slightly different viewpoint — we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy. |
Jiajun Deng; Shaoshuai Shi; Peiwei Li; Wengang Zhou; Yanyong Zhang; Houqiang Li; |
| 840 | Arbitrary Video Style Transfer Via Multi-Channel Correlation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards this end, we propose a Multi-Channel Correlation network (MCCNet), which can be trained to fuse exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos to output videos. |
Yingying Deng; Fan Tang; Weiming Dong; Haibin Huang; Chongyang Ma; Changsheng Xu; |
| 841 | Similarity Reasoning and Filtration for Image-Text Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Similarity Graph Reasoning and Attention Filtration (SGRAF) network for image-text matching. |
Haiwen Diao; Ying Zhang; Lin Ma; Huchuan Lu; |
| 842 | Spatio-Temporal Difference Descriptor for Skeleton-Based Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a spatio-temporal difference descriptor based on a directional convolution architecture that enables us to learn the spatio-temporal differences and contextual dependencies between different body joints simultaneously. |
Chongyang Ding; Kai Liu; Jari Korhonen; Evgeny Belyaev; |
| 843 | Towards Universal Physical Attacks on Single Object Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent studies show that small perturbations in video frames could misguide single object trackers. |
Li Ding; Yongwei Wang; Kaiwen Yuan; Minyang Jiang; Ping Wang; Hua Huang; Z. Jane Wang; |
| 844 | Modeling The Probabilistic Distribution of Unlabeled Data for One-shot Medical Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. |
Yuhang Ding; Xin Yu; Yi Yang; |
| 845 | Few-Shot Class-Incremental Learning Via Relation Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the challenging few-shot class incremental learning (FSCIL) problem, which requires to transfer knowledge from old tasks to new ones and solves catastrophic forgetting. |
Songlin Dong; Xiaopeng Hong; Xiaoyu Tao; Xinyuan Chang; Xing Wei; Yihong Gong; |
| 846 | MIEHDR CNN: Main Image Enhancement Based Ghost-Free High Dynamic Range Imaging Using Dual-Lens Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In detail, we propose a new model, named MIEHDR CNN model, which consists of three subnets, i.e. Soft Warp CNN, 3D Guided Denoising CNN and Fusion CNN. |
Xuan Dong; Xiaoyan Hu; Weixin Li; Xiaojie Wang; Yunhong Wang; |
| 847 | Boosting Image-based Mutual Gaze Detection Using Pseudo 3D Gaze Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by using an auxiliary 3D gaze estimation task during the training phase. |
Bardia Doosti; Ching-Hui Chen; Raviteja Vemulapalli; Xuhui Jia; Yukun Zhu; Bradley Green; |
| 848 | How to Save Your Annotation Cost for Panoptic Segmentation? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By closely examining different kinds of cheaper labels, we introduce a novel multi-objective framework to automatically determine the allocation of different annotations, so as to reach a better segmentation quality with a lower annotation cost. |
Xuefeng Du; ChenHan Jiang; Hang Xu; Gengwei Zhang; Zhenguo Li; |
| 849 | DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the problems above, we propose a novel one-stage HOI detection approach DIRV in this paper, based on a new concept called interaction region for the HOI problem. |
Hao-Shu Fang; Yichen Xie; Dian Shao; Cewu Lu; |
| 850 | DecAug: Augmenting HOI Detection Via Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To increase data efficiency, in this paper, we propose an efficient and effective data augmentation method called DecAug for HOI detection. |
Hao-Shu Fang; Yichen Xie; Dian Shao; Yong-Lu Li; Cewu Lu; |
| 851 | Partially Non-Autoregressive Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To make a better trade-off between speed and quality, we introduce a partially non-autoregressive model, named PNAIC, which considers a caption as a series of concatenated word groups. |
Zhengcong Fei; |
| 852 | Memory-Augmented Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards this goal, we introduce a memory-augmented method, which extends an existing image caption model by incorporating extra explicit knowledge from a memory bank. |
Zhengcong Fei; |
| 853 | Edge-competing Pathological Liver Vessel Segmentation with Limited Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the collected dataset, we propose an Edge-competing Vessel Segmentation Network (EVS-Net), which contains a segmentation network and two edge segmentation discriminators. |
Zunlei Feng; Zhonghua Wang; Xinchao Wang; Xiuming Zhang; Lechao Cheng; Jie Lei; Yuexuan Wang; Mingli Song; |
| 854 | Visual Boundary Knowledge Translation for Foreground Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make an attempt towards building models that explicitly account for visual boundary knowledge, in hope to reduce the training effort on segmenting unseen categories. |
Zunlei Feng; Lechao Cheng; Xinchao Wang; Xiang Wang; Ya Jie Liu; Xiangtong Du; Mingli Song; |
| 855 | Learning Complex 3D Human Self-Contact Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we develop a model for Self-Contact Prediction (SCP), that estimates the body surface signature of self-contact, leveraging the localization of self-contact in the image, during both training and inference. |
Mihai Fieraru; Mihai Zanfir; Elisabeta Oneata; Alin-Ionut Popa; Vlad Olaru; Cristian Sminchisescu; |
| 856 | Rain Streak Removal Via Dual Graph Convolutional Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above problem, we propose a simple yet effective dual graph convolutional network (GCN) for single image rain removal. |
Xueyang Fu; Qi Qi; Zheng-Jun Zha; Yurui Zhu; Xinghao Ding; |
| 857 | CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To eliminate ambiguities introduced by only using single-frame features, we propose a novel comprehensive feature aggregation approach (CompFeat) to refine features atboth frame-level and object-level with temporal and spatial context information. |
Yang Fu; Linjie Yang; Ding Liu; Thomas S. Huang; Humphrey Shi; |
| 858 | Deep Metric Learning with Self-Supervised Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel self-supervised ranking auxiliary framework, which captures intra-class characteristics as well as inter-class characteristics for better metric learning. |
Zheren Fu; Yan Li; Zhendong Mao; Quan Wang; Yongdong Zhang; |
| 859 | A Systematic Evaluation of Object Detection Networks for Scientific Plots Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this open problem, we make a series of contributions: (a) an efficient region proposal method based on Laplacian edge detectors, (b) a feature representation of region proposals that includes neighbouring information, (c) a linking component to join multiple region proposals for detecting longer textual objects, and (d) a custom loss function that combines a smooth L1-loss with an IOU-based loss. |
Pritha Ganguly; Nitesh S Methani; Mitesh M. Khapra; Pratyush Kumar; |
| 860 | The Complexity of Object Association in Multiple Object Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Two prominent models capturing this task have been introduced in the literature: the Lifted Multicut model and the more recent Lifted Paths model. |
Robert Ganian; Thekla Hamm; Sebastian Ordyniak; |
| 861 | Learning Local Neighboring Structure for Robust 3D Shape Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each node according to the local neighboring structure and performs shared anisotropic filters. |
Zhongpai Gao; Junchi Yan; Guangtao Zhai; Juyong Zhang; Yiyan Yang; Xiaokang Yang; |
| 862 | Semantic-guided Reinforced Region Embedding for Generalized Zero-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Semantic-guided Reinforced Region Embedding (SR2E) network that can localize important objects in the long-term interests to construct semantic-visual embedding space. |
Jiannan Ge; Hongtao Xie; Shaobo Min; Yongdong Zhang; |
| 863 | Dynamic Graph Representation Learning for Video Dialog Via Multi-Modal Shuffled Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing representations conditioned on the input question. |
Shijie Geng; Peng Gao; Moitreya Chatterjee; Chiori Hori; Jonathan Le Roux; Yongfeng Zhang; Hongsheng Li; Anoop Cherian; |
| 864 | Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Built upon the boundary-aware GEM, we build our network and test it on benchmarks like ScanNet v2, S3DIS. |
Jingyu Gong; Jiachen Xu; Xin Tan; Jie Zhou; Yanyun Qu; Yuan Xie; Lizhuang Ma; |
| 865 | Analogical Image Translation for Fog Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim at adding adverse weather effects, more specifically fog, to images taken in clear weather. |
Rui Gong; Dengxin Dai; Yuhua Chen; Wen Li; Danda Pani Paudel; Luc Van Gool; |
| 866 | Temporal ROI Align for Video Object Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, considering the features of the same object instance are highly similar among frames in a video, a novel Temporal ROI Align operator is proposed to extract features from other frames feature maps for current frame proposals by utilizing feature similarity. |
Tao Gong; Kai Chen; Xinjiang Wang; Qi Chu; Feng Zhu; Dahua Lin; Nenghai Yu; Huamin Feng; |
| 867 | SMART Frame Selection for Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address theproblem of frame selection to reduce the computational cost of video classification.Recent work has successfully leveraged frame selection for long, untrimmed videos,where much of the content is not relevant, and easy to discard. |
Shreyank N Gowda; Marcus Rohrbach; Laura Sevilla-Lara; |
| 868 | Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a simple regularizer called Proxy Synthesis that exploits synthetic classes for stronger generalization in deep metric learning. |
Geonmo Gu; Byungsoo Ko; Han-Gyu Kim; |
| 869 | Interpretable Graph Capsule Networks for Object Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the lack of interpretability, we can either propose new post-hoc interpretation methods for CapsNets or modifying the model to have build-in explanations. |
Jindong Gu; |
| 870 | Class-Incremental Instance Segmentation Via Multi-Teacher Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose an incremental instance segmentation method that consists of three networks: Former Teacher Network (FTN), Current Student Network (CSN) and Current Teacher Network (CTN). |
Yanan Gu; Cheng Deng; Kun Wei; |
| 871 | EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i.e., EfficientDeRain, which is able to process a rainy image within 10 ms (i.e., around 6 ms on average), over 80 times faster than the state-of-the-art method (i.e., RCDNet), while achieving similar de-rain effects. |
Qing Guo; Jingyang Sun; Felix Juefei-Xu; Lei Ma; Xiaofei Xie; Wei Feng; Yang Liu; Jianjun Zhao; |
| 872 | Order Regularization on Ordinal Loss for Head Pose, Age and Gaze Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose order regularization on ordinal loss, which makes the outputs in order by explicitly constraining the ordinal classifiers in order. |
Tianchu Guo; Hui Zhang; ByungIn Yoo; Yongchao Liu; Youngjun Kwak; Jae-Joon Han; |
| 873 | Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these problems, we propose a decoupled and memory-reinforced network (DMRNet). |
Chuchu Han; Zhedong Zheng; Changxin Gao; Nong Sang; Yi Yang; |
| 874 | Spherical Image Generation from A Single Image By Considering Scene Symmetry Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method for generating a spherical image from a single NFOV image and controlling the degree of freedom of the generated regions using the scene symmetry. |
Takayuki Hara; Yusuke Mukuta; Tatsuya Harada; |
| 875 | Progressive One-shot Human Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new problem named one-shot human parsing (OSHP) that requires to parse human into an open set of reference classes defined by any single reference example. |
Haoyu He; Jing Zhang; Bhavani Thuraisingham; Dacheng Tao; |
| 876 | Consistent-Separable Feature Representation for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Consistent-Separable Feature Representation Network to model the Consistent-Separable (C-S) features, which are intra-class consistent and inter-class separable, improving the discriminative power of the deep features. |
Xingjian He; Jing Liu; Jun Fu; Xinxin Zhu; Jinqiao Wang; Hanqing Lu; |
| 877 | Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a novel Error-aware Density Isomorphism REConstruction Network (EDIREC-Net) for cross-domain crowd counting. |
Yuhang He; Zhiheng Ma; Xing Wei; Xiaopeng Hong; Wei Ke; Yihong Gong; |
| 878 | DropLoss for Long-Tail Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate that the rare categories are heavily suppressed by correct background predictions, which reduces the probability for all foreground categories with equal weight. |
Ting-I Hsieh; Esther Robb; Hwann-Tzong Chen; Jia-Bin Huang; |
| 879 | Hand-Model-Aware Sign Language Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the hand prior and propose a new hand-model-aware framework for isolated SLR with the modeling hand as the intermediate representation. |
Hezhen Hu; Wengang Zhou; Houqiang Li; |
| 880 | Stratified Rule-Aware Network for Abstract Visual Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fix the defects, we propose an answer set generation algorithm called Attribute Bisection Tree (ABT), forming an improved dataset named Impartial-RAVEN (I-RAVEN for short). |
Sheng Hu; Yuqing Ma; Xianglong Liu; Yanlu Wei; Shihao Bai; |
| 881 | VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents VIsual VOcabulary pre-training (VIVO) that performs pre-training in the absence of caption annotations. |
Xiaowei Hu; Xi Yin; Kevin Lin; Lei Zhang; Jianfeng Gao; Lijuan Wang; Zicheng Liu; |
| 882 | Exploiting Relationship for Complex-scene Image Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the help of relationships, we propose three major updates in the generation framework. |
Tianyu Hua; Hongdong Zheng; Yalong Bai; Wei Zhang; Xiao-Ping Zhang; Tao Mei; |
| 883 | Modeling Deep Learning Based Privacy Attacks on Physical Mail Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, for the first time, we show that with a well designed deep learning model, the hidden content may be largely recovered without opening the envelope. |
Bingyao Huang; Ruyi Lian; Dimitris Samaras; Haibin Ling; |
| 884 | PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Poisson Transfer Network (PTN) to mine the unlabeled information for SSFSL from two aspects. |
Huaxi Huang; Junjie Zhang; Jian Zhang; Qiang Wu; Chang Xu; |
| 885 | Text-Guided Graph Neural Networks for Referring 3D Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Text-guided Graph Neural Network (TGNN) for referring 3D instance segmentation on point clouds. |
Pin-Hao Huang; Han-Hung Lee; Hwann-Tzong Chen; Tyng-Luh Liu; |
| 886 | Initiative Defense Against Facial Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the limitation, in this paper, we propose a novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users. |
Qidong Huang; Jie Zhang; Wenbo Zhou; Weiming Zhang; Nenghai Yu; |
| 887 | SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Semantically Proportional Mixing (SnapMix) that exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. |
Shaoli Huang; Xinchao Wang; Dacheng Tao; |
| 888 | A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel framework named HAM-Net with a hybrid attention mechanism which includes temporal soft, semi-soft and hard attentions to address these issues. |
Ashraful Islam; Chengjiang Long; Richard Radke; |
| 889 | Context-Aware Graph Convolution Network for Target Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel Context-Aware Graph Convolution Network (CAGCN), where the probe-gallery relations are encoded into the graph nodes and the graph edge connections are well controlled by the gallery-gallery relations. |
Deyi Ji; Haoran Wang; Hanzhe Hu; Weihao Gan; Wei Wu; Junjie Yan; |
| 890 | Improving Image Captioning By Leveraging Intra- and Inter-layer Global Representation in Transformer Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions. |
Jiayi Ji; Yunpeng Luo; Xiaoshuai Sun; Fuhai Chen; Gen Luo; Yongjian Wu; Yue Gao; Rongrong Ji; |
| 891 | Frequency Consistent Adaptation for Real World Super Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To provide useful gradient information for kernel estimation, we propose Frequency Density Comparator (FDC) by distinguishing the frequency density of images on different scales. |
Xiaozhong Ji; Guangpin Tao; Yun Cao; Ying Tai; Tong Lu; Chengjie Wang; Jilin Li; Feiyue Huang; |
| 892 | Matching on Sets: Conquer Occluded Person Re-identification Without Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents Matching on Sets (MoS), a novel method that positions occluded person re-ID as a set matching task without requiring spatial alignment. |
Mengxi Jia; Xinhua Cheng; Yunpeng Zhai; Shijian Lu; Siwei Ma; Yonghong Tian; Jian Zhang; |
| 893 | GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection By Grading The Box Candidates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a new two-stage framework for WSOD, named GradingNet, which can make good use of the generated candidate bounding boxes. |
Qifei Jia; Shikui Wei; Tao Ruan; Yufeng Zhao; Yao Zhao; |
| 894 | SSN3D: Self-Separated Network to Align Parts for 3D Convolution in Video Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose Self-Separated network (SSN) to seek out the same parts in different images. |
Xiaoke Jiang; Yu Qiao; Junjie Yan; Qichen Li; Wanrong Zheng; Dapeng Chen; |
| 895 | Training Binary Neural Network Without Batch Normalization for Image Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Extensive experiments demonstrate that the proposed method not only presents advantages of lower computation as compared to conventional floating-point networks but outperforms the state-of-the-art binary methods on the standard SR networks. |
Xinrui Jiang; Nannan Wang; Jingwei Xin; Keyu Li; Xi Yang; Xinbo Gao; |
| 896 | What to Select: Pursuing Consistent Motion Segmentation from Multiple Geometric Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel geometric-model-fusion framework for motion segmentation, which targets at constructing a consistent affinity matrix across all the geometric models. |
Yangbangyan Jiang; Qianqian Xu; Ke Ma; Zhiyong Yang; Xiaochun Cao; Qingming Huang; |
| 897 | Asynchronous Teacher Guided Bit-wise Hard Mining for Online Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel online hashing method to handle the above-mentioned issues jointly, termed Asynchronus Teacher-Guided Bit-wise Hard Mining for Online Hashing. |
Sheng Jin; Qin Zhou; Hongxun Yao; Yao Liu; Xian-Sheng Hua; |
| 898 | Deep Low-Contrast Image Enhancement Using Structure Tensor Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new deep learning framework for low-contrast image enhancement, which trains the network using the multi-exposure sequences rather than explicit ground-truth images. |
Hyungjoo Jung; Hyunsung Jang; Namkoo Ha; Kwanghoon Sohn; |
| 899 | Spectral Distribution Aware Image Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. |
Steffen Jung; Margret Keuper; |
| 900 | StarNet: Towards Weakly Supervised Few-Shot Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce StarNet – a few-shot model featuring an end-to-end differentiable non-parametric star-model detection and classification head. |
Leonid Karlinsky; Joseph Shtok; Amit Alfassy; Moshe Lichtenstein; Sivan Harary; Eli Schwartz; Sivan Doveh; Prasanna Sattigeri; Rogerio Feris; Alex Bronstein; Raja Giryes; |
| 901 | Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we introduce discriminative region suppression (DRS) module that is a simple yet effective method to expand object activation regions. |
Beomyoung Kim; Sangeun Han; Junmo Kim; |
| 902 | Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a novel deep reinforcement learning (DRL)-based approach for depth adjustment named VCA-RL (Visual Comfort Aware Reinforcement Learning) to explicitly model human sequential decision making in depth editing operations. |
Hak Gu Kim; Minho Park; Sangmin Lee; Seongyeop Kim; Yong Man Ro; |
| 903 | Dual Compositional Learning in Interactive Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an approach named Dual Composition Network (DCNet) for interactive image retrieval that searches for the best target image for a natural language query and a reference image. |
Jongseok Kim; Youngjae Yu; Hoeseong Kim; Gunhee Kim; |
| 904 | End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (HDRI) process. |
Junghee Kim; Siyeong Lee; Suk-Ju Kang; |
| 905 | Structured Co-reference Graph Attention for Video-grounded Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our empirical results show that SCGA outperforms other state-of-the-art dialogue systems on both benchmarks, while extensive ablation study and qualitative analysis reveal performance gain and improved interpretability. |
Junyeong Kim; Sunjae Yoon; Dahyun Kim; Chang D. Yoo; |
| 906 | Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation, we introduce a learnable clustering module, and a novel domain adaptation framework, called cross-domain grouping and alignment. |
Minsu Kim; Sunghun Joung; Seungryong Kim; JungIn Park; Ig-Jae Kim; Kwanghoon Sohn; |
| 907 | Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation. |
Soopil Kim; Sion An; Philip Chikontwe; Sang Hyun Park; |
| 908 | DASZL: Dynamic Action Signatures for Zero-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an approach to fine-grained recognition that models activities as compositions of dynamic action signatures. |
Tae Soo Kim; Jonathan Jones; Michael Peven; Zihao Xiao; Jin Bai; Yi Zhang; Weichao Qiu; Alan Yuille; Gregory D. Hager; |
| 909 | Multi-level Distance Regularization for Deep Metric Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). |
Yonghyun Kim; Wonpyo Park; |
| 910 | Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. |
Prashant Kumar; Sabyasachi Sahoo; Vanshil Shah; Vineetha Kondameedi; Abhinav Jain; Akshaj Verma; Chiranjib Bhattacharyya; Vinay Vishwanath; |
| 911 | Regularizing Attention Networks for Anomaly Detection in Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we evaluate the robustness of state-of-the-art VQA models to five different anomalies, including worst-case scenarios, the most frequent scenarios, and the current limitation of VQA models. |
Doyup Lee; Yeongjae Cheon; Wook-Shin Han; |
| 912 | Weakly-supervised Temporal Action Localization By Uncertainty Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new perspective on background frames where they are modeled as out-of-distribution samples regarding their inconsistency. |
Pilhyeon Lee; Jinglu Wang; Yan Lu; Hyeran Byun; |
| 913 | Learning Monocular Depth in Dynamic Scenes Via Instance-Aware Projection Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion, and depth in a monocular camera setup without supervision. |
Seokju Lee; Sunghoon Im; Stephen Lin; In So Kweon; |
| 914 | Patch-Wise Attention Network for Monocular Depth Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the drawbacks of existing methods, we propose a patch-wise attention method for focusing on each local area. |
Sihaeng Lee; Janghyeon Lee; Byungju Kim; Eojindl Yi; Junmo Kim; |
| 915 | Semi-Supervised Learning for Multi-Task Scene Understanding By Neural Graph Consensus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. |
Marius Leordeanu; Mihai Cristian Pîrvu; Dragos Costea; Alina E Marcu; Emil Slusanschi; Rahul Sukthankar; |
| 916 | Static-Dynamic Interaction Networks for Offline Signature Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Static-Dynamic Interaction Network (SDINet) model which introduces sequential representation into static signature images. |
Huan Li; Ping Wei; Ping Hu; |
| 917 | Proposal-Free Video Grounding with Contextual Pyramid Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel proposal-free framework named Contextual Pyramid Network (CPNet) to investigate multi-scale temporal correlation in the video. |
Kun Li; Dan Guo; Meng Wang; |
| 918 | Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. |
Lincheng Li; Suzhen Wang; Zhimeng Zhang; Yu Ding; Yixing Zheng; Xin Yu; Changjie Fan; |
| 919 | Exploiting Learnable Joint Groups for Hand Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to estimate 3D hand pose by recovering the 3D coordinates of joints in a group-wise manner, where less-related joints are automatically categorized into different groups and exhibit different features. |
Moran Li; Yuan Gao; Nong Sang; |
| 920 | RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient and accurate 3D object detection method from stereo images, named RTS3D. |
Peixuan Li; Shun Su; Huaici Zhao; |
| 921 | Adversarial Pose Regression Network for Pose-Invariant Face Recognitions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the observation, we propose an Adversarial Pose Regression Network (APRN) to extract pose-invariant identity representations by disentangling their pose variation in hidden feature maps. |
Pengyu Li; Biao Wang; Lei Zhang; |
| 922 | Category Dictionary Guided Unsupervised Domain Adaptation for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a category dictionary guided (CDG) UDA model for cross-domain object detection, which learns category-specific dictionaries from the source domain to represent the candidate boxes in target domain. |
Shuai Li; Jianqiang Huang; Xian-Sheng Hua; Lei Zhang; |
| 923 | Joint Semantic-geometric Learning for Polygonal Building Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above issues, in this paper, we propose a polygonal building segmentation approach and make the following contributions: (1) We design a multi-task segmentation network for joint semantic and geometric learning via three tasks, i.e., pixel-wise building segmentation, multi-class corner prediction, and edge orientation prediction. |
Weijia Li; Wenqian Zhao; Huaping Zhong; Conghui He; Dahua Lin; |
| 924 | Generalized Zero-Shot Learning Via Disentangled Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel method, dubbed Disentangled-VAE, which aims to disentangle category-distilling factors and category-dispersing factors from visual as well as semantic features, respectively. |
Xiangyu Li; Zhe Xu; Kun Wei; Cheng Deng; |
| 925 | Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Omni-frequency Region-adaptive Network (OR-Net) to address both challenges, here we call features of all low, middle and high frequencies omni-frequency features. |
Xin Li; Xin Jin; Tao Yu; Simeng Sun; Yingxue Pang; Zhizheng Zhang; Zhibo Chen; |
| 926 | Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formulate WSSS as a novel group-wise learning task that explicitly models se- mantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models. |
Xueyi Li; Tianfei Zhou; Jianwu Li; Yi Zhou; Zhaoxiang Zhang; |
| 927 | Inference Fusion with Associative Semantics for Unseen Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, inspired from human cognitive experience, we propose a simple but effective dual-path detection model that further explores associative semantics to supplement the basic visual-semantic knowledge transfer. |
Yanan Li; Pengyang Li; Han Cui; Donghui Wang; |
| 928 | Deep Unsupervised Image Hashing By Maximizing Bit Entropy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an unsupervised deep hashing layer called Bi-Half Net that maximizes entropy of the binary codes. |
Yunqiang Li; Jan van Gemert; |
| 929 | Sequential End-to-end Network for Efficient Person Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a Sequential End-to-end Network (SeqNet) to extract superior features. |
Zhengjia Li; Duoqian Miao; |
| 930 | SD-Pose: Semantic Decomposition for Cross-Domain 6D Object Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this observation, we introduce an approach, SD-Pose, that explicitly decomposes the input image into multi-level semantic representations and then combines the merits of each representation to bridge the domain gap. |
Zhigang Li; Yinlin Hu; Mathieu Salzmann; Xiangyang Ji; |
| 931 | Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. |
Rongqin Liang; Yuanman Li; Xia Li; Yi Tang; Jiantao Zhou; Wenbin Zou; |
| 932 | Query-Memory Re-Aggregation for Weakly-supervised Video Object Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problem, we propose a novel Re-Aggregation based framework, which uses feature matching to efficiently find the target and capture the temporal dependencies from multiple frames to guide the segmentation. |
Fanchao Lin; Hongtao Xie; Yan Li; Yongdong Zhang; |
| 933 | Augmented Partial Mutual Learning with Frame Masking for Video Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Augmented Partial Mutual Learning (APML) training method where multiple decoders are trained jointly with mimicry losses between different decoders and different input variations. |
Ke Lin; Zhuoxin Gan; Liwei Wang; |
| 934 | Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose an audio spatialization framework to convert a monaural video into a binaural one exploiting the relationship across audio and visual components. |
Yan-Bo Lin; Yu-Chiang Frank Wang; |
| 935 | Single View Point Cloud Generation Via Unified 3D Prototype Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this problem by integrating image features with 3D prototype features. |
Yu Lin; Yigong Wang; Yi-Fan Li; Zhuoyi Wang; Yang Gao; Latifur Khan; |
| 936 | Self-Supervised Sketch-to-Image Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. |
Bingchen Liu; Yizhe Zhu; Kunpeng Song; Ahmed Elgammal; |
| 937 | TIME: Text and Image Mutual-Translation Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. |
Bingchen Liu; Kunpeng Song; Yizhe Zhu; Gerard de Melo; Ahmed Elgammal; |
| 938 | SA-BNN: State-Aware Binary Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a state-aware binary neural network (SA-BNN) equipped with the well designed state-aware gradient. |
Chunlei Liu; Peng Chen; Bohan Zhuang; Chunhua Shen; Baochang Zhang; Wenrui Ding; |
| 939 | Spatiotemporal Graph Neural Network Based Mask Reconstruction for Video Object Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel spatiotemporal graph neural network (STG-Net) to reconstruct more accurate masks for video object segmentation, which captures the local contexts by utilizing all proposals. |
Daizong Liu; Shuangjie Xu; Xiao-Yang Liu; Zichuan Xu; Wei Wei; Pan Zhou; |
| 940 | F2Net: Learning to Focus on The Foreground for Unsupervised Video Object Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these issues, we propose a novel Focus on Foreground Network (F2Net), which delves into the intra-inter frame details for the foreground objects and thus effectively improve the segmentation performance. |
Daizong Liu; Dongdong Yu; Changhu Wang; Pan Zhou; |
| 941 | Toward Realistic Virtual Try-on Through Landmark Guided Shape Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel virtual try-on network based on landmark-guided shape matching (LM-VTON). |
Guoqiang Liu; Dan Song; Ruofeng Tong; Min Tang; |
| 942 | Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion. |
Hongying Liu; Peng Zhao; Zhubo Ruan; Fanhua Shang; Yuanyuan Liu; |
| 943 | FCFR-Net: Feature Fusion Based Coarse-to-Fine Residual Learning for Depth Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparse-to-coarse stage and a coarse-to-fine stage. |
Lina Liu; Xibin Song; Xiaoyang Lyu; Junwei Diao; Mengmeng Wang; Yong Liu; Liangjun Zhang; |
| 944 | Activity Image-to-Video Retrieval By Disentangling Appearance and Motion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Motion-assisted Activity Proposal-based Image-to-Video Retrieval (MAP-IVR) approach to disentangle the video features into motion features and appearance features and obtain appearance features from the images. |
Liu Liu; Jiangtong Li; Li Niu; Ruicong Xu; Liqing Zhang; |
| 945 | Adaptive Pattern-Parameter Matching for Robust Pedestrian Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose in this paper a novel detection approach via adaptive pattern-parameter matching. |
Mengyin Liu; Chao Zhu; Jun Wang; Xu-Cheng Yin; |
| 946 | Temporal Segmentation of Fine-gained Semantic Action: A Motion-Centered Figure Skating Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to explore more models and practical applications of motion-centered TAS, we introduce a Motion-Centered Figure Skating (MCFS) dataset in this paper. |
Shenglan Liu; Aibin Zhang; Yunheng Li; Jian Zhou; Li Xu; Zhuben Dong; Renhao Zhang; |
| 947 | Learning Hybrid Relationships for Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method named Hybrid Relationship Network (HRNet) to learn the two types of relationships in a unified framework that makes use of their own advantages. |
Shuang Liu; Wenmin Huang; Zhong Zhang; |
| 948 | Translate The Facial Regions You Like Using Self-Adaptive Region Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel self-adaptive region translation network (SART) for region-level translation, which uses region-adaptive instance normalization (RIN) and a region matching loss (RML) for this task. |
Wenshuang Liu; Wenting Chen; Zhanjia Yang; Linlin Shen; |
| 949 | Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. |
Xiaofeng Liu; Xiongchang Liu; Bo Hu; Wenxuan Ji; Fangxu Xing; Jun Lu; Jane You; C.-C. Jay Kuo; Georges El Fakhri; Jonghye Woo; |
| 950 | FontRL: Chinese Font Synthesis Via Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose FontRL, a novel method for Chinese font synthesis by using deep reinforcement learning. |
Yitian Liu; Zhouhui Lian; |
| 951 | Hierarchical Information Passing Based Noise-Tolerant Hybrid Learning for Semi-Supervised Human Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate annotation efforts, we propose a new semi-supervised human parsing method for which we only need a small number of labels for training. |
Yunan Liu; Shanshan Zhang; Jian Yang; PongChi Yuen; |
| 952 | Delving Into Variance Transmission and Normalization: Shift of Average Gradient Makes The Network Collapse Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that the problem of the shift of the average gradient will amplify the variance of every convolutional (conv) layer. |
Yuxiang Liu; Jidong Ge; Chuanyi Li; Jie Gui; |
| 953 | Aggregated Multi-GANs for Controlled 3D Human Motion Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. |
Zhenguang Liu; Kedi Lyu; Shuang Wu; Haipeng Chen; Yanbin Hao; Shouling Ji; |
| 954 | ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce an Action-Context Separation Network (ACSNet) that explicitly takes into account context for accurate action localization. |
Ziyi Liu; Le Wang; Qilin Zhang; Wei Tang; Junsong Yuan; Nanning Zheng; Gang Hua; |
| 955 | Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we introduce a framework that learns two feature subspaces respectively for actions and their context. |
Ziyi Liu; Le Wang; Wei Tang; Junsong Yuan; Nanning Zheng; Gang Hua; |
| 956 | PointINet: Point Cloud Frame Interpolation Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve that, we propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). |
Fan Lu; Guang Chen; Sanqing Qu; Zhijun Li; Yinlong Liu; Alois Knoll; |
| 957 | A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, instead of locally detecting and masking out occlusions and moving objects, we propose to alleviate their negative effects on monocular VO implicitly but more effectively from two global perspectives. |
Yao Lu; Xiaoli Xu; Mingyu Ding; Zhiwu Lu; Tao Xiang; |
| 958 | PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose to exploit 3D pose to calibrate human mesh. |
Tianyu Luan; Yali Wang; Junhao Zhang; Zhe Wang; Zhipeng Zhou; Yu Qiao; |
| 959 | DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel learning-based network, named DeepDT, is proposed to reconstruct the surface from Delaunay triangulation of point cloud. |
Yiming Luo; Zhenxing Mi; Wenbing Tao; |
| 960 | Dual-level Collaborative Transformer for Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. |
Yunpeng Luo; Jiayi Ji; Xiaoshuai Sun; Liujuan Cao; Yongjian Wu; Feiyue Huang; Chia-Wen Lin; Rongrong Ji; |
| 961 | HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we find the core reason comes from the inaccurate depth estimation in large gradient regions, making the bilinear interpolation error gradually disappear as the resolution increases. |
Xiaoyang Lyu; Liang Liu; Mengmeng Wang; Xin Kong; Lina Liu; Yong Liu; Xinxin Chen; Yi Yuan; |
| 962 | SMIL: Multimodal Learning with Severely Missing Modality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Technically, we propose a new method named SMIL that leverages Bayesian meta-learning in uniformly achieving both objectives. |
Mengmeng Ma; Jian Ren; Long Zhao; Sergey Tulyakov; Cathy Wu; Xi Peng; |
| 963 | Pyramidal Feature Shrinking for Salient Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose pyramidal feature shrinking network (PFSNet), which aims to aggregate adjacent feature nodes in pairs with layer-by-layer shrinkage, so that the aggregated features fuse effective details and semantics together and discard interference information. |
Mingcan Ma; Changqun Xia; Jia Li; |
| 964 | Learning to Count Via Unbalanced Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate crowd counting as a measure regression problem to minimize the distance between two measures with different supports and unequal total mass. |
Zhiheng Ma; Xing Wei; Xiaopeng Hong; Hui Lin; Yunfeng Qiu; Yihong Gong; |
| 965 | Scene Graph Embeddings Using Relative Similarity Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we employ a graph convolutional network to exploit structure in scene graphs and produce image embeddings useful for semantic image retrieval. |
Paridhi Maheshwari; Ritwick Chaudhry; Vishwa Vinay; |
| 966 | Few-Shot Lifelong Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Few-Shot Lifelong Learning (FSLL) method that enables deep learning models to perform lifelong/continual learning on few-shot data. |
Pratik Mazumder; Pravendra Singh; Piyush Rai; |
| 967 | CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. |
Matias Mendieta; Hamed Tabkhi; |
| 968 | Dynamic Anchor Learning for Arbitrary-Oriented Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a dynamic anchor learning (DAL) method, which utilizes the newly defined matching degree to comprehensively evaluate the localization potential of the anchors and carries out a more efficient label assignment process. |
Qi Ming; Zhiqiang Zhou; Lingjuan Miao; Hongwei Zhang; Linhao Li; |
| 969 | Terrace-based Food Counting and Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents how the terrace model deals with arbitrary shape, size, obscure boundary and occlusion of instances, where other techniques are currently short of. |
Huu-Thanh Nguyen; Chong-Wah Ngo; |
| 970 | Embodied Visual Active Learning for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We extensively evaluate the proposed models using the photorealistic Matterport3D simulator and show that a fully learnt method outperforms comparable pre-specified counterparts, even when requesting fewer annotations. |
David Nilsson; Aleksis Pirinen; Erik Gärtner; Cristian Sminchisescu; |
| 971 | TDAF: Top-Down Attention Framework for Vision Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models. |
Bo Pang; Yizhuo Li; Jiefeng Li; Muchen Li; Hanwen Cao; Cewu Lu; |
| 972 | Few-shot Font Generation with Localized Style Representations and Factorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel font generation method by learning localized styles, namely component-wise style representations, instead of universal styles. |
Song Park; Sanghyuk Chun; Junbum Cha; Bado Lee; Hyunjung Shim; |
| 973 | Learning Disentangled Representation for Fair Facial Attribute Classification Via Fairness-aware Information Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the limitation, we propose Fairness-aware Disentangling Variational Auto-Encoder (FD-VAE) that disentangles data representation into three subspaces: 1) Target Attribute Latent (TAL), 2) Protected Attribute Latent (PAL), 3) Mutual Attribute Latent (MAL). |
Sungho Park; Sunhee Hwang; Dohyung Kim; Hyeran Byun; |
| 974 | Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve the restricted nature of existing video generation models’ ability to handle arbitrary timesteps, we propose continuous-time video generation by combining neural ODE (Vid-ODE) with pixel-level video processing techniques. |
Sunghyun Park; Kangyeol Kim; Junsoo Lee; Jaegul Choo; Joonseok Lee; Sookyung Kim; Edward Choi; |
| 975 | CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we endeavour to discover the entities and their corresponding importance in cooking recipes automatically as a visual-linguistic association problem. |
Hai X. Pham; Ricardo Guerrero; Vladimir Pavlovic; Jiatong Li; |
| 976 | Explainable Models with Consistent Interpretations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, rather than introducing a novel explanation method, we learn models that are encouraged to be interpretable given an explanation method. |
Vipin Pillai; Hamed Pirsiavash; |
| 977 | Dual Adversarial Graph Neural Networks for Multi-label Cross-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Dual Adversarial Graph Neural Networks (DAGNN) composed of the dual generative adversarial networks and the multi-hop graph neural networks, which learn modality-invariant and discriminative common representations for cross-modal retrieval. |
Shengsheng Qian; Dizhan Xue; Huaiwen Zhang; Quan Fang; Changsheng Xu; |
| 978 | KGDet: Keypoint-Guided Fashion Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To incorporate the keypoint cues into clothing detection, we design a simple yet effective Keypoint-Guided clothing Detector, named KGDet. |
Shenhan Qian; Dongze Lian; Binqiang Zhao; Tong Liu; Bohui Zhu; Hai Li; Shenghua Gao; |
| 979 | Learning Modulated Loss for Rotated Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the aforementioned integration can cause training instability and performance degeneration. |
Wen Qian; Xue Yang; Silong Peng; Junchi Yan; Yue Guo; |
| 980 | MANGO: A Mask Attention Guided One-Stage Scene Text Spotter Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. |
Liang Qiao; Ying Chen; Zhanzhan Cheng; Yunlu Xu; Yi Niu; Shiliang Pu; Fei Wu; |
| 981 | REFINE: Prediction Fusion Network for Panoptic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present REFINE, pREdiction FusIon NEtwork for panoptic segmentation, to achieve high-quality panoptic segmentation by improving cross-task prediction fusion, and within-task prediction fusion. |
Jiawei Ren; Cunjun Yu; Zhongang Cai; Mingyuan Zhang; Chongsong Chen; Haiyu Zhao; Shuai Yi; Hongsheng Li; |
| 982 | AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on our discussion, we propose an algorithm that improves fine-tuning performance and reduces network complexity through layer-wise pruning and auto-tuning of layer-wise learning rates. |
Youngmin Ro; Jin Young Choi; |
| 983 | DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these limitations, we propose a novel dynamic and progressive filter pruning (DPFPS) scheme that directly learns a structured sparsity network from Scratch. |
Xiaofeng Ruan; Yufan Liu; Bing Li; Chunfeng Yuan; Weiming Hu; |
| 984 | Efficient Certification of Spatial Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose novel convex relaxations, enabling us, for the first time, to provide a certificate of robustness against vector field transformations. |
Anian Ruoss; Maximilian Baader; Mislav Balunović; Martin Vechev; |
| 985 | Semantic Grouping Network for Video Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of partially decoded caption and then (2) to decode those semantically aligned groups in predicting the next word. |
Hobin Ryu; Sunghun Kang; Haeyong Kang; Chang D. Yoo; |
| 986 | Audio-Visual Localization By Synthetic Acoustic Image Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To exploit this empowered modality while using standard microphones and cameras we propose to leverage the generation of synthetic acoustic images from common audio-video data for the task of audio-visual localization. |
Valentina Sanguineti; Pietro Morerio; Alessio Del Bue; Vittorio Murino; |
| 987 | Enhanced Regularizers for Attributional Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a robust attribution training strategy to improve attributional robustness of deep neural networks. |
Anindya Sarkar; Anirban Sarkar; Vineeth N Balasubramanian; |
| 988 | Progressive Network Grafting for Few-Shot Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the practical few-shot knowledge distillation scenario, where we assume only a few samples without human annotations are available for each category. |
Chengchao Shen; Xinchao Wang; Youtan Yin; Jie Song; Sihui Luo; Mingli Song; |
| 989 | Social-DPF: Socially Acceptable Distribution Prediction of Futures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a model that incorporates multiple interacting motion sequences jointly and predicts multi-modal socially acceptable distributions of futures. |
Xiaodan Shi; Xiaowei Shao; Guangming Wu; Haoran Zhang; Zhiling Guo; Renhe Jiang; Ryosuke Shibasaki; |
| 990 | Robust Knowledge Transfer Via Hybrid Forward on The Teacher-Student Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we denote a well-trained model as a teacher network and a model for the new task as a student network. |
Liangchen Song; Jialian Wu; Ming Yang; Qian Zhang; Yuan Li; Junsong Yuan; |
| 991 | AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multi-level semantics while keeping the efficiency high. |
Qi Song; Kangfu Mei; Rui Huang; |
| 992 | To Choose or to Fuse? Scale Selection for Crowd Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the large scale variation problem in crowd counting by taking full advantage of the multi-scale feature representations in a multi-level network. |
Qingyu Song; Changan Wang; Yabiao Wang; Ying Tai; Chengjie Wang; Jilin Li; Jian Wu; Jiayi Ma; |
| 993 | Image Captioning with Context-Aware Auxiliary Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Context-Aware Auxiliary Guidance (CAAG) mechanism that can guide the captioning model to perceive global contexts. |
Zeliang Song; Xiaofei Zhou; Zhendong Mao; Jianlong Tan; |
| 994 | Unsupervised Model Adaptation for Continual Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. |
Serban Stan; Mohammad Rostami; |
| 995 | BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation. |
Haisheng Su; Weihao Gan; Wei Wu; Yu Qiao; Junjie Yan; |
| 996 | MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. |
Hao Su; Jianwei Niu; Xuefeng Liu; Qingfeng Li; Jiahe Cui; Ji Wan; |
| 997 | MAMBA: Multi-level Aggregation Via Memory Bank for Video Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-level aggregation architecture via memory bank called MAMBA. |
Guanxiong Sun; Yang Hua; Guosheng Hu; Neil Robertson; |
| 998 | Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. |
He Sun; Katherine L. Bouman; |
| 999 | Domain General Face Forgery Detection By Learning to Weight Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before. |
Ke Sun; Hong Liu; Qixiang Ye; Yue Gao; Jianzhuang Liu; Ling Shao; Rongrong Ji; |
| 1000 | Object-Centric Image Generation from Layouts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. |
Tristan Sylvain; Pengchuan Zhang; Yoshua Bengio; R Devon Hjelm; Shikhar Sharma; |
| 1001 | Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance. |
Jilin Tang; Yi Yuan; Tianjia Shao; Yong Liu; Mengmeng Wang; Kun Zhou; |
| 1002 | Gradient Regularized Contrastive Learning for Continual Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains. |
Shixiang Tang; Peng Su; Dapeng Chen; Wanli Ouyang; |
| 1003 | Adversarial Training Reduces Information and Improves Transferability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The latter property may seem counter-intuitive as it is widely accepted by the community that classification models should only capture the minimal information (features) required for the task. |
Matteo Terzi; Alessandro Achille; Marco Maggipinto; Gian Antonio Susto; |
| 1004 | Adversarial Turing Patterns from Cellular Automata Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a theoretical bridge between these two different theories, by mapping a simplified algorithm for crafting universal perturbations to (inhomogeneous) cellular automata, the latter is known to generate Turing patterns. |
Nurislam Tursynbek; Ilya Vilkoviskiy; Maria Sindeeva; Ivan Oseledets; |
| 1005 | Artificial Dummies for Urban Dataset Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the setup with only day-time data available, we improve the night-time detector by 17% log-average miss rate over the detector trained with the day-time data only. |
Antonín Vobecký; David Hurych; Michal Uřičář; Patrick Pérez; Josef Sivic; |
| 1006 | SCNet: Training Inference Sample Consistency for Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an architecture referred to as Sample Consistency Network (SCNet) to ensure that the IoU distribution of the samples at training time is close to that at inference time. |
Thang Vu; Haeyong Kang; Chang D. Yoo; |
| 1007 | Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Dual-Contrastive Embedding Network (DCEN) that simultaneously learns task-specific and task-independent knowledge via semantic alignment and instance discrimination. |
Chaoqun Wang; Xuejin Chen; Shaobo Min; Xiaoyan Sun; Houqiang Li; |
| 1008 | Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Object-level Visual Context modeling framework (OVC) to efficiently capture and explore visual information for multimodal machine translation. |
Dexin Wang; Deyi Xiong; |
| 1009 | Temporal Relational Modeling with Self-Supervision for Action Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, in this paper, we introduce an effective GCN module, Dilated Temporal Graph Reasoning Module (DTGRM), designed to model temporal relations and dependencies between video frames at various time spans. |
Dong Wang; Di Hu; Xingjian Li; Dejing Dou; |
| 1010 | Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a robust Visual Information Extraction System (VIES) towards real-world scenarios, which is an unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. |
Jiapeng Wang; Chongyu Liu; Lianwen Jin; Guozhi Tang; Jiaxin Zhang; Shuaitao Zhang; Qianying Wang; Yaqiang Wu; Mingxiang Cai; |
| 1011 | Self-Domain Adaptation for Face Anti-Spoofing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference. |
Jingjing Wang; Jingyi Zhang; Ying Bian; Youyi Cai; Chunmao Wang; Shiliang Pu; |
| 1012 | Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), which is the first work to learn deep quantization from weakly tagged images. |
Jinpeng Wang; Bin Chen; Qiang Zhang; Zaiqiao Meng; Shangsong Liang; Shutao Xia; |
| 1013 | Camera-Aware Proxies for Unsupervised Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. |
Menglin Wang; Baisheng Lai; Jianqiang Huang; Xiaojin Gong; Xian-Sheng Hua; |
| 1014 | Unsupervised 3D Learning for Shape Analysis Via Multiresolution Instance Discrimination Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. |
Peng-Shuai Wang; Yu-Qi Yang; Qian-Fang Zou; Zhirong Wu; Yang Liu; Xin Tong; |
| 1015 | PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to address the above problems, we propose a novel fully convolutional Point Gathering Network (PGNet) for reading arbitrarily-shaped text in real-time. |
Pengfei Wang; Chengquan Zhang; Fei Qi; Shanshan Liu; Xiaoqiang Zhang; Pengyuan Lyu; Junyu Han; Jingtuo Liu; Errui Ding; Guangming Shi; |
| 1016 | Dynamic Position-aware Network for Fine-grained Image Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose an end-to-end Dynamic Position-aware Network (DP-Net) to directly incorporate the position clues into visual content and dynamically align them without extra annotations, which eliminates the effect of position information for visual variances of subcategories. |
Shijie Wang; Haojie Li; Zhihui Wang; Wanli Ouyang; |
| 1017 | Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple but effective mechanism, called Co-mining, for sparsely annotated object detection. |
Tiancai Wang; Tong Yang; Jiale Cao; Xiangyu Zhang; |
| 1018 | Very Important Person Localization in Unconstrained Conditions: A New Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new high-quality dataset for Very Important Person Localization (VIPLoc), named Unconstrained-7k. |
Xiao Wang; Zheng Wang; Toshihiko Yamasaki; Wenjun Zeng; |
| 1019 | Teacher Guided Neural Architecture Search for Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a novel teacher guided neural architecture search method to directly search for a student network with flexible channel and layer sizes. |
Xiaobo Wang; |
| 1020 | Deep Multi-Task Learning for Diabetic Retinopathy Grading in Fundus Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we mainly focus on diabetic retinopathy (DR) grading with LR fundus images. |
Xiaofei Wang; Mai Xu; Jicong Zhang; Lai Jiang; Liu Li; |
| 1021 | Confidence-aware Non-repetitive Multimodal Transformers for TextCaps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a Confidence-aware Non-repetitive Multimodal Transformers (CNMT) to tackle the above challenges. |
Zhaokai Wang; Renda Bao; Qi Wu; Si Liu; |
| 1022 | Geodesic-HOF: 3D Reconstruction Without Cutting Corners Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose an approach to 3D reconstruction that embeds points on the surface of an object into a higher-dimensional space that captures both the original 3D surface as well as geodesic distances between points on the surface of the object. |
Ziyun Wang; Eric A. Mitchell; Volkan Isler; Daniel D. Lee; |
| 1023 | C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Coarse-to-Fine Flow Warping Network (C2F-FWN) for spatial-temporal consistent HVMT. |
Dongxu Wei; Xiaowei Xu; Haibin Shen; Kejie Huang; |
| 1024 | Semantic Consistency Networks for 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel semantic consistency network (SCNet) driven by a natural principle: the class of a predicted 3D bounding box should be consistent with the classes of all the points inside this box. |
Wenwen Wei; Ping Wei; Nanning Zheng; |
| 1025 | Holistic Multi-View Building Analysis in The Wild with Projection Pooling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. |
Zbigniew Wojna; Krzysztof Maziarz; Łukasz Jocz; Robert Pałuba; Robert Kozikowski; Iason Kokkinos; |
| 1026 | Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the effect of adversarial perturbations of images on the estimates of disparity by deep learning models trained for stereo. |
Alex Wong; Mukund Mundhra; Stefano Soatto; |
| 1027 | Generalising Without Forgetting for Lifelong Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we call this lifelong person Re-ID, characterised by solving a problem of unseen class identification subject to continuous new domain generalisation and adaptation with class imbalanced learning. |
Guile Wu; Shaogang Gong; |
| 1028 | Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we solve the Re-ID problem by decentralised learning from non-shared private training data distributed at multiple user sites of independent multi-domain label spaces. |
Guile Wu; Shaogang Gong; |
| 1029 | Region-aware Global Context Modeling for Automatic Nerve Segmentation from Ultrasound Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel deep learning model equipped with a new region-aware global context modeling technique for automatic nerve segmentation from ultrasound images, which is a challenging task due to (1) the large variation and blurred boundaries of targets, (2) the large amount of speckle noise in ultrasound images, and (3) the inherent real-time requirement of this task. |
Huisi Wu; Jiasheng Liu; Wei Wang; Zhenkun Wen; Jing Qin; |
| 1030 | Precise Yet Efficient Semantic Calibration and Refinement in ConvNets for Real-time Polyp Segmentation from Colonoscopy Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel convolutional neural network (ConvNet) equipped with two new semantic calibration and refinement approaches for automatic polyp segmentation from colonoscopy videos. |
Huisi Wu; Jiafu Zhong; Wei Wang; Zhenkun Wen; Jing Qin; |
| 1031 | Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the above issues by formulating the HMER as a graph-to-graph (G2G) learning problem. |
Jin-Wen Wu; Fei Yin; Yan-Ming Zhang; Xu-Yao Zhang; Cheng-Lin Liu; |
| 1032 | Learning Comprehensive Motion Representation for Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent efforts attempt to capture motion information by establishing inter-frame connections while still suffering the limited temporal receptive field or high latency. |
Mingyu Wu; Boyuan Jiang; Donghao Luo; Junchi Yan; Yabiao Wang; Ying Tai; Chengjie Wang; Jilin Li; Feiyue Huang; Xiaokang Yang; |
| 1033 | MVFNet: Multi-View Fusion Network for Efficient Video Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we attempt to acquire both efficiency and effectiveness simultaneously. |
Wenhao Wu; Dongliang He; Tianwei Lin; Fu Li; Chuang Gan; Errui Ding; |
| 1034 | Anticipating Future Relations Via Graph Growing for Action Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate how the interaction and correlation between visual objects evolve and propose a graph growing method to anticipate future object relations from limited video observations for reliable action prediction. |
Xinxiao Wu; Jianwei Zhao; Ruiqi Wang; |
| 1035 | Binaural Audio-Visual Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a deep learning method for pixel-level sound source localization by leveraging both binaural recordings and the corresponding videos. |
Xinyi Wu; Zhenyao Wu; Lili Ju; Song Wang; |
| 1036 | Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: State-of-the-art adversarial example detection methods characterize an input example as adversarial either by quantifying the magnitude of feature variations under multiple perturbations or by measuring its distance from estimated benign example distribution. |
Yuhang Wu; Sunpreet S Arora; Yanhong Wu; Hao Yang; |
| 1037 | Shape-Pose Ambiguity in Learning 3D Reconstruction from Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to resolve the ambiguity without extra pose-aware labels or annotations. |
Yunjie Wu; Zhengxing Sun; Youcheng Song; Yunhan Sun; YiJie Zhong; Jinlong Shi; |
| 1038 | Boundary Proposal Network for Two-stage Natural Language Video Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal two-stage framework that gets rid of the issues mentioned above. |
Shaoning Xiao; Long Chen; Songyang Zhang; Wei Ji; Jian Shao; Lu Ye; Jun Xiao; |
| 1039 | Amodal Segmentation Based on Visible Region Segmentation and Shape Prior Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mimic the behavior of human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. |
Yuting Xiao; Yanyu Xu; Ziming Zhong; Weixin Luo; Jiawei Li; Shenghua Gao; |
| 1040 | Locate Globally, Segment Locally: A Progressive Architecture With Knowledge Review Network for Salient Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We assume that the human vision system orderly locates and segments objects, so we propose a novel progressive architecture with knowledge review network (PA-KRN) for SOD. |
Binwei Xu; Haoran Liang; Ronghua Liang; Peng Chen; |
| 1041 | Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. |
Chenxin Xu; Siheng Chen; Maosen Li; Ya Zhang; |
| 1042 | Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel imagine-reason-write generation framework (IRW) for visual storytelling, inspired by the logic of humans when they write the story. |
Chunpu Xu; Min Yang; Chengming Li; Ying Shen; Xiang Ao; Ruifeng Xu; |
| 1043 | Self-supervised Multi-view Stereo Via Effective Co-Segmentation and Data-Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. |
Hongbin Xu; Zhipeng Zhou; Yu Qiao; Wenxiong Kang; Qiuxia Wu; |
| 1044 | Efficient Deep Image Denoising Via Class Specific Convolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification. |
Lu Xu; Jiawei Zhang; Xuanye Cheng; Feng Zhang; Xing Wei; Jimmy Ren; |
| 1045 | Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose a novel Indistinguishable Area Focalization Network (IAF-Net), which select indistinguishable points adaptively by utilizing the hierarchical semantic features and enhance fine-grained features for points especially those indistinguishable points. |
Mingye Xu; Zhipeng Zhou; Junhao Zhang; Yu Qiao; |
| 1046 | Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose Geometry-Disentangled Attention Network (GDANet). |
Mutian Xu; Junhao Zhang; Zhipeng Zhou; Mingye Xu; Xiaojuan Qi; Yu Qiao; |
| 1047 | Searching for Alignment in Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We construct a well-defined searching space by decomposing the template searching into the crop size and vertical shift, and propose an efficient method Face Alignment Policy Search (FAPS). |
Xiaqing Xu; Qiang Meng; Yunxiao Qin; Jianzhu Guo; Chenxu Zhao; Feng Zhou; Zhen Lei; |
| 1048 | GIF Thumbnails: Attract More Clicks to Your Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address a novel problem, namely GIF thumbnail generation, which aims to automatically generate GIF thumbnails for videos and consequently boost their Click-Through-Rate (CTR). |
Yi Xu; Fan Bai; Yingxuan Shi; Qiuyu Chen; Longwen Gao; Kai Tian; Shuigeng Zhou; Huyang Sun; |
| 1049 | FaceController: Controllable Attribute Editing for Face in The Wild Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Unlike prior works such as GAN inversion which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. |
Zhiliang Xu; Xiyu Yu; Zhibin Hong; Zhen Zhu; Junyu Han; Jingtuo Liu; Errui Ding; Xiang Bai; |
| 1050 | AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy. |
Zixuan Xu; Banghuai Li; Ye Yuan; Miao Geng; |
| 1051 | Sparse Single Sweep LiDAR Point Cloud Segmentation Via Learning Contextual Shape Priors from Scene Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors. |
Xu Yan; Jiantao Gao; Jie Li; Ruimao Zhang; Zhen Li; Rui Huang; Shuguang Cui; |
| 1052 | Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a Semantic Context based Anomaly Detection Network, SCADN, for unsupervised anomaly detection by learning the semantic context from the normal samples. |
Xudong Yan; Huaidong Zhang; Xuemiao Xu; Xiaowei Hu; Pheng-Ann Heng; |
| 1053 | Non-Autoregressive Coarse-to-Fine Video Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a non-autoregressive decoding based model with a coarse-to-fine captioning procedure to alleviate these defects. |
Bang Yang; Yuexian Zou; Fenglin Liu; Can Zhang; |
| 1054 | Learning to Attack Real-World Models for Person Re-identification Via Virtual-Guided Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we argue that learning powerful attackers with high universality that works well on unseen domains is an important step in promoting the robustness of re-ID systems. |
Fengxiang Yang; Zhun Zhong; Hong Liu; Zheng Wang; Zhiming Luo; Shaozi Li; Nicu Sebe; Shin’ichi Satoh; |
| 1055 | Object Relation Attention for Image Paragraph Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It is still an open question how to achieve this goal, and for it we propose a method to incorporate objects’ spatial coherence into a language-generating model. |
Li-Chuan Yang; Chih-Yuan Yang; Jane Yung-jen Hsu; |
| 1056 | Adversarial Robustness Through Disentangled Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this motivation, we propose a novel defense method called Deep Robust Representation Disentanglement Network (DRRDN). |
Shuo Yang; Tianyu Guo; Yunhe Wang; Chang Xu; |
| 1057 | CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel generative adversarial network to generate 3D point clouds from random latent codes, named Controllable Point Cloud Generative Adversarial Network(CPCGAN). |
Ximing Yang; Yuan Wu; Kaiyi Zhang; Cheng Jin; |
| 1058 | R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity. |
Xue Yang; Junchi Yan; Ziming Feng; Tao He; |
| 1059 | One-shot Face Reenactment Using Appearance Adaptive Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance. |
Guangming Yao; Yi Yuan; Tianjia Shao; Shuang Li; Shanqi Liu; Yong Liu; Mengmeng Wang; Kun Zhou; |
| 1060 | A Case Study of The Shortcut Effects in Visual Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve the issue, we propose a curriculum-based masking approach, as a mechanism to perform more robust training. |
Keren Ye; Adriana Kovashka; |
| 1061 | Instance Mining with Class Feature Banks for Weakly Supervised Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problem, we introduce a novel Instance Mining with Class Feature Banks (IM-CFB) framework, which includes a Class Feature Banks (CFB) module and a Feature Guided Instance Mining (FGIM) algorithm. |
Yufei Yin; Jiajun Deng; Wengang Zhou; Houqiang Li; |
| 1062 | Multimodal Fusion Via Teacher-Student Network for Indoor Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Teacher-Student Multimodal Fusion (TSMF) model that fuses the skeleton and RGB modalities at the model level for indoor action recognition. |
Bruce X.B. Yu; Yan Liu; Keith C.C. Chan; |
| 1063 | ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. |
Fei Yu; Jiji Tang; Weichong Yin; Yu Sun; Hao Tian; Hua Wu; Haifeng Wang; |
| 1064 | High-Resolution Deep Image Matting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose HDMatt, a first deep learning based image matting approach for high-resolution inputs. |
Haichao Yu; Ning Xu; Zilong Huang; Yuqian Zhou; Humphrey Shi; |
| 1065 | CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Channel-wise Automatic KErnel Shrinking (CAKES), to enable efficient 3D learning by shrinking standard 3D convolutions into a set of economic operations (e.g., 1D, 2D convolutions). |
Qihang Yu; Yingwei Li; Jieru Mei; Yuyin Zhou; Alan Yuille; |
| 1066 | Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data. |
Siyue Yu; Bingfeng Zhang; Jimin Xiao; Eng Gee Lim; |
| 1067 | Fast and Compact Bilinear Pooling By Shifted Random Maclaurin Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Shifted Random Maclaurin (SRM) strategy for fast and compact bilinear pooling. |
Tan Yu; Xiaoyun Li; Ping Li; |
| 1068 | Simple and Effective Stochastic Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a simple and effective stochastic neural network (SE-SNN) architecture for discriminative learning by directly modeling activation uncertainty and encouraging high activation variability. |
Tianyuan Yu; Yongxin Yang; Da Li; Timothy Hospedales; Tao Xiang; |
| 1069 | Learning Visual Context for Group Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new reasoning paradigm to incorporate global contextual information. |
Hangjie Yuan; Dong Ni; |
| 1070 | StrokeGAN: Reducing Mode Collapse in Chinese Font Generation Via Stroke Encoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a one-bit stroke encoding to capture the key mode information of Chinese characters and then incorporate it into CycleGAN, a popular deep generative model for Chinese font generation. |
Jinshan Zeng; Qi Chen; Yunxin Liu; Mingwen Wang; Yuan Yao; |
| 1071 | Demodalizing Face Recognition with Synthetic Samples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, after observing in experiments that modality information has a fixed form, we propose a demodalizing face recognition training architecture for the first time and provide a feasible method for recognition training using synthetic samples. |
Zhonghua Zhai; Pengju Yang; Xiaofeng Zhang; Maji Huang; Haijing Cheng; Xuejun Yan; Chunmao Wang; Shiliang Pu; |
| 1072 | EMLight: Lighting Estimation Via Spherical Distribution Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Earth Mover’s Light (EMLight), an illumination estimation framework that leverages a regression network and a neural projector for accurate illumination estimation. |
Fangneng Zhan; Changgong Zhang; Yingchen Yu; Yuan Chang; Shijian Lu; Feiying Ma; Xuansong Xie; |
| 1073 | Universal Adversarial Perturbations Through The Lens of Deep Steganography: Towards A Fourier Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We perform task-specific and joint analysis and reveal that (a) frequency is a key factor that influences their performance based on the proposed entropy metric for quantifying the frequency distribution; (b) their success can be attributed to a DNN being highly sensitive to high-frequency content. |
Chaoning Zhang; Philipp Benz; Adil Karjauv; In So Kweon; |
| 1074 | SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a new learnable geometric-unrelated rectification, Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network. |
Chengwei Zhang; Yunlu Xu; Zhanzhan Cheng; Shiliang Pu; Yi Niu; Fei Wu; Futai Zou; |
| 1075 | Visual Tracking Via Hierarchical Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show how to teach machines to track a generic object in videos like humans, who can use a few search steps to perform tracking. |
Dawei Zhang; Zhonglong Zheng; Riheng Jia; Minglu Li; |
| 1076 | One for More: Selecting Generalizable Samples for Generalizable ReID Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, instead of simply presuming on what samples are generalizable, this paper proposes a one-for-more training objective that directly takes the generalization ability of selected samples as a loss function and learn a sampler to automatically select generalizable samples. |
Enwei Zhang; Xinyang Jiang; Hao Cheng; Ancong Wu; Fufu Yu; Ke Li; Xiaowei Guo; Feng Zheng; Weishi Zheng; Xing Sun; |
| 1077 | Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The extensive ablation studies reveal the ever-changing dynamics throughout the training process, necessitating the incorporation of an automated and adaptive learning strategy as presented in this paper. |
Gengwei Zhang; Yiming Gao; Hang Xu; Hao Zhang; Zhenguo Li; Xiaodan Liang; |
| 1078 | SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The practical application requests both accuracy and efficiency on multi-person pose estimation algorithms. |
Jiabin Zhang; Zheng Zhu; Jiwen Lu; Junjie Huang; Guan Huang; Jie Zhou; |
| 1079 | Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a novel audio-visual representation learning method dubbed self-supervised curriculum learning (SSCL) under the teacher-student learning manner. |
Jingran Zhang; Xing Xu; Fumin Shen; Huimin Lu; Xin Liu; Heng Tao Shen; |
| 1080 | Unsupervised Domain Adaptation for Person Re-identification Via Heterogeneous Graph Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a coarse-to-fine heterogeneous graph alignment (HGA) method to find cross-camera person matches by characterizing the unlabeled data as a heterogeneous graph for each camera. |
Minying Zhang; Kai Liu; Yidong Li; Shihui Guo; Hongtao Duan; Yimin Long; Yi Jin; |
| 1081 | Proactive Privacy-preserving Learning for Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a data-centric Proactive Privacy-preserving Learning (PPL) algorithm for hashing based retrieval, which achieves the protection purpose by employing a generator to transfer the original data into the adversarial data with quasi-imperceptible perturbations before releasing them. |
Peng-Fei Zhang; Zi Huang; Xin-Shun Xu; |
| 1082 | A Novel Visual Interpretability for Deep Neural Networks By Optimizing Activation Maps with Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, a two-stage framework for visualizing the interpretability of deep neural networks, called Activation Optimized with Perturbation (AOP), is designed to optimize activation maps generated by general activation-based methods with the help of perturbation-based methods. |
Qinglong Zhang; Lu Rao; Yubin Yang; |
| 1083 | Point Cloud Semantic Scene Completion from RGB-D Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we devise a novel semantic completion network, called point cloud semantic scene completion network (PCSSC-Net), for indoor scenes solely based on point clouds. |
Shoulong Zhang; Shuai Li; Aimin Hao; Hong Qin; |
| 1084 | Consensus Graph Representation Learning for Better Grounded Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a novel perspective on the issue above: exploiting the semantic coherency between the visual and language modalities. |
Wenqiao Zhang; Haochen Shi; Siliang Tang; Jun Xiao; Qiang Yu; Yueting Zhuang; |
| 1085 | BoW Pooling: A Plug-and-Play Unit for Feature Aggregation of Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the BoW pooling, a plug-and-play unit to substitute the max pooling. |
Xiang Zhang; Xiao Sun; Zhouhui Lian; |
| 1086 | Diverse Knowledge Distillation for End-to-End Person Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the gap between the two kinds of methods is mainly caused by the Re-ID sub-networks of end-to-end methods. |
Xinyu Zhang; Xinlong Wang; Jia-Wang Bian; Chunhua Shen; Mingyu You; |
| 1087 | Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an effective weakly supervised method containing two components to solve the above problem. |
Yachao Zhang; Zonghao Li; Yuan Xie; Yanyun Qu; Cuihua Li; Tao Mei; |
| 1088 | PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel two-stage framework, namely PC-RGNN, which deals with these challenges by two specific solutions. |
Yanan Zhang; Di Huang; Yunhong Wang; |
| 1089 | Efficient License Plate Recognition Via Holistic Position Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this issue, we propose a novel holistic position attention (HPA) in this paper that consists of position network and shared classifier. |
Yesheng Zhang; Zilei Wang; Jiafan Zhuang; |
| 1090 | Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on … |
Yongshun Zhang; Xiu-Shen Wei; Boyan Zhou; Jianxin Wu; |
| 1091 | Depth Privileged Object Detection in Indoor Scenes Via Deformation Hallucination Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Considering depth images are unavailable in some scenarios, we focus on depth privileged object detection in indoor scenes, where the depth images are only available in the training phase. |
Zhijie Zhang; Yan Liu; Junjie Chen; Li Niu; Liqing Zhang; |
| 1092 | Learning Flexibly Distributional Representation for Low-quality 3D Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this issue, in this paper, we propose to learn flexibly distributional representation for low-quality 3D FR. |
Zihui Zhang; Cuican Yu; Shuang Xu; Huibin Li; |
| 1093 | IA-GM: A Deep Bidirectional Learning Method for Graph Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we overcome the above limitation under a deep bidirectional learning framework.Our method circulates the output of the GM optimization layer to fuse with the input for affinity learning. |
Kaixuan Zhao; Shikui Tu; Lei Xu; |
| 1094 | Distribution Adaptive INT8 Quantization for Training CNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel INT8 quantization training framework for convolutional neural network to address the above issues. |
Kang Zhao; Sida Huang; Pan Pan; Yinghan Li; Yingya Zhang; Zhenyu Gu; Yinghui Xu; |
| 1095 | Context-Guided Adaptive Network for Efficient Human Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a fast and accurate end-to-end HPE method, which is specifically designed to overcome the commonly encountered jitter box, defective box and ambiguous box problems of box-based methods, e.g. Mask R-CNN. |
Lei Zhao; Jun Wen; Pengfei Wang; Nenggan Zheng; |
| 1096 | EPointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. |
Sicheng Zhao; Yezhen Wang; Bo Li; Bichen Wu; Yang Gao; Pengfei Xu; Trevor Darrell; Kurt Keutzer; |
| 1097 | Robust Lightweight Facial Expression Recognition Network with Label Distribution Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an efficiently robust facial expression recognition (FER) network, named EfficientFace, which holds much fewer parameters but more robust to the FER in the wild. |
Zengqun Zhao; Qingshan Liu; Feng Zhou; |
| 1098 | Joint Color-irrelevant Consistency Learning and Identity-aware Modality Adaptation for Visible-infrared Cross Modality Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle above problems, we propose a novel approach for VI-ReID. |
Zhiwei Zhao; Bin Liu; Qi Chu; Yan Lu; Nenghai Yu; |
| 1099 | Robust Multi-Modality Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To avoid the illumination limitation in visible person re-identification (Re-ID) and the heterogeneous issue in cross-modality Re-ID, we propose to utilize complementary advantages of multiple modalities including visible (RGB), near infrared (NI) and thermal infrared (TI) ones for robust person Re-ID. |
Aihua Zheng; Zi Wang; Zihan Chen; Chenglong Li; Jin Tang; |
| 1100 | Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples. |
Kecheng Zheng; Cuiling Lan; Wenjun Zeng; Zhizheng Zhang; Zheng-Jun Zha; |
| 1101 | RESA: Recurrent Feature-Shift Aggregator for Lane Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel module named REcurrent Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary feature extraction with an ordinary CNN. |
Tu Zheng; Hao Fang; Yi Zhang; Wenjian Tang; Zheng Yang; Haifeng Liu; Deng Cai; |
| 1102 | CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we present a new single-stage detector named the Confident IoU-Aware Single-Stage object Detector (CIA-SSD). |
Wu Zheng; Weiliang Tang; Sijin Chen; Li Jiang; Chi-Wing Fu; |
| 1103 | Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. |
Benjia Zhou; Yunan Li; Jun Wan; |
| 1104 | Deep Semantic Dictionary Learning for Multi-label Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an innovative path towards the solution of the multi-label image classification which considers it as a dictionary learning task. |
Fengtao Zhou; Sheng Huang; Yun Xing; |
| 1105 | Model Uncertainty Guides Visual Object Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make several improvements aimed at tackling uncertainty and improving robustness in object tracking. |
Lijun Zhou; Antoine Ledent; Qintao Hu; Ting Liu; Jianlin Zhang; Marius Kloft; |
| 1106 | Optimizing Information Theory Based Bitwise Bottlenecks for Efficient Mixed-Precision Activation Quantization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the problem of lossy signal compression for wireless communication, this paper presents a Bitwise Bottleneck approach for quantizing and encoding neural network activations. |
Xichuan Zhou; Kui Liu; Cong Shi; Haijun Liu; Ji Liu; |
| 1107 | Inferring Camouflaged Objects By Texture-Aware Interactive Guidance Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the complementary relationship between texture labels and camouflaged object labels, we propose an interactive guidance framework named TINet, which focuses on finding the indefinable boundary and the texture difference by progressive interactive guidance. |
Jinchao Zhu; Xiaoyu Zhang; Shuo Zhang; Junnan Liu; |
| 1108 | Simple Is Not Easy: A Simple Strong Baseline for TextVQA and TextCaps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that a simple attention mechanism can do the same or even better job without any bells and whistles. |
Qi Zhu; Chenyu Gao; Peng Wang; Qi Wu; |
| 1109 | Fooling Thermal Infrared Pedestrian Detectors in Real World Using Small Bulbs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a physical attack method with small bulbs on a board against the state of-the-art pedestrian detectors. |
Xiaopei Zhu; Xiao Li; Jianmin Li; Zheyao Wang; Xiaolin Hu; |
| 1110 | ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for Robust Point Cloud Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an adaptive sampling and hierarchical folding network (ASHF-Net) for robust 3D point cloud completion. |
Daoming Zong; Shiliang Sun; Jing Zhao; |
| 1111 | New Length Dependent Algorithm for Maximum Satisfiability Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the computational complexity of the Maximum Satisfiability problem in terms of the length L of a given formula. |
Vasily Alferov; Ivan Bliznets; |
| 1112 | Online Search with Maximum Clearance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a multi-criteria search problem in which the searcher has a budget on its allotted search time, and the objective is to design strategies that are competitively efficient, respect the budget, and maximize the total searched ground. |
Spyros Angelopoulos; Malachi Voss; |
| 1113 | Counting Maximal Satisfiable Subsets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The primary contribution of this work is an affirmative answer to the above question. |
Jaroslav Bendík; Kuldeep S. Meel; |
| 1114 | Learning To Scale Mixed-Integer Programs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the use of machine learning to choose at the beginning of the solution process between two common scaling methods: Standard scaling and Curtis-Reid scaling. |
Timo Berthold; Gregor Hendel; |
| 1115 | A SAT-based Resolution of Lam’s Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we resolve Lam’s problem by translating the problem into Boolean logic and use satisfiability (SAT) solvers to produce nonexistence certificates that can be verified by a third party. |
Curtis Bright; Kevin K. H. Cheung; Brett Stevens; Ilias Kotsireas; Vijay Ganesh; |
| 1116 | Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems. |
Quentin Cappart; Thierry Moisan; Louis-Martin Rousseau; Isabeau Prémont-Schwarz; Andre A. Cire; |
| 1117 | Necessary and Sufficient Conditions for Avoiding Reopenings in Best First Suboptimal Search with General Bounding Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent work introduced XDP and XUP priority functions for best-first bounded-suboptimal search that do not need to perform state re-expansions as long as the search heuristic is consistent. |
Jingwei Chen; Nathan R. Sturtevant; |
| 1118 | A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new SSAT solver based on the framework of clause selection and cube distribution previously proposed for QBF solving. |
Pei-Wei Chen; Yu-Ching Huang; Jie-Hong R. Jiang; |
| 1119 | An Improved Upper Bound for SAT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that the CNF satisfiability problem can be solved O^*(1.2226^m) time, where m is the number of clauses in the formula, improving the known upper bounds O^*(1.234^m) given by Yamamoto 15 years ago and O^*(1.239^m) given by Hirsch 22 years ago. |
Huairui Chu; Mingyu Xiao; Zhe Zhang; |
| 1120 | Solving Infinite-Domain CSPs Using The Patchwork Property Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We improve this bound to f(w)n^(O(1)), where the function f only depends on the language Γ, for CSPs whose basic relations have the patchwork property. |
Konrad K Dabrowski; Peter Jonsson; Sebastian Ordyniak; George Osipov; |
| 1121 | Disjunctive Temporal Problems Under Structural Restrictions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that instances of DTP of any arity with integers bounded by poly(n) can be solved in n^{f(w)} time, where n denotes the problem size, w is the treewidth of the incidence graph and f is a computable function; in other words, this problem is in the complexity class XP and it can be solved in polynomial time whenever w is fixed. |
Konrad K Dabrowski; Peter Jonsson; Sebastian Ordyniak; George Osipov; |
| 1122 | Optimal Decision Trees for Nonlinear Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this gap, we propose a novel algorithm based on bi-objective optimisation, which treats misclassifications of each binary class as a separate objective. |
Emir Demirović; Peter J. Stuckey; |
| 1123 | Teaching The Old Dog New Tricks: Supervised Learning with Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. |
Fabrizio Detassis; Michele Lombardi; Michela Milano; |
| 1124 | Cutting to The Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we lift core-guided search to pseudo-Boolean (PB) solvers, which deal with more general PB optimization problems and operate natively with cardinality constraints. |
Jo Devriendt; Stephan Gocht; Emir Demirović; Jakob Nordström; Peter J. Stuckey; |
| 1125 | Optimising Automatic Calibration of Electric Muscle Stimulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new SAT-based black-box calibration method, which requires no spatial information about muscular or electrode positioning. |
Graeme Gange; Jarrod Knibbe; |
| 1126 | Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show how to instead use pseudo-Boolean inequalities with extension variables to concisely justify XOR reasoning. |
Stephan Gocht; Jakob Nordström; |
| 1127 | Finding Diverse Trees, Paths, and More Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Mathematical modeling is a standard approach to solve many real-world problems and diversity of solutions is an important issue, emerging in applying solutions obtained from mathematical models to real-world problems. |
Tesshu Hanaka; Yasuaki Kobayashi; Kazuhiro Kurita; Yota Otachi; |
| 1128 | Scalable Verification of Quantized Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that verifying the bit-exact implementation of quantized neural networks with bit-vector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP. |
Thomas A. Henzinger; Mathias Lechner; Đorđe Žikelić; |
| 1129 | Integrated Optimization of Bipartite Matching and Its Stochastic Behavior: New Formulation and Approximation Algorithm Via Min-cost Flow Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate an optimization problem for determining the values of the control variables so as to maximize the expected value of matching weights. |
Yuya Hikima; Yasunori Akagi; Hideaki Kim; Masahiro Kohjima; Takeshi Kurashima; Hiroyuki Toda; |
| 1130 | A Scalable Two Stage Approach to Computing Optimal Decision Sets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The approach makes use of modern maximum satisfiability and integer linear programming technologies. |
Alexey Ignatiev; Edward Lam; Peter J. Stuckey; Joao Marques-Silva; |
| 1131 | Smooth Convex Optimization Using Sub-Zeroth-Order Oracles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of minimizing a smooth, Lipschitz, convex function over a compact, convex set using sub-zeroth-order oracles: an oracle that outputs the sign of the directional derivative for a given point and a given direction, an oracle that compares the function values for a given pair of points, and an oracle that outputs a noisy function value for a given point. |
Mustafa O. Karabag; Cyrus Neary; Ufuk Topcu; |
| 1132 | Binary Matrix Factorisation Via Column Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the problem of low-rank binary matrix factorisation (BMF) under Boolean arithmetic. |
Reka A. Kovacs; Oktay Gunluk; Raphael A. Hauser; |
| 1133 | Backdoor Decomposable Monotone Circuits and Propagation Complete Encodings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a compilation language of backdoor decomposable monotone circuits (BDMCs) which generalizes several concepts appearing in the literature, e.g. DNNFs and backdoor trees. |
Petr Kučera; Petr Savický; |
| 1134 | On Continuous Local BDD-Based Search for Hybrid SAT Solving Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel algorithm for efficiently computing the gradient needed by CLS. |
Anastasios Kyrillidis; Moshe Vardi; Zhiwei Zhang; |
| 1135 | The Power of Literal Equivalence in Model Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Taking advantage of literal equivalences, this paper designs an efficient model counting technique such that its trace is a generalization of Decision-DNNF formula. |
Yong Lai; Kuldeep S. Meel; Roland H. C. Yap; |
| 1136 | Parallel Constraint Acquisition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose PACQ, a portfolio-based parallel constraint acquisition system. |
Nadjib Lazaar; |
| 1137 | Towards More Practical and Efficient Automatic Dominance Breaking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper proposes separate techniques to generalize and make more efficient the nogood generation phase of an automated dominance breaking framework by Lee and Zhong’s. |
Jimmy H.M. Lee; Allen Z. Zhong; |
| 1138 | Dependency Stochastic Boolean Satisfiability: A Logical Formalism for NEXPTIME Decision Problems with Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the theoretical foundations paved in this work, we hope to encourage the development of DSSAT solvers for potential broad applications. |
Nian-Ze Lee; Jie-Hong R. Jiang; |
| 1139 | Satisfiability and Algorithms for Non-uniform Random K-SAT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we tackle a somewhat nonstandard type of Random Satisfiability, the one where instances are not chosen uniformly from a certain class of instances, but rather from a certain nontrivial distribution. |
Oleksii Omelchenko; Andrei Bulatov; |
| 1140 | Turbocharging Treewidth-Bounded Bayesian Network Structure Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new approach for learning the structure of a treewidth-bounded Bayesian Network (BN). |
Vaidyanathan Peruvemba Ramaswamy; Stefan Szeider; |
| 1141 | SAT-based Decision Tree Learning for Large Data Sets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new hybrid approach to decision tree learning, combining heuristic and exact methods in a novel way. |
Andre Schidler; Stefan Szeider; |
| 1142 | LCollision: Fast Generation of Collision-Free Human Poses Using Learned Non-Penetration Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present LCollision, a learning-based method that synthesizes collision-free 3D human poses. |
Qingyang Tan; Zherong Pan; Dinesh Manocha; |
| 1143 | Symmetric Component Caching for Model Counting on Combinatorial Instances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the observation that these counters do not exploit the inherent symmetries exhibited in such instances, we revisit the component caching architecture employed in current counters and introduce a novel caching scheme that focuses on identifying symmetric components. |
Timothy van Bremen; Vincent Derkinderen; Shubham Sharma; Subhajit Roy; Kuldeep S. Meel; |
| 1144 | Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization. |
Giulia Zarpellon; Jason Jo; Andrea Lodi; Yoshua Bengio; |
| 1145 | Extreme K-Center Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop the first highly scalable approximation algorithm for k-center clustering, with O~(n^ε) space per machine and O~(n^(1+ε)) total work, for arbitrary small constant ε. |
MohammadHossein Bateni; Hossein Esfandiari; Manuela Fischer; Vahab Mirrokni; |
| 1146 | Beyond Low-frequency Information in Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We tackle this challenge and propose a novel Frequency Adaptation Graph Convolutional Networks (FAGCN) with a self-gating mechanism, which can adaptively integrate different signals in the process of message passing. |
Deyu Bo; Xiao Wang; Chuan Shi; Huawei Shen; |
| 1147 | Graph Heterogeneous Multi-Relational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new multi-relational recommendation model named Graph Heterogeneous Collaborative Filtering (GHCF). |
Chong Chen; Weizhi Ma; Min Zhang; Zhaowei Wang; Xiuqiang He; Chenyang Wang; Yiqun Liu; Shaoping Ma; |
| 1148 | Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose an Adaptive and Efficient ad creative Selection (AES) framework based on a tree structure. |
Jin Chen; Tiezheng Ge; Gangwei Jiang; Zhiqiang Zhang; Defu Lian; Kai Zheng; |
| 1149 | Revisiting Consistent Hashing with Bounded Loads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. |
John Chen; Benjamin Coleman; Anshumali Shrivastava; |
| 1150 | A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that the typical prediction framework that treats all users equally during the inference phase is inefficient in running time, as well as sub-optimal in accuracy. |
Lei Chen; Fajie Yuan; Jiaxi Yang; Xiang Ao; Chengming Li; Min Yang; |
| 1151 | Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new approach for the zero-shot text-to-SQL task which does not rely on any additional manual annotations. |
Yongrui Chen; Xinnan Guo; Chaojie Wang; Jian Qiu; Guilin Qi; Meng Wang; Huiying Li; |
| 1152 | Towards Faster Deep Collaborative Filtering Via Hierarchical Decision Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For personalized recommendations, collaborative filtering (CF) methods aim to recommend items to users based on data of historical user-item interactions. |
Yu Chen; Sinno Jialin Pan; |
| 1153 | Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A deep transfer tensor decomposition (DTTD) method is proposed by integrating deep structure and Tucker decomposition, where an orthogonal constrained stacked denoising autoencoder (OC-SDAE) is proposed for alleviating the scale variation in learning effective latent representation, and the side information is incorporated as a compensation for tensor sparsity. |
Zhengyu Chen; Ziqing Xu; Donglin Wang; |
| 1154 | PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of embedding uncertain knowledge graphs, where each relation between entities is associated with a confidence score. |
Zhu-Mu Chen; Mi-Yen Yeh; Tei-Wei Kuo; |
| 1155 | Graph Neural Network-Based Anomaly Detection in Multivariate Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? |
Ailin Deng; Bryan Hooi; |
| 1156 | A Hybrid Bandit Framework for Diversified Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this problem, we propose the Linear Modular Dispersion Bandit (LMDB) framework, which is an online learning setting for optimizing a combination of modular functions and dispersion functions. |
Qinxu Ding; Yong Liu; Chunyan Miao; Fei Cheng; Haihong Tang; |
| 1157 | Estimating The Number of Induced Subgraphs from Incomplete Data and Neighborhood Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: After specifying a random generator which removes edges from the underlying graph, we present estimators with strong provable performance guarantees, which exploit information from the noisy network samples and query a constant number of the most important vertices for the estimation. |
Dimitris Fotakis; Thanasis Pittas; Stratis Skoulakis; |
| 1158 | Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a statistical model for dynamically evolving networks, together with a variational inference approach. |
Tony Gracious; Shubham Gupta; Arun Kanthali; Rui M. Castro; Ambedkar Dukkipati; |
| 1159 | Exploiting Behavioral Consistence for Universal User Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on developing universal user representation model. |
Jie Gu; Feng Wang; Qinghui Sun; Zhiquan Ye; Xiaoxiao Xu; Jingmin Chen; Jun Zhang; |
| 1160 | NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Neural Attentional Cooperation-competition model (NeuralAC), which incorporates weighted-cooperation effects (i.e., intra-team interactions) and weighted-competition effects (i.e., inter-team interactions) for predicting match outcomes. |
Yin Gu; Qi Liu; Kai Zhang; Zhenya Huang; Runze Wu; Jianrong Tao; |
| 1161 | Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather prediction. |
Jindong Han; Hao Liu; Hengshu Zhu; Hui Xiong; Dejing Dou; |
| 1162 | GAN Ensemble for Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to construct GAN ensembles for anomaly detection. |
Xu Han; Xiaohui Chen; Li-Ping Liu; |
| 1163 | Complete Closed Time Intervals-Related Patterns Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce the TIRPClo algorithm – for complete and efficient mining of frequent closed TIRPs. |
Omer David Harel; Robert Moskovitch; |
| 1164 | Online Learning in Variable Feature Spaces Under Incomplete Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper explores a new online learning problem where the input sequence lives in an over-time varying feature space and the ground-truth label of any input point is given only occasionally, making online learners less restrictive and more applicable. |
Yi He; Xu Yuan; Sheng Chen; Xindong Wu; |
| 1165 | Knowledge-aware Coupled Graph Neural Network for Social Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. |
Chao Huang; Huance Xu; Yong Xu; Peng Dai; Lianghao Xia; Mengyin Lu; Liefeng Bo; Hao Xing; Xiaoping Lai; Yanfang Ye; |
| 1166 | Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-task learning framework with Multi-level Transition Dynamics (MTD), which enables the jointly learning of intra- and inter-session item transition dynamics in automatic and hierarchical manner. |
Chao Huang; Jiahui Chen; Lianghao Xia; Yong Xu; Peng Dai; Yanqing Chen; Liefeng Bo; Jiashu Zhao; Jimmy Xiangji Huang; |
| 1167 | Anomaly Attribution with Likelihood Compensation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formalize this task as a statistical inverse problem: Given model deviation from the expected value, infer the responsibility score of each of the input variables. |
Tsuyoshi Idé; Amit Dhurandhar; Jiří Navrátil; Moninder Singh; Naoki Abe; |
| 1168 | LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem while ensuring the detection performance, we present an unsupervised low-rank embedded network (LREN) in this paper. |
Kai Jiang; Weiying Xie; Jie Lei; Tao Jiang; Yunsong Li; |
| 1169 | On Estimating Recommendation Evaluation Metrics Under Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce some principled approach to derive the estimators of top-k metric based on sampling. |
Ruoming Jin; Dong Li; Benjamin Mudrak; Jing Gao; Zhi Liu; |
| 1170 | Randomized Generation of Adversary-aware Fake Knowledge Graphs to Combat Intellectual Property Theft Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to deter theft of intellectual property via cyber-attacks, we consider the following problem: given a KG K0 (e.g., representing a software or biomedical device design or the content of a technical document), can we automatically generate a set of KGs that are similar enough to K0 (so they are hard to discern as synthetic) but sufficiently different (so as to be wrong)? |
Snow Kang; Cristian Molinaro; Andrea Pugliese; V. S. Subrahmanian; |
| 1171 | PREMERE: Meta-Reweighting Via Self-Ensembling for Point-of-Interest Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper, we propose PREMERE, an adaptive weighting scheme based on meta-learning. |
Minseok Kim; Hwanjun Song; Doyoung Kim; Kijung Shin; Jae-Gil Lee; |
| 1172 | Disposable Linear Bandits for Online Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the classic stochastic linear bandit problem under the restriction that each arm may be selected for limited number of times. |
Melda Korkut; Andrew Li; |
| 1173 | Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model, hierarchical negative binomial factorization, which models data dispersion via a hierarchical Bayesian structure, thus alleviating the effect of data overdispersion to help with performance gain for recommendation. |
Li-Yen Kuo; Ming-Syan Chen; |
| 1174 | Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome those limitations, our paper proposes a novel Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. |
Mengzhang Li; Zhanxing Zhu; |
| 1175 | Rejection Sampling for Weighted Jaccard Similarity Revisited Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to improve RS by a strategy, which we call efficient rejection sampling (ERS), based on “early stopping + densification”. |
Xiaoyun Li; Ping Li; |
| 1176 | GraphMSE: Efficient Meta-path Selection in Semantically Aligned Feature Space for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these drawbacks, we propose GraphMSE, an efficient heterogeneous GCN combined with automatic meta-path selection. |
Yi Li; Yilun Jin; Guojie Song; Zihao Zhu; Chuan Shi; Yiming Wang; |
| 1177 | Cross-Oilfield Reservoir Classification Via Multi-Scale Sensor Knowledge Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To that end, in this paper, we present a focused study on the cross-oilfield reservoir classification task. |
Zhi Li; Zhefeng Wang; Zhicheng Wei; Xiangguang Zhou; Yijun Wang; Baoxing Huai; Qi Liu; Nicholas Jing Yuan; Renbin Gong; Enhong Chen; |
| 1178 | FedRec++: Lossless Federated Recommendation with Explicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel lossless federated recommendation method (FedRec++) by allocating some denoising clients (i.e., users) to eliminate the noise in a privacy-aware manner. |
Feng Liang; Weike Pan; Zhong Ming; |
| 1179 | HMS: A Hierarchical Solver with Dependency-Enhanced Understanding for Math Word Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a novel Hierarchical Math Solver (HMS) to make deep understanding and exploitation of problems. |
Xin Lin; Zhenya Huang; Hongke Zhao; Enhong Chen; Qi Liu; Hao Wang; Shijin Wang; |
| 1180 | Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a Context and Time aware Location Embedding (CTLE) model, which calculates a location’s representation vector with consideration of its specific contextual neighbors in trajectories. |
Yan Lin; Huaiyu Wan; Shengnan Guo; Youfang Lin; |
| 1181 | Noninvasive Self-attention for Side Information Fusion in Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we propose the NOn-inVasive self-Attention mechanism (NOVA) to leverage side information effectively under the BERT framework. |
Chang Liu; Xiaoguang Li; Guohao Cai; Zhenhua Dong; Hong Zhu; Lifeng Shang; |
| 1182 | Visual Pivoting for (Unsupervised) Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. |
Fangyu Liu; Muhao Chen; Dan Roth; Nigel Collier; |
| 1183 | Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with this, we propose a novel model called Relative and Absolute Location Embedding (RALE) hinged on the concept of hub nodes. |
Zemin Liu; Yuan Fang; Chenghao Liu; Steven C.H. Hoi; |
| 1184 | Learning to Pre-train Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct an analysis to show the divergence between pre-training and fine-tuning, and to alleviate such divergence, we propose L2P-GNN, a self-supervised pre-training strategy for GNNs. |
Yuanfu Lu; Xunqiang Jiang; Yuan Fang; Chuan Shi; |
| 1185 | Knowledge-Enhanced Top-K Recommendation in Poincaré Ball Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. |
Chen Ma; Liheng Ma; Yingxue Zhang; Haolun Wu; Xue Liu; Mark Coates; |
| 1186 | Communicative Message Passing for Inductive Relation Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce a Communicative Message Passing neural network for Inductive reLation rEasoning, CoMPILE, that reasons over local directed subgraph structures and has a vigorous inductive bias to process entity-independent semantic relations. |
Sijie Mai; Shuangjia Zheng; Yuedong Yang; Haifeng Hu; |
| 1187 | Learning Accurate and Interpretable Decision Rule Sets from Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new paradigm for learning a set of independent logical rules in disjunctive normal form as an interpretable model for classification. |
Litao Qiao; Weijia Wang; Bill Lin; |
| 1188 | Robust Spatio-Temporal Purchase Prediction Via Deep Meta Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose the Spatio-Temporal Meta-learning Prediction (STMP) model for purchase prediction during shopping festivals. |
Huiling Qin; Songyu Ke; Xiaodu Yang; Haoran Xu; Xianyuan Zhan; Yu Zheng; |
| 1189 | U-BERT: Pre-training User Representations for Improved Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the recent success of BERT in NLP, we propose a novel pre-training and fine-tuning based approach U-BERT. |
Zhaopeng Qiu; Xian Wu; Jingyue Gao; Wei Fan; |
| 1190 | DocParser: Hierarchical Document Structure Parsing from Renderings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our second contribution is to provide a dataset for evaluating hierarchical document structure parsing. |
Johannes Rausch; Octavio Martinez; Fabian Bissig; Ce Zhang; Stefan Feuerriegel; |
| 1191 | Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions. |
Siyu Ren; Kenny Q. Zhu; |
| 1192 | Group Testing on A Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formalize the group assembling problem as a graph partitioning problem, where the goal is to minimize the expected number of tests needed to screen the entire network. |
Arlei Silva; Ambuj Singh; |
| 1193 | Detecting Beneficial Feature Interactions for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. |
Yixin Su; Rui Zhang; Sarah Erfani; Zhenghua Xu; |
| 1194 | A Hybrid Probabilistic Approach for Table Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce models that identify cell types, group these cells into blocks of data that serve a similar functional role, and predict the relationships between these blocks. |
Kexuan Sun; Harsha Rayudu; Jay Pujara; |
| 1195 | Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. |
Li Sun; Zhongbao Zhang; Jiawei Zhang; Feiyang Wang; Hao Peng; Sen Su; Philip S. Yu; |
| 1196 | Dynamic Memory Based Attention Network for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge the gap, we propose a novel long sequential recommendation model, called Dynamic Memory-based Attention Network (DMAN). |
Qiaoyu Tan; Jianwei Zhang; Ninghao Liu; Xiao Huang; Hongxia Yang; Jingren Zhou; Xia Hu; |
| 1197 | GaussianPath:A Bayesian Multi-Hop Reasoning Framework for Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider a Bayesian reinforcement learning paradigm to harness uncertainty into multi-hop reasoning. |
Guojia Wan; Bo Du; |
| 1198 | GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model, named GSNet, to learn the spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting. |
Beibei Wang; Youfang Lin; Shengnan Guo; Huaiyu Wan; |
| 1199 | Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users’ characteristics in the human mobility modeling pipeline. |
Dongjie Wang; Pengyang Wang; Kunpeng Liu; Yuanchun Zhou; Charles E Hughes; Yanjie Fu; |
| 1200 | Coupling Macro-Sector-Micro Financial Indicators for Learning Stock Representations with Less Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, we propose a copula-based contrastive predictive coding (Co-CPC) method. |
Guifeng Wang; Longbing Cao; Hongke Zhao; Qi Liu; Enhong Chen; |
| 1201 | Reinforcement Learning with A Disentangled Universal Value Function for Item Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendation. |
Kai Wang; Zhene Zou; Qilin Deng; Jianrong Tao; Runze Wu; Changjie Fan; Liang Chen; Peng Cui; |
| 1202 | Learning to Recommend from Sparse Data Via Generative User Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback. |
Wenlin Wang; |
| 1203 | How Do We Move: Modeling Human Movement with System Dynamics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MoveSD to model state transition in human movement from a novel perspective, by learning the decision model and integrating the system dynamics. |
Hua Wei; Dongkuan Xu; Junjie Liang; Zhenhui (Jessie) Li; |
| 1204 | Learning to Truncate Ranked Lists for Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a global decision based truncation model named AttnCut, which directly optimizes user-defined objectives for the ranked list truncation. |
Chen Wu; Ruqing Zhang; Jiafeng Guo; Yixing Fan; Yanyan Lan; Xueqi Cheng; |
| 1205 | Fairness-aware News Recommendation with Decomposed Adversarial Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes. |
Chuhan Wu; Fangzhao Wu; Xiting Wang; Yongfeng Huang; Xing Xie; |
| 1206 | Hybrid-order Stochastic Block Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. |
Xunxun Wu; Chang-Dong Wang; Pengfei Jiao; |
| 1207 | Inductive Graph Neural Networks for Spatiotemporal Kriging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop an Inductive Graph Neural Network Kriging (IGNNK) model to recover data for unsampled sensors on a network/graph structure. |
Yuankai Wu; Dingyi Zhuang; Aurelie Labbe; Lijun Sun; |
| 1208 | Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, this work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT), to investigate multi-typed interactive patterns between users and items in recommender systems. |
Lianghao Xia; Chao Huang; Yong Xu; Peng Dai; Xiyue Zhang; Hongsheng Yang; Jian Pei; Liefeng Bo; |
| 1209 | AttnMove: History Enhanced Trajectory Recovery Via Attentional Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations at a fine-grained spatial-temporal resolution. |
Tong Xia; Yunhan Qi; Jie Feng; Fengli Xu; Funing Sun; Diansheng Guo; Yong Li; |
| 1210 | Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user … |
Xin Xia; Hongzhi Yin; Junliang Yu; Qinyong Wang; Lizhen Cui; Xiangliang Zhang; |
| 1211 | A General Offline Reinforcement Learning Framework for Interactive Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to perform offline learning more effectively, we propose five approaches to minimize the distribution mismatch between the logging policy and recommendation policy: support constraints, supervised regularization, policy constraints, dual constraints and reward extrapolation. |
Teng Xiao; Donglin Wang; |
| 1212 | Hierarchical Reinforcement Learning for Integrated Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Hierarchical reinforcement learning framework for integrated recommendation (HRL-Rec), which divides the integrated recommendation into two tasks to recommend channels and items sequentially. |
Ruobing Xie; Shaoliang Zhang; Rui Wang; Feng Xia; Leyu Lin; |
| 1213 | Out-of-Town Recommendation with Travel Intention Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. |
Haoran Xin; Xinjiang Lu; Tong Xu; Hao Liu; Jingjing Gu; Dejing Dou; Hui Xiong; |
| 1214 | Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel Graph neural network with a Role-constrained Conditional random field, namely GRC, to learn the representation of applicants and detect loan fraud based on the learned representation. |
Bingbing Xu; Huawei Shen; Bingjie Sun; Rong An; Qi Cao; Xueqi Cheng; |
| 1215 | Transformer-Style Relational Reasoning with Dynamic Memory Updating for Temporal Network Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present TRRN, a transformer-style relational reasoning network with dynamic memory updating, to deal with the above challenges. |
Dongkuan Xu; Junjie Liang; Wei Cheng; Hua Wei; Haifeng Chen; Xiang Zhang; |
| 1216 | A Unified Pretraining Framework for Passage Ranking and Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we propose a general pretraining framework to enhance both tasks with Unified Encoder-Decoder networks (UED). |
Ming Yan; Chenliang Li; Bin Bi; Wei Wang; Songfang Huang; |
| 1217 | Dynamic Knowledge Graph Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the task of dynamic knowledge graph alignment, the main challenge of which is how to efficiently update entity embeddings for the evolving graph topology. |
Yuchen Yan; Lihui Liu; Yikun Ban; Baoyu Jing; Hanghang Tong; |
| 1218 | Rethinking Graph Regularization for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide formal analyses to show that P-reg not only infuses extra information (that is not captured by the traditional graph Laplacian regularization) into GNNs, but also has the capacity equivalent to an infinite-depth graph convolutional network. |
Han Yang; Kaili Ma; James Cheng; |
| 1219 | Capturing Delayed Feedback in Conversion Rate Prediction Via Elapsed-Time Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. |
Jia-Qi Yang; Xiang Li; Shuguang Han; Tao Zhuang; De-Chuan Zhan; Xiaoyi Zeng; Bin Tong; |
| 1220 | Why Do Attributes Propagate in Graph Convolutional Neural Networks? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim at providing a novel explanation to the question of "Why do attributes propagate in GCNNs?” |
Liang Yang; Chuan Wang; Junhua Gu; Xiaochun Cao; Bingxin Niu; |
| 1221 | Relaxed Clustered Hawkes Process for Student Procrastination Modeling in MOOCs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the problem of detecting and predicting student procrastination in students Massive Open Online Courses (MOOCs) with missing and partially observed data, in this work, we propose a novel personalized Hawkes process model (RCHawkes-Gamma) that discovers meaningful student behavior clusters by jointly learning all partially observed processes simultaneously, without relying on auxiliary features. |
Mengfan Yao; Siqian Zhao; Shaghayegh Sahebi; Reza Feyzi Behnagh; |
| 1222 | Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters. |
Yuhang Yao; Carlee Joe-Wong; |
| 1223 | Coupled Layer-wise Graph Convolution for Transportation Demand Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To cope with the above problems, this paper provides a novel graph convolutional network for transportation demand prediction. |
Junchen Ye; Leilei Sun; Bowen Du; Yanjie Fu; Hui Xiong; |
| 1224 | Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we devise a deep graph-neighbor coherence preserving network (DGCPN). |
Jun Yu; Hao Zhou; Yibing Zhan; Dacheng Tao; |
| 1225 | Dual Sparse Attention Network For Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Dual Sparse Attention Network for the session-based recommendation called DSAN to address these shortcomings. |
Jiahao Yuan; Zihan Song; Mingyou Sun; Xiaoling Wang; Wayne Xin Zhao; |
| 1226 | Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a Self-Supervised Prototype Representation Learning (SePaL) framework for dynamic corporate profiling. |
Zixuan Yuan; Hao Liu; Renjun Hu; Denghui Zhang; Hui Xiong; |
| 1227 | AugSplicing: Synchronized Behavior Detection in Streaming Tensors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a fast streaming algorithm, AUGSPLICING, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. |
Jiabao Zhang; Shenghua Liu; Wenting Hou; Siddharth Bhatia; Huawei Shen; Wenjian Yu; Xueqi Cheng; |
| 1228 | Taxonomy Completion Via Triplet Matching Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate a new task, “taxonomy completion”, by discovering both the hypernym and hyponym concepts for a query. |
Jieyu Zhang; Xiangchen Song; Ying Zeng; Jiaze Chen; Jiaming Shen; Yuning Mao; Lei Li; |
| 1229 | Tripartite Collaborative Filtering with Observability and Selection for Debiasing Rating Estimation on Missing-Not-at-Random Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To debias MNAR rating estimation, we introduce item observability and user selection to depict the generation of MNAR ratings and propose a tripartite CF (TCF) framework to jointly model the triple aspects of rating generation: item observability, user selection, and ratings, and to estimate the MNAR ratings. |
Qi Zhang; Longbing Cao; Chongyang Shi; Liang Hu; |
| 1230 | Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We advocate a holistic understanding of KGs and we propose in this work a unified Generalized Relation Learning framework GRL to address the above two problems, which can be plugged into existing link prediction models. |
Yao Zhang; Xu Zhang; Jun Wang; Hongru Liang; Wenqiang Lei; Zhe Sun; Adam Jatowt; Zhenglu Yang; |
| 1231 | A Graph-based Relevance Matching Model for Ad-hoc Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel relevance matching model based on graph neural networks to leverage the document-level word relationships for ad-hoc retrieval. |
Yufeng Zhang; Jinghao Zhang; Zeyu Cui; Shu Wu; Liang Wang; |
| 1232 | Heterogeneous Graph Structure Learning for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In light of this, we make the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and propose a novel framework HGSL, which jointly performs Heterogeneous Graph Structure Learning and GNN parameters learning for classification task. |
Jianan Zhao; Xiao Wang; Chuan Shi; Binbin Hu; Guojie Song; Yanfang Ye; |
| 1233 | Cold-start Sequential Recommendation Via Meta Learner Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. |
Yujia Zheng; Siyi Liu; Zekun Li; Shu Wu; |
| 1234 | Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the question: can GNNs be applied to continuously learning a sequence of tasks? |
Fan Zhou; Chengtai Cao; |
| 1235 | Modeling Heterogeneous Relations Across Multiple Modes for Potential Crowd Flow Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a data-driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. |
Qiang Zhou; Jingjing Gu; Xinjiang Lu; Fuzhen Zhuang; Yanchao Zhao; Qiuhong Wang; Xiao Zhang; |
| 1236 | Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel time-aware copy-generation mechanism. |
Cunchao Zhu; Muhao Chen; Changjun Fan; Guangquan Cheng; Yan Zhang; |
| 1237 | Adversarial Directed Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. |
Shijie Zhu; Jianxin Li; Hao Peng; Senzhang Wang; Lifang He; |
| 1238 | Relation-Aware Neighborhood Matching Model for Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. |
Yao Zhu; Hongzhi Liu; Zhonghai Wu; Yingpeng Du; |
| 1239 | Argument Mining Driven Analysis of Peer-Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an Argument Mining based approach for the assistance of editors, meta-reviewers, and reviewers. |
Michael Fromm; Evgeniy Faerman; Max Berrendorf; Siddharth Bhargava; Ruoxia Qi; Yao Zhang; Lukas Dennert; Sophia Selle; Yang Mao; Thomas Seidl; |
| 1240 | Uncovering Latent Biases in Text: Method and Application to Peer Review Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a novel framework to quantify bias in text caused by the visibility of subgroup membership indicators. |
Emaad Manzoor; Nihar B. Shah; |
| 1241 | A Market-Inspired Bidding Scheme for Peer Review Paper Assignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. |
Reshef Meir; Jérôme Lang; Julien Lesca; Nicholas Mattei; Natan Kaminsky; |
| 1242 | A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider the problem of reviewer recruiting with a focus on the scarcity of qualified reviewers in large conferences. |
Ivan Stelmakh; Nihar B. Shah; Aarti Singh; Hal Daumé III; |
| 1243 | Catch Me If I Can: Detecting Strategic Behaviour in Peer Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate a statistical framework for this problem and design a principled test for detecting strategic behaviour. |
Ivan Stelmakh; Nihar B. Shah; Aarti Singh; |
| 1244 | Savable But Lost Lives When ICU Is Overloaded: A Model from 733 Patients in Epicenter Wuhan, China Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our analysis had shown that the MI-mortality is 41%, yet the mortality of ICU is 32%, implying that enough bed of ICU in treating patients in critical conditions. |
Tingting Dan; Yang Li; Ziwei Zhu; Xijie Chen; Wuxiu Quan; Yu Hu; Guihua Tao; Lei Zhu; Jijin Zhu; Hongmin Cai; Hanchun Wen; |
| 1245 | Persistence of Anti-vaccine Sentiment in Social Networks Through Strategic Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We use the framework of network coordination games to study the persistence of anti-vaccine sentiment in a population. |
A S M Ahsan-Ul Haque; Mugdha Thakur; Matthew Bielskas; Achla Marathe; Anil Vullikanti; |
| 1246 | Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. |
Xin He; Shihao Wang; Xiaowen Chu; Shaohuai Shi; Jiangping Tang; Xin Liu; Chenggang Yan; Jiyong Zhang; Guiguang Ding; |
| 1247 | STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. |
Nikos Kargas; Cheng Qian; Nicholas D. Sidiropoulos; Cao Xiao; Lucas M. Glass; Jimeng Sun; |
| 1248 | Transfer Graph Neural Networks for Pandemic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we utilize graph representation learning to capitalize on the underlying relationship of population movement with the spread of COVID-19. |
George Panagopoulos; Giannis Nikolentzos; Michalis Vazirgiannis; |
| 1249 | MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. |
Yu Qiu; Yun Liu; Shijie Li; Jing Xu; |
| 1250 | Steering A Historical Disease Forecasting Model Under A Pandemic: Case of Flu and COVID-19 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose CALI-Net, a neural transfer learning architecture which allows us to ‘steer’ a historical disease forecasting model to new scenarios where flu and COVID co-exist. |
Alexander Rodríguez; Nikhil Muralidhar; Bijaya Adhikari; Anika Tabassum; Naren Ramakrishnan; B. Aditya Prakash; |
| 1251 | Gaining Insight Into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a model that builds on Graph Attention Networks (GAT), creates edge features using self-supervised learning, and ingests these edge features via a Set Transformer. |
Arijit Sehanobish; Neal Ravindra; David van Dijk; |
| 1252 | Context Matters: Graph-based Self-supervised Representation Learning for Medical Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. |
Li Sun; Ke Yu; Kayhan Batmanghelich; |
| 1253 | Tracking Disease Outbreaks from Sparse Data with Bayesian Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Bayesian framework which accommodates partial observability in a principled manner. |
Bryan Wilder; Michael Mina; Milind Tambe; |
| 1254 | C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. |
Congxi Xiao; Jingbo Zhou; Jizhou Huang; An Zhuo; Ji Liu; Haoyi Xiong; Dejing Dou; |
| 1255 | Conversational Neuro-Symbolic Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a neuro-symbolic theorem prover that extracts multi-hop reasoning chains, and apply it to this problem. |
Forough Arabshahi; Jennifer Lee; Mikayla Gawarecki; Kathryn Mazaitis; Amos Azaria; Tom Mitchell; |
| 1256 | Interpretable Actions: Controlling Experts with Understandable Commands Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For such decomposable domains, we present a two-stage learning procedure producing combinations of the external bases which are trivially extractable from the network. |
Shumeet Baluja; David Marwood; Michele Covell; |
| 1257 | Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. |
Antoine Bosselut; Ronan Le Bras; Yejin Choi; |
| 1258 | Aligning Artificial Neural Networks and Ontologies Towards Explainable AI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this issue by leveraging on ontologies and building small classifiers that map a neural network model’s internal state to concepts from an ontology, enabling the generation of symbolic justifications for the output of neural network models. |
Manuel de Sousa Ribeiro; João Leite; |
| 1259 | Planning from Pixels in Atari with Learned Symbolic Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we leverage variational autoencoders (VAEs) to learn features directly from pixels in a principled manner, and without supervision. |
Andrea Dittadi; Frederik K. Drachmann; Thomas Bolander; |
| 1260 | Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For prediction of interacting agents’ trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making. |
Philipp Geiger; Christoph-Nikolas Straehle; |
| 1261 | Learning By Fixing: Solving Math Word Problems with Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this issue by introducing a weakly-supervised paradigm for learning MWPs. |
Yining Hong; Qing Li; Daniel Ciao; Siyuan Huang; Song-Chun Zhu; |
| 1262 | Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we address the more ambitious challenge of predicting the answers of conjunctive queries with multiple missing entities. |
Bhushan Kotnis; Carolin Lawrence; Mathias Niepert; |
| 1263 | Self-Supervised Self-Supervision By Combining Deep Learning and Probabilistic Logic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Self-Supervised Self-Supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. |
Hunter Lang; Hoifung Poon; |
| 1264 | Explaining Neural Matrix Factorization with Gradient Rollback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose gradient rollback, a general approach for influence estimation, applicable to neural models where each parameter update step during gradient descent touches a smaller number of parameters, even if the overall number of parameters is large. |
Carolin Lawrence; Timo Sztyler; Mathias Niepert; |
| 1265 | A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this, we propose a novel neural-symbolic stream reasoning approach realised by semantic stream reasoning programs which specify DNN-based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. |
Danh Le-Phuoc; Thomas Eiter; Anh Le-Tuan; |
| 1266 | Recognizing and Verifying Mathematical Equations Using Multiplicative Differential Neural Units Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To over-come this, we extend recursive-NNs to utilize multiplicative,higher-order synaptic connections and, furthermore, to learn to dynamically control and manipulate an external memory.We argue that this key modification gives the neural system the ability to capture powerful transition functions for each possible input. |
Ankur Mali; Alexander G. Ororbia; Daniel Kifer; C. Lee Giles; |
| 1267 | A Unified Framework for Planning with Learned Neural Network Transition Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on improving the effectiveness of solving the second stage of the approach by introducing (i) a novel Lagrangian RNN architecture that can model the previously ignored components of the planning problem as Lagrangian functions, and (ii) a novel framework that unifies the MILP and the Lagrangian RNN models such that the weakness of one model is complemented by the strength of the other. |
Buser Say; |
| 1268 | Classification By Attention: Scene Graph Classification with Prior Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, we take a multi-task learning approach by introducing schema representations and implementing the classification as an attention layer between image-based representations and the schemata. |
Sahand Sharifzadeh; Sina Moayed Baharlou; Volker Tresp; |
| 1269 | Differentiable Inductive Logic Programming for Structured Examples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore we propose a new framework to learn logic programs from noisy and structured examples, including the following contributions. |
Hikaru Shindo; Masaaki Nishino; Akihiro Yamamoto; |
| 1270 | Encoding Human Domain Knowledge to Warm Start Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel reinforcement learning technique that allows for intelligent initialization of a neural network weights and architecture. |
Andrew Silva; Matthew Gombolay; |
| 1271 | Neural-Symbolic Integration: A Compositional Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture, without making assumptions on their internal structure and semantics. |
Efthymia Tsamoura; Timothy Hospedales; Loizos Michael; |
| 1272 | Adaptive Teaching of Temporal Logic Formulas to Preference-based Learners Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an efficient approach for teaching parametric linear temporal logic formulas. |
Zhe Xu; Yuxin Chen; Ufuk Topcu; |
| 1273 | Double Oracle Algorithm for Computing Equilibria in Continuous Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this contribution, we develop an iterative strategy generation technique for finding a Nash equilibrium in a whole class of continuous two-person zero-sum games with compact strategy sets. |
Lukáš Adam; Rostislav Horčík; Tomáš Kasl; Tomáš Kroupa; |
| 1274 | A Few Queries Go A Long Way: Information-Distortion Tradeoffs in Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the one-sided matching problem, where n agents have preferences over n items, and these preferences are induced by underlying cardinal valuation functions. |
Georgios Amanatidis; Georgios Birmpas; Aris Filos-Ratsikas; Alexandros A. Voudouris; |
| 1275 | Representative Proxy Voting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study a model of proxy voting where the candidates, voters, and proxies are all located on the real line, and instead of voting directly, each voter delegates its vote to the closest proxy. |
Elliot Anshelevich; Zack Fitzsimmons; Rohit Vaish; Lirong Xia; |
| 1276 | Forming Better Stable Solutions in Group Formation Games Inspired By Internet Exchange Points (IXPs) Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study a coordination game motivated by the formation of Internet Exchange Points (IXPs), in which agents choose which facilities to join. |
Elliot Anshelevich; Wennan Zhu; |
| 1277 | Achieving Envy-freeness and Equitability with Monetary Transfers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a sufficient condition and an algorithm to achieve envy-freeness and equitability when monetary transfers are allowed. |
Haris Aziz; |
| 1278 | Proportionally Representative Participatory Budgeting with Ordinal Preferences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose proportional representation axioms and clarify how they fit into other preference aggregation settings, such as multi-winner voting and approval-based multi-winner voting. |
Haris Aziz; Barton E. Lee; |
| 1279 | Fair and Truthful Mechanisms for Dichotomous Valuations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of allocating a set on indivisible items to players with private preferences in an efficient and fair way. |
Moshe Babaioff; Tomer Ezra; Uriel Feige; |
| 1280 | Bayesian Persuasion Under Ex Ante and Ex Post Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple way to mathematically model such constraints as restrictions on Receiver’s admissible posterior beliefs. |
Yakov Babichenko; Inbal Talgam-Cohen; Konstantin Zabarnyi; |
| 1281 | Defending Against Contagious Attacks on A Network with Resource Reallocation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the network defending problem against contagious attacks. |
Rufan Bai; Haoxing Lin; Xinyu Yang; Xiaowei Wu; Minming Li; Weijia Jia; |
| 1282 | Achieving Proportionality Up to The Maximin Item with Indivisible Goods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the notion of proportionality up to the maximin item (PROPm) and show how to reach an allocation satisfying this notion for any instance involving up to five agents with additive valuations. |
Artem Baklanov; Pranav Garimidi; Vasilis Gkatzelis; Daniel Schoepflin; |
| 1283 | The Price of Connectivity in Fair Division Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the price of connectivity to capture the largest gap between the graph-specific and the unconstrained maximin share, and derive bounds on this quantity which are tight for large classes of graphs in the case of two agents and for paths and stars in the general case. |
Xiaohui Bei; Ayumi Igarashi; Xinhang Lu; Warut Suksompong; |
| 1284 | Dividing A Graphical Cake Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Work on the subject typically assumes that the cake is represented by an interval. |
Xiaohui Bei; Warut Suksompong; |
| 1285 | Maximin Fairness with Mixed Divisible and Indivisible Goods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: On the algorithmic front, we propose a constructive algorithm that will always produce an \alpha-MMS allocation for any number of agents, where \alpha takes values between 1/2 and 1 and is a monotonically increasing function determined by how agents value the divisible goods relative to their MMS values. |
Xiaohui Bei; Shengxin Liu; Xinhang Lu; Hongao Wang; |
| 1286 | Protecting The Protected Group: Circumventing Harmful Fairness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we initiate the discussion of the \emph{mismatch}, the unavoidable difference between the underlying utilities of the population and the utilities assumed by the regulator. |
Omer Ben-Porat; Fedor Sandomirskiy; Moshe Tennenholtz; |
| 1287 | Selfish Creation of Social Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose and analyze a network creation model inspired by real-world social networks. |
Davide Bilò; Tobias Friedrich; Pascal Lenzner; Stefanie Lowski; Anna Melnichenko; |
| 1288 | On The Complexity of Finding Justifications for Collective Decisions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide computational complexity results that address the problem of finding and verifying justifications for collective decisions. |
Arthur Boixel; Ronald de Haan; |
| 1289 | Preserving Condorcet Winners Under Strategic Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: On the bright side, we identify several indecisive but otherwise attractive tournament solutions that do guarantee the preservation of Condorcet winners under strategic manipulation for a large class of preference extensions. |
Sirin Botan; Ulle Endriss; |
| 1290 | Reaching Individually Stable Coalition Structures in Hedonic Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the convergence of simple dynamics leading to stable partitions in a variety of classes of hedonic games, including anonymous, dichotomous, fractional, and hedonic diversity games. |
Felix Brandt; Martin Bullinger; Anaëlle Wilczynski; |
| 1291 | Reinforcement Learning of Sequential Price Mechanisms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms. |
Gianluca Brero; Alon Eden; Matthias Gerstgrasser; David Parkes; Duncan Rheingans-Yoo; |
| 1292 | Margin of Victory in Tournaments: Structural and Experimental Results Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we reveal a number of structural insights on the MoV by investigating fundamental properties such as monotonicity and consistency with respect to the covering relation. |
Markus Brill; Ulrike Schmidt-Kraepelin; Warut Suksompong; |
| 1293 | Welfare Guarantees in Schelling Segregation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by a recent stream of work, we study welfare guarantees and complexity in this model with respect to several welfare measures. |
Martin Bullinger; Warut Suksompong; Alexandros A. Voudouris; |
| 1294 | Persuading Voters in District-based Elections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In doing so, we introduce a novel property, namely comparative stability, and we design a bi-criteria PTAS for public signaling in general Bayesian persuasion problems beyond elections when the sender’s utility function is state-dependent. |
Matteo Castiglioni; Nicola Gatti; |
| 1295 | Signaling in Bayesian Network Congestion Games: The Subtle Power of Symmetry Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper focuses on the problem of computing optimal ex ante persuasive signaling schemes, showing that symmetry is a crucial property for its solution. |
Matteo Castiglioni; Andrea Celli; Alberto Marchesi; Nicola Gatti; |
| 1296 | Computing Quantal Stackelberg Equilibrium in Extensive-Form Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study optimal strategies to commit to against subrational opponents in sequential games for the first time and make the following key contributions: (1) we prove the problem is NP-hard in general; (2) to enable further analysis, we introduce a non-fractional reformulation of the direct non-concave representation of the equilibrium; (3) we identify conditions under which the problem can be approximated in polynomial time in the size of the representation; (4) we show how an MILP can approximate the reformulation with a guaranteed bounded error, and (5) we experimentally demonstrate that our algorithm provides higher quality results several orders of magnitude faster than a baseline method for general non-linear optimization. |
Jakub Černý; Viliam Lisý; Branislav Bošanský; Bo An; |
| 1297 | Fair and Efficient Allocations Under Subadditive Valuations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a polynomial-time algorithm that outputs an allocation that satisfies either of the two approximations of EFX as well as achieves an O(n) approximation to the Nash welfare. |
Bhaskar Ray Chaudhury; Jugal Garg; Ruta Mehta; |
| 1298 | Scalable Equilibrium Computation in Multi-agent Influence Games on Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a polynomial-time, scalable algorithm for equilibrium computation in multi-agent influence games on networks, extending work of Bindel, Kleinberg, and Oren (2015) from the single-agent to the multi-agent setting. |
Fotini Christia; Michael Curry; Constantinos Daskalakis; Erik Demaine; John P. Dickerson; MohammadTaghi Hajiaghayi; Adam Hesterberg; Marina Knittel; Aidan Milliff; |
| 1299 | Proportional Representation Under Single-Crossing Preferences Revisited Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For the line, Skowron et al. (2015) describe an O(n^2mk) algorithm (where n, m, k are the number of voters, the number of candidates and the committee size, respectively); we show that a simple tweak improves the time complexity to O(nmk). |
Andrei Costin Constantinescu; Edith Elkind; |
| 1300 | Computational Analyses of The Electoral College: Campaigning Is Hard But Approximately Manageable Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider a more general setting of the two-player-multi-battleground game, in which multifaceted resources (troops) may have different contributions to different battlegrounds. |
Sina Dehghani; Hamed Saleh; Saeed Seddighin; Shang-Hua Teng; |
| 1301 | Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show how this situation can be naturally analyzed through the framework of coalitional game theory. |
Kate Donahue; Jon Kleinberg; |
| 1302 | On Fair Division Under Heterogeneous Matroid Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we make progress on this problem by providing positive and negative results for different matroid and valuation types. |
Amitay Dror; Michal Feldman; Erel Segal-Halevi; |
| 1303 | PoA of Simple Auctions with Interdependent Values Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The main technical difficulty in establishing this result is that the standard tool for establishing PoA results — the smoothness framework — is unsuitable for IDV settings, and so we must introduce new techniques to address the unique challenges imposed by such settings. |
Alon Eden; Michal Feldman; Inbal Talgam-Cohen; Ori Zviran; |
| 1304 | Mind The Gap: Cake Cutting With Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of fairly allocating a divisible resource, also known as cake cutting, with an additional requirement that the shares that different agents receive should be sufficiently separated from one another. |
Edith Elkind; Erel Segal-Halevi; Warut Suksompong; |
| 1305 | United for Change: Deliberative Coalition Formation to Change The Status Quo Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a deliberation process in which agents dynamically form coalitions around proposals that they prefer over the status quo. |
Edith Elkind; Davide Grossi; Ehud Shapiro; Nimrod Talmon; |
| 1306 | Incentivizing Truthfulness Through Audits in Strategic Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of optimal auditing of agents in such settings. |
Andrew Estornell; Sanmay Das; Yevgeniy Vorobeychik; |
| 1307 | Almost Envy-freeness, Envy-rank, and Nash Social Welfare Matchings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For our algorithm we introduce Nash Social Welfare Matching which makes a connection between Nash Social Welfare and envy freeness. |
Alireza Farhadi; MohammadTaghi Hajiaghayi; Mohamad Latifian; Masoud Seddighin; Hadi Yami; |
| 1308 | Faster Game Solving Via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce predictive Blackwell approachability, where an estimate of the next payoff vector is given, and the decision maker tries to achieve better performance based on the accuracy of that estimator. |
Gabriele Farina; Christian Kroer; Tuomas Sandholm; |
| 1309 | Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give the first algorithm for the bandit linear optimization problem for TFSDM that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O(sqrt(T)) cumulative regret in expectation compared to any fixed strategy, at all times T. |
Gabriele Farina; Robin Schmucker; Tuomas Sandholm; |
| 1310 | Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We give an efficient algorithm that achieves O(T^3/4) regret with high probability for that setting, even when the agent faces an adversarial environment. |
Gabriele Farina; Tuomas Sandholm; |
| 1311 | Simultaneous 2nd Price Item Auctions with No-Underbidding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To derive our results, we introduce a new parameterized property of auctions, termed (gamma,delta) revenue guaranteed, which implies a PoA of at least gamma/(1+delta). |
Michal Feldman; Galia Shabtai; |
| 1312 | Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the convergence of no-regret bidding algorithms in auctions. |
Zhe Feng; Guru Guruganesh; Christopher Liaw; Aranyak Mehta; Abhishek Sethi; |
| 1313 | Condorcet Relaxation In Spatial Voting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that 0.557≤ β*(ℝd,\|⋅\|2) for any dimension d (notice that 1/√d <0.557 for any d≥ 4). |
Arnold Filtser; Omrit Filtser; |
| 1314 | Present-Biased Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, the paper extends the origi- nal framework proposed by Akerlof (1991) for studying various aspects of human behavior related to time-inconsistent planning, including pro- crastination, and abandonment, as well as the elegant graph-theoretic model encapsulating this framework recently proposed by Kleinberg and Oren (2014). |
Fedor V. Fomin; Pierre Fraigniaud; Petr A. Golovach; |
| 1315 | Efficient Truthful Scheduling and Resource Allocation Through Monitoring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the power and limitations of the Vickrey-Clarke-Groves mechanism with monitoring (VCGmon) for cost minimization problems with objective functions that are more general than the social cost. |
Dimitris Fotakis; Piotr Krysta; Carmine Ventre; |
| 1316 | Infinite-Dimensional Fisher Markets: Equilibrium, Duality and Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual. |
Yuan Gao; Christian Kroer; |
| 1317 | Fair and Efficient Online Allocations with Normalized Valuations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to distribute these resources to maximize fairness and efficiency. |
Vasilis Gkatzelis; Alexandros Psomas; Xizhi Tan; |
| 1318 | An Analysis of Approval-Based Committee Rules for 2D-Euclidean Elections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider two main issues: First, we ask for the complexity of computing election results. |
Michał T. Godziszewski; Paweł Batko; Piotr Skowron; Piotr Faliszewski; |
| 1319 | Aggregating Binary Judgments Ranked By Accuracy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We revisit the fundamental problem of predicting a binary ground truth based on independent binary judgments provided by experts. |
Daniel Halpern; Gregory Kehne; Dominik Peters; Ariel D. Procaccia; Nisarg Shah; Piotr Skowron; |
| 1320 | District-Fair Participatory Budgeting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a notion of fairness that guarantees each district at least as much welfare as it would have received in a district-level election. |
D Ellis Hershkowitz; Anson Kahng; Dominik Peters; Ariel D. Procaccia; |
| 1321 | Fair and Efficient Allocations Under Lexicographic Preferences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the existence and computation of EFX in conjunction with various other economic properties under lexicographic preferences–a well-studied preference restriction model in artificial intelligence and economics. |
Hadi Hosseini; Sujoy Sikdar; Rohit Vaish; Lirong Xia; |
| 1322 | Necessarily Optimal One-Sided Matchings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead of asking the agents to report their complete preferences, our goal is to learn a desirable matching from partial preferences, specifically a matching that is necessarily Pareto optimal (NPO) or necessarily rank-maximal (NRM) under any completion of the partial preferences. |
Hadi Hosseini; Vijay Menon; Nisarg Shah; Sujoy Sikdar; |
| 1323 | Computing The Proportional Veto Core Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a polynomial time algorithm for computing the veto core and present a neutral and anonymous algorithm for selecting a candidate from it. |
Egor Ianovski; Aleksei Y. Kondratev; |
| 1324 | Multi-Scale Games: Representing and Solving Games on Networks with Group Structure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a general model of multi-scale network games that encodes such multi-level structure. |
Kun Jin; Yevgeniy Vorobeychik; Mingyan Liu; |
| 1325 | Multi-Party Campaigning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Following our general theorem, we design quite general algorithms; in particular, we describe how to design efficient algorithms for various settings, including settings in which we model diffusion of opinions in a social network, complex budgeting schemes available to the manipulating agents, and various realistic restrictions on adversary actions. |
Martin Koutecký; Nimrod Talmon; |
| 1326 | Classification with Strategically Withheld Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present IC-LR, a modification of Logistic Regression that removes the incentive to strategically drop features. |
Anilesh K. Krishnaswamy; Haoming Li; David Rein; Hanrui Zhang; Vincent Conitzer; |
| 1327 | On The PTAS for Maximin Shares in An Indivisible Mixed Manna Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we complement the hardness result by obtaining a PTAS to compute the MMS value when its absolute value is at least 1/p times either the total value of all the goods or total cost of all the chores, for some constant p valued at least 1. |
Rucha Kulkarni; Ruta Mehta; Setareh Taki; |
| 1328 | Evolution Strategies for Approximate Solution of Bayesian Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the problem of solving complex Bayesian games, characterized by high-dimensional type and action spaces, many (> 2) players, and general-sum payoffs. |
Zun Li; Michael P. Wellman; |
| 1329 | Safe Search for Stackelberg Equilibria in Extensive-Form Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a theoretically sound and empirically effective way to apply search, which leverages extra online computation to improve a solution, to the computation of Stackelberg equilibria in general-sum games. |
Chun Kai Ling; Noam Brown; |
| 1330 | Budget Feasible Mechanisms Over Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose efficient budget-feasible diffusion mechanisms for large markets that simultaneously guarantee individual rationality, budget-feasibility, strong budget-balance, incentive-compatibility to report private costs and diffuse auction information. |
Xiang Liu; Weiwei Wu; Minming Li; Wanyuan Wang; |
| 1331 | On The Approximation of Nash Equilibria in Sparse Win-Lose Multi-player Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we show that the problem of approximating a Nash equilibrium in a polymatrix game within the polynomial precision is PPAD-hard, even in sparse and win-lose ones. |
Zhengyang Liu; Jiawei Li; Xiaotie Deng; |
| 1332 | Trembling-Hand Perfection and Correlation in Sequential Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the extensive-form perfect correlated equilibrium (EFPCE) as a refinement of the classical extensive-form correlated equilibrium (EFCE) that amends its weaknesses off the equilibrium path. |
Alberto Marchesi; Nicola Gatti; |
| 1333 | Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games. |
David Milec; Jakub Černý; Viliam Lisý; Bo An; |
| 1334 | Hindsight and Sequential Rationality of Correlated Play Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a set of examples illustrating the distinct strengths and weaknesses of each type of equilibrium in the literature, and prove that no tractable concept subsumes all others. |
Dustin Morrill; Ryan D’Orazio; Reca Sarfati; Marc Lanctot; James R Wright; Amy R Greenwald; Michael Bowling; |
| 1335 | On Fair and Efficient Allocations of Indivisible Goods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a pseudo-polynomial time algorithm to compute an EF1+fPO allocation, thereby improving the earlier results. |
Aniket Murhekar; Jugal Garg; |
| 1336 | Coalition Formation in Multi-defender Security Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we approach the problem from the perspective of cooperative game theory and study coalition formation among the defenders. |
Dolev Mutzari; Jiarui Gan; Sarit Kraus; |
| 1337 | Majority Opinion Diffusion in Social Networks: An Adversarial Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce and study a novel majority based opinion diffusion model. |
Ahad N. Zehmakan; |
| 1338 | Fair and Efficient Allocations with Limited Demands Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose as an alternative the Least Cost Product (LCP) mechanism, a natural adaptation of Maximum Nash Welfare to this setting. |
Sushirdeep Narayana; Ian A. Kash; |
| 1339 | Scarce Societal Resource Allocation and The Price of (Local) Justice Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the allocation of scarce societal resources, where a central authority decides which individuals receive which resources under capacity or budget constraints. |
Quan Nguyen; Sanmay Das; Roman Garnett; |
| 1340 | From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we leverage equilibrium concepts from behavioral economics for this purpose and ask how well they perform compared to the quantal regret and Nash equilibrium methods. |
Gali Noti; |
| 1341 | Preference Elicitation As Average-Case Sorting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For these cases, we design elicitation algorithms that ask fewer questions in expectation, by building on results for average-case sorting. |
Dominik Peters; Ariel D. Procaccia; |
| 1342 | Market-Based Explanations of Collective Decisions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two new solution concepts, stable priceability and balanced stable priceability, and show that they select arguably fair committees. |
Dominik Peters; Grzegorz Pierczyński; Nisarg Shah; Piotr Skowron; |
| 1343 | A Permutation-Equivariant Neural Network Architecture For Auction Design Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider auction design problems that have permutation-equivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutation-equivariant optimal mechanism, which we show is not possible with the previous architecture. |
Jad Rahme; Samy Jelassi; Joan Bruna; S. Matthew Weinberg; |
| 1344 | Estimating Α-Rank By Maximizing Information Gain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on α-rank, a popular game-theoretic solution concept designed to perform well in such scenarios. |
Tabish Rashid; Cheng Zhang; Kamil Ciosek; |
| 1345 | Online Posted Pricing with Unknown Time-Discounted Valuations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of designing posted-price mechanisms in order to sell a single unit of a single item within a finite period of time. |
Giulia Romano; Gianluca Tartaglia; Alberto Marchesi; Nicola Gatti; |
| 1346 | The Maximin Support Method: An Extension of The D’Hondt Method to Approval-Based Multiwinner Elections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the maximin support method, a novel extension of the D’Hondt apportionment method to approval-based multiwinner elections. |
Luis Sánchez-Fernández; Norberto Fernández García; Jesús A. Fisteus; Markus Brill; |
| 1347 | Solution Concepts in Hierarchical Games Under Bounded Rationality With Applications to Autonomous Driving Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formalize the different solution concepts that can be applied in the context of hierarchical games, a framework used in multi-agent motion planning, for the purpose of creating game theoretic models of driving behavior. |
Atrisha Sarkar; Krzysztof Czarnecki; |
| 1348 | Modeling Voters in Multi-Winner Approval Voting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we examine voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty using behavioral data obtained from Mechanical Turk. |
Jaelle Scheuerman; Jason Harman; Nicholas Mattei; K. Brent Venable; |
| 1349 | Coupon Design in Advertising Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the coupon design problem for revenue maximization in the widely used VCG auction. |
Weiran Shen; Pingzhong Tang; Xun Wang; Yadong Xu; Xiwang Yang; |
| 1350 | Restricted Domains of Dichotomous Preferences with Possibly Incomplete Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We tackle the problem of determining whether an incomplete profile admits a completion within a certain restricted domain and design constructive, polynomial algorithms to that effect. |
Zoi Terzopoulou; Alexander Karpov; Svetlana Obraztsova; |
| 1351 | Facility’s Perspective to Fair Facility Location Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we are interested in fair allocation among facility managers and consider the well-studied proportionality and envy-freeness fairness notions and their relaxations. |
Chenhao Wang; Xiaoying Wu; Minming Li; Hau Chan; |
| 1352 | The Smoothed Complexity of Computing Kemeny and Slater Rankings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop the first smoothed complexity results for winner determination in voting. |
Lirong Xia; Weiqiang Zheng; |
| 1353 | If You Like Shapley Then You’ll Love The Core Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to challenge the machine learning community’s current consensus around the Shapley value, and make a case for the core as a viable alternative. |
Tom Yan; Ariel D. Procaccia; |
| 1354 | A Model of Winners Allocation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: We propose a model of winners allocation. In this model, we are given are two elections where the sets of candidates may intersect. The goal is to find two disjoint winning … |
Yongjie Yang; |
| 1355 | Targeted Negative Campaigning: Complexity and Approximations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present a model where elections can be manipulated by convincing voters to demote specific non-favored candidates, and study its properties in the classic setting of scoring rules. |
Avishai Zagoury; Orgad Keller; Avinatan Hassidim; Noam Hazon; |
| 1356 | Finding and Certifying (Near-)Optimal Strategies in Black-Box Extensive-Form Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we relax both of the assumptions. |
Brian Hu Zhang; Tuomas Sandholm; |
| 1357 | Automated Mechanism Design for Classification with Partial Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of automated mechanism design with partial verification, where each type can (mis)report only a restricted set of types (rather than any other type), induced by the principal’s limited verification power. |
Hanrui Zhang; Yu Cheng; Vincent Conitzer; |
| 1358 | Incentive-Aware PAC Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, we propose an incentive-aware version of the ERM principle which has asymptotically optimal sample complexity. |
Hanrui Zhang; Vincent Conitzer; |
| 1359 | Classification with Few Tests Through Self-Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study test-based binary classification, where a principal either accepts or rejects agents based on the outcomes they get in a set of tests. |
Hanrui Zhang; Yu Cheng; Vincent Conitzer; |
| 1360 | Computing Ex Ante Coordinated Team-Maxmin Equilibria in Zero-Sum Multiplayer Extensive-Form Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To compute a TMECor in larger games, we make the following key contributions: (1) we propose a hybrid-form strategy representation for the team, which preserves the set of equilibria; (2) we introduce a column-generation algorithm with a guaranteed finite-time convergence in the infinite strategy space based on a novel best-response oracle; (3) we develop an associated-representation technique for the exact representation of the multilinear terms in the best-response oracle; and (4) we experimentally show that our algorithm is several orders of magnitude faster than prior state-of-the-art algorithms in large games. |
Youzhi Zhang; Bo An; Jakub Černý; |
| 1361 | Power in Liquid Democracy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper develops a theory of power for delegable proxy voting systems. |
Yuzhe Zhang; Davide Grossi; |
| 1362 | Learning from Crowds By Modeling Common Confusions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we provide a new perspective to decompose annotation noise into common noise and individual noise and differentiate the source of confusion based on instance difficulty and annotator expertise on a per-instance-annotator basis. |
Zhendong Chu; Jing Ma; Hongning Wang; |
| 1363 | Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To detect the normal/transferable utterances, we propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing difficulty-assisted encoding to enhance the representations of utterances. |
Jiawei Liu; Zhe Gao; Yangyang Kang; Zhuoren Jiang; Guoxiu He; Changlong Sun; Xiaozhong Liu; Wei Lu; |
| 1364 | Teaching Active Human Learners Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a model of active learners and design an efficient teaching algorithm accordingly. |
Zizhe Wang; Hailong Sun; |
| 1365 | Automated Storytelling Via Causal, Commonsense Plot Ordering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning. |
Prithviraj Ammanabrolu; Wesley Cheung; William Broniec; Mark O. Riedl; |
| 1366 | MARTA: Leveraging Human Rationales for Explainable Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce MARTA, a Bayesian framework that jointly learns an attention-based model and the reliability of workers while injecting human rationales into model training. |
Ines Arous; Ljiljana Dolamic; Jie Yang; Akansha Bhardwaj; Giuseppe Cuccu; Philippe Cudré-Mauroux; |
| 1367 | Human Uncertainty Inference Via Deterministic Ensemble Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new model for human uncertainty inference, called proxy ensemble network (PEN). |
Yujin Cha; Sang Wan Lee; |
| 1368 | Learning to Sit: Synthesizing Human-Chair Interactions Via Hierarchical Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. |
Yu-Wei Chao; Jimei Yang; Weifeng Chen; Jia Deng; |
| 1369 | User Driven Model Adjustment Via Boolean Rule Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a solution which leverages the predictive power of ML models while allowing the user to specify modifications to decision boundaries. |
Elizabeth M. Daly; Massimiliano Mattetti; Öznur Alkan; Rahul Nair; |
| 1370 | Classification Under Human Assistance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this empirical observation, our goal is to design classifiers that are optimized to operate under different automation levels. |
Abir De; Nastaran Okati; Ali Zarezade; Manuel Gomez Rodriguez; |
| 1371 | Wasserstein Distributionally Robust Inverse Multiobjective Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To hedge against the uncertainties in the hypothetical DMP, the data, and the parameter space, we investigate in this paper the distributionally robust approach for inverse multiobjective optimization. |
Chaosheng Dong; Bo Zeng; |
| 1372 | Illuminating Mario Scenes in The Latent Space of A Generative Adversarial Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose using state-of-the-art quality diversity algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to efficiently explore the latent space of a GAN to extract levels that vary across a set of specified gameplay measures. |
Matthew C. Fontaine; Ruilin Liu; Ahmed Khalifa; Jignesh Modi; Julian Togelius; Amy K. Hoover; Stefanos Nikolaidis; |
| 1373 | ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the success of pre-training based approaches in NLP for tackling a variety of problems in a data-efficient way, we introduce a new pre-trained UI representation model called ActionBert. |
Zecheng He; Srinivas Sunkara; Xiaoxue Zang; Ying Xu; Lijuan Liu; Nevan Wichers; Gabriel Schubiner; Ruby Lee; Jindong Chen; |
| 1374 | Goal Blending for Responsive Shared Autonomy in A Navigating Vehicle Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel goal-blending shared autonomy (GBSA) system, which aims to improve responsiveness in shared autonomy systems by blending human and robot input during the selection of local navigation goals as opposed to low-level motor (servo-level) commands. |
Yu-Sian Jiang; Garrett Warnell; Peter Stone; |
| 1375 | Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new adversarial learning for FER. |
Daeha Kim; Byung Cheol Song; |
| 1376 | AI-Assisted Scientific Data Collection with Iterative Human Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a new framework to allow AI systems to work together with humans (e.g. scientists) to collect data more effectively in simple scientific domains. |
Travis Mandel; James Boyd; Sebastian J. Carter; Randall H. Tanaka; Taishi Nammoto; |
| 1377 | Improving The Performance-Compatibility Tradeoff with Personalized Objective Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present several use cases that illustrate the difference between the personalized and non-personalized approach for two of our domains. |
Jonathan Martinez; Kobi Gal; Ece Kamar; Levi H. S. Lelis; |
| 1378 | Indecision Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Techniques for preference modeling and social choice help researchers learn and aggregate peoples’ preferences, which are used to guide AI behavior; thus, it is imperative that these learned preferences are accurate. |
Duncan C. McElfresh; Lok Chan; Kenzie Doyle; Walter Sinnott-Armstrong; Vincent Conitzer; Jana Schaich Borg; John P. Dickerson; |
| 1379 | Narrative Plan Generation with Self-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the feasibility of narrative generation through self-supervised learning, using sequence embedding techniques or auto-encoders to produce narrative sequences. |
Mihai Polceanu; Julie Porteous; Alan Lindsay; Marc Cavazza; |
| 1380 | Uncertain Graph Neural Networks for Facial Action Unit Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Rather than employing a deterministic mode, we propose an uncertain graph neural network (UGN) to learn the probabilistic mask that simultaneously captures both the individual dependencies among AUs and the uncertainties. |
Tengfei Song; Lisha Chen; Wenming Zheng; Qiang Ji; |
| 1381 | Learning Rewards From Linguistic Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a general framework which does not make this assumption, instead using aspect-based sentiment analysis to decompose feedback into sentiment over the features of a Markov decision process. |
Theodore R. Sumers; Mark K. Ho; Robert D. Hawkins; Karthik Narasimhan; Thomas L. Griffiths; |
| 1382 | Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Drawing on iterative reasoning models and cumulative prospect theory, we propose a new game-theoretic framework, bounded risk-sensitive Markov Game (BRSMG), that captures two aspects of realistic human behaviors: bounded intelligence and risk-sensitivity. |
Ran Tian; Liting Sun; Masayoshi Tomizuka; |
| 1383 | Content Learning with Structure-Aware Writing: A Graph-Infused Dual Conditional Variational Autoencoder for Automatic Storytelling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a graph-infused dual conditional variational autoencoder model to capture multi-level intra-story structures (i.e., graph) by continuous variational latent variables and generate consistent stories through dual-infusion of story structure planning and content learning. |
Meng-Hsuan Yu; Juntao Li; Zhangming Chan; Rui Yan; Dongyan Zhao; |
| 1384 | A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate interactive image segmentation as a continual learning problem and propose a framework to effectively learn from user annotations, aiming to improve the segmentation on both the current image and unseen images in future tasks while avoiding deteriorated performance on previously-seen images. |
Ervine Zheng; Qi Yu; Rui Li; Pengcheng Shi; Anne Haake; |
| 1385 | Inferring Emotion from Large-scale Internet Voice Data: A Semi-supervised Curriculum Augmentation Based Deep Learning Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a novel semi-supervised multi-modal curriculum augmentation deep learning framework. |
Suping Zhou; Jia Jia; Zhiyong Wu; Zhihan Yang; Yanfeng Wang; Wei Chen; Fanbo Meng; Shuo Huang; Jialie Shen; Xiaochuan Wang; |
| 1386 | Automatic Generation of Flexible Plans Via Diverse Temporal Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this challenge by describing a technique for automatically synthesizing a TPN which covers multiple possible executions for a given temporal planning problem specified in PDDL 2.1. |
Yotam Amitai; Ayal Taitler; Erez Karpas; |
| 1387 | BT Expansion: A Sound and Complete Algorithm for Behavior Planning of Intelligent Robots with Behavior Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes BT expansion, an automated planning approach to building intelligent robot behaviors with BTs, and proves the soundness and completeness through the state-space formulation of BTs. |
Zhongxuan Cai; Minglong Li; Wanrong Huang; Wenjing Yang; |
| 1388 | I3DOL: Incremental 3D Object Learning Without Catastrophic Forgetting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a new Incremental 3D Object Learning (i.e., I3DOL) model, which is the first exploration to learn new classes of 3D object continually. |
Jiahua Dong; Yang Cong; Gan Sun; Bingtao Ma; Lichen Wang; |
| 1389 | Enabling Fast Instruction-Based Modification of Learned Robot Skills Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a skill modification framework is introduced that allows users to modify a robot’s stored skills quickly through instructions to (1) reduce inefficiencies, (2) fix errors, and (3) enable generalizations, all in a way for modified skills to be immediately available for task performance. |
Tyler Frasca; Bradley Oosterveld; Meia Chita-Tegmark; Matthias Scheutz; |
| 1390 | Consistent Right-Invariant Fixed-Lag Smoother with Application to Visual Inertial SLAM Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To ensure the consistency of a FLS, this paper introduces the right invariant error formulation into the FLS framework. |
Jianzhu Huai; Yukai Lin; Yuan Zhuang; Min Shi; |
| 1391 | Supervised Training of Dense Object Nets Using Optimal Descriptors for Industrial Robotic Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs. |
Andras Gabor Kupcsik; Markus Spies; Alexander Klein; Marco Todescato; Nicolai Waniek; Philipp Schillinger; Mathias Bürger; |
| 1392 | DenserNet: Weakly Supervised Visual Localization Using Multi-Scale Feature Aggregation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a Denser Feature Network(DenserNet) for visual localization. |
Dongfang Liu; Yiming Cui; Liqi Yan; Christos Mousas; Baijian Yang; Yingjie Chen; |
| 1393 | Learning Intuitive Physics with Multimodal Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. |
Sahand Rezaei-Shoshtari; Francois R. Hogan; Michael Jenkin; David Meger; Gregory Dudek; |
| 1394 | SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose SCAN, a Spatial Context Attentive Network that can jointly predict socially-acceptable multiple future trajectories for all pedestrians in a scene. |
Jasmine Sekhon; Cody Fleming; |
| 1395 | IDOL: Inertial Deep Orientation-Estimation and Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem of inaccurate orientation estimates, we present a two-stage, data-driven pipeline using a commodity smartphone that first estimates device orientations and then estimates device position. |
Scott Sun; Dennis Melamed; Kris Kitani; |
| 1396 | Differentiable Fluids with Solid Coupling for Learning and Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an efficient differentiable fluid simulator that can be integrated with deep neural networks as a part of layers for learning dynamics and solving control problems. |
Tetsuya Takahashi; Junbang Liang; Yi-Ling Qiao; Ming C. Lin; |
| 1397 | CMAX++ : Leveraging Experience in Planning and Execution Using Inaccurate Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task. |
Anirudh Vemula; J. Andrew Bagnell; Maxim Likhachev; |
| 1398 | Generative Partial Visual-Tactile Fused Object Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the mentioned challenges, we propose a Generative Partial Visual-Tactile Fused (i.e., GPVTF) framework for object clustering. |
Tao Zhang; Yang Cong; Gan Sun; Jiahua Dong; Yuyang Liu; Zhengming Ding; |
| 1399 | VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose an end-to-end framework, termed VMLoc, to fuse different sensor inputs into a common latent space through a variational Product-of-Experts (PoE) followed by attention-based fusion. |
Kaichen Zhou; Changhao Chen; Bing Wang; Muhamad Risqi U. Saputra; Niki Trigoni; Andrew Markham; |
| 1400 | Argumentation Frameworks with Strong and Weak Constraints: Semantics and Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, after providing an intuitive semantics based on Lukasiewicz’s logic for AFs with (strong) constraints, called Constrained AFs (CAFs), we propose Weak constrained AFs (WAFs) that enhance CAFs with weak constraints. |
Gianvincenzo Alfano; Sergio Greco; Francesco Parisi; Irina Trubitsyna; |
| 1401 | A General Setting for Gradual Semantics Dealing with Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose intuitive constraints for the three functions and key rationality principles for semantics, and show how the former lead to the satisfaction of the latter. |
Leila Amgoud; Victor David; |
| 1402 | Living Without Beth and Craig: Definitions and Interpolants in Description Logics with Nominals and Role Inclusions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article we show the following: even without Craig and Beth, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as ALCO, ALCH and ALCHIO. |
Alessandro Artale; Jean Christoph Jung; Andrea Mazzullo; Ana Ozaki; Frank Wolter; |
| 1403 | Equivalent Causal Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. |
Sander Beckers; |
| 1404 | The Counterfactual NESS Definition of Causation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper contributes to that analysis in two ways. |
Sander Beckers; |
| 1405 | Network Satisfaction for Symmetric Relation Algebras with A Flexible Atom Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a complete classification for the case that A is symmetric and has a flexible atom; the problem is in this case NP-complete or in P. |
Manuel Bodirsky; Simon Knäuer; |
| 1406 | Conditional Inference Under Disjunctive Rationality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a solution to this question and explore some of its properties. |
Richard Booth; Ivan Varzinczak; |
| 1407 | Algebra of Modular Systems: Containment and Equivalence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the problem of checking modular system containment, which we relate to a homomorphism problem. |
Andrei Bulatov; Eugenia Ternovska; |
| 1408 | Certifying Top-Down Decision-DNNF Compilers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, the focus is laid on a general family of top-down Decision-DNNF compilers. |
Florent Capelli; Jean-Marie Lagniez; Pierre Marquis; |
| 1409 | Contextual Conditional Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce the language for the enriched logic, and define an appropriate semantic framework for it. |
Giovanni Casini; Thomas Meyer; Ivan Varzinczak; |
| 1410 | Preferred Explanations for Ontology-Mediated Queries Under Existential Rules Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a detailed complexity analysis for all the aforementioned problems, thereby providing a more complete picture for explaining query answers under existential rules. |
İsmail İlkan Ceylan; Thomas Lukasiewicz; Enrico Malizia; Cristian Molinaro; Andrius Vaicenavičius; |
| 1411 | Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. |
Jiajun Chen; Huarui He; Feng Wu; Jie Wang; |
| 1412 | A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce TRAIL, a system that applies deep reinforcement learning to saturation-based theorem proving. |
Maxwell Crouse; Ibrahim Abdelaziz; Bassem Makni; Spencer Whitehead; Cristina Cornelio; Pavan Kapanipathi; Kavitha Srinivas; Veronika Thost; Michael Witbrock; Achille Fokoue; |
| 1413 | Recursion in Abstract Argumentation Is Hard — On The Complexity of Semantics Based on Weak Admissibility Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the computational complexity of abstract argumentation semantics based on weak admissibility, a recently introduced concept to deal with arguments of self-defeating nature. |
Wolfgang Dvořák; Markus Ulbricht; Stefan Woltran; |
| 1414 | The Complexity Landscape of Claim-Augmented Argumentation Frameworks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The inherent difference of these approaches not only potentially results in different outcomes but, as we will show in this paper, is also mirrored in terms of computational complexity. |
Wolfgang Dvořák; Alexander Greßler; Anna Rapberger; Stefan Woltran; |
| 1415 | On The Complexity of Sum-of-Products Problems Over Semirings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We characterize the latter by NP(R), a novel generalization of NP over semiring R, and link it to well-known complexity classes. |
Thomas Eiter; Rafael Kiesel; |
| 1416 | Treewidth-Aware Complexity in ASP: Not All Positive Cycles Are Equally Hard Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we refine this recent result and show that consistency for ASP can be decided in exponential time in k · log(ι) where ι is a novel measure, bounded by both treewidth k and the size of the largest strongly-connected component of the positive dependency graph of the program. |
Jorge Fandinno; Markus Hecher; |
| 1417 | SMT-based Safety Checking of Parameterized Multi-Agent Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of verifying whether a given parameterized multi-agent system (PMAS) is safe, namely whether none of its possible executions can lead to bad states. |
Paolo Felli; Alessandro Gianola; Marco Montali; |
| 1418 | A Simple Framework for Cognitive Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel approach to cognitive planning, i.e., an agent’s planning aimed at changing the cognitive attitudes of another agent including her beliefs and intentions. |
Jorge Luis Fernandez Davila; Dominique Longin; Emiliano Lorini; Frédéric Maris; |
| 1419 | Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we extend an approach for answering RPQs over graph databases that uses weighted transducers to approximate paths from the query in two ways. |
Oliver Fernández Gil; Anni-Yasmin Turhan; |
| 1420 | Knowledge-Base Degrees of Inconsistency: Complexity and Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a complexity analysis of fixed-domain non-entailment (NE) on Datalog programs for well-established families of knowledge bases (KBs). |
Johannes K. Fichte; Markus Hecher; Arne Meier; |
| 1421 | Constraint Logic Programming for Real-World Test Laboratory Scheduling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show how TLSP-S can be solved by Answer-set Programming extended with ideas from other constraint solving paradigms. |
Tobias Geibinger; Florian Mischek; Nysret Musliu; |
| 1422 | Mining EL Bases with Adaptable Role Depth Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: In Formal Concept Analysis, a base for a finite structure is a set of implications that characterizes all valid implications of the structure. This notion can be adapted to the … |
Ricardo Guimarães; Ana Ozaki; Cosimo Persia; Baris Sertkaya; |
| 1423 | REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a recursive erasure memory network (REM-Net) to cope with the quality improvement of evidence. |
Yinya Huang; Meng Fang; Xunlin Zhan; Qingxing Cao; Xiaodan Liang; |
| 1424 | (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we posit that manually constructed CSKGs will never achieve the coverage necessary to be applicable in all situations encountered by NLP agents. |
Jena D. Hwang; Chandra Bhagavatula; Ronan Le Bras; Jeff Da; Keisuke Sakaguchi; Antoine Bosselut; Yejin Choi; |
| 1425 | Commonsense Knowledge Augmentation for Low-Resource Languages Via Adversarial Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Commonsense reasoning is one of the ultimate goals of artificial intelligence research because it simulates the human thinking process. |
Bosung Kim; Juae Kim; Youngjoong Ko; Jungyun Seo; |
| 1426 | Parameterized Logical Theories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a translational semantics for these parameterized theories in first-order logic using the situation calculus. |
Fangzhen Lin; |
| 1427 | Learning Term Embeddings for Lexical Taxonomies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method for lexical taxonomy embedding. |
Jingping Liu; Menghui Wang; Chao Wang; Jiaqing Liang; Lihan Chen; Haiyun Jiang; Yanghua Xiao; Yunwen Chen; |
| 1428 | KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. |
Ye Liu; Yao Wan; Lifang He; Hao Peng; Philip S. Yu; |
| 1429 | Parameterized Complexity of Logic-Based Argumentation in Schaefer’s Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the propositional variants of the following three computational tasks studied in argumentation: ARG (exists a support for a given claim with respect to a given set of formulas), ARG-Check (is a given set a support for a given claim), and ARG-Rel (similarly as ARG plus requiring an additionally given formula to be contained in the support). |
Yasir Mahmood; Arne Meier; Johannes Schmidt; |
| 1430 | Ranking Sets of Defeasible Elements in Preferential Approaches to Structured Argumentation: Postulates, Relations, and Characterizations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Extending existing works from argumentation and social choice, we propose a list of postulates for lifting operations, and give a complete picture of (non-)satisfaction for the considered operators. |
Jan Maly; Johannes P. Wallner; |
| 1431 | GENSYNTH: Synthesizing Datalog Programs Without Language Bias Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a technique to learn Datalog programs from input-output examples without requiring the user to specify any language bias. |
Jonathan Mendelson; Aaditya Naik; Mukund Raghothaman; Mayur Naik; |
| 1432 | Parameterized Complexity of Small Decision Tree Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the NP-hard problem of learning a decision tree (DT) of smallest depth or size from data. |
Sebastian Ordyniak; Stefan Szeider; |
| 1433 | Interpreting Neural Networks As Quantitative Argumentation Frameworks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that an interesting class of feed-forward neural networks can be understood as quantitative argumentation frameworks. |
Nico Potyka; |
| 1434 | ChronoR: Rotation Based Temporal Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the challenging problem of inference over temporal knowledge graphs. |
Ali Sadeghian; Mohammadreza Armandpour; Anthony Colas; Daisy Zhe Wang; |
| 1435 | Quantification of Resource Production Incompleteness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a logic-based framework for measuring resource production incompleteness: the greater the value returned by a measure, the greater is the intensity of incompleteness. |
Yakoub Salhi; |
| 1436 | Stratified Negation in Datalog with Metric Temporal Operators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider models over the rational timeline, study their properties, and establish the computational complexity of reasoning. |
David J Tena Cucala; Przemysław A Wałęga; Bernardo Cuenca Grau; Egor Kostylev; |
| 1437 | Strong Explanations in Abstract Argumentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by recent research, we present a more general class of explanations: in this paper we propose and study so-called strong explanations for explaining argumentative acceptance in AFs. |
Markus Ulbricht; Johannes P. Wallner; |
| 1438 | On The Tractability of SHAP Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we establish the complexity of computing the SHAP explanation in three important settings. |
Guy Van den Broeck; Anton Lykov; Maximilian Schleich; Dan Suciu; |
| 1439 | On Exploiting Hitting Sets for Model Reconciliation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a logic-based framework for model reconciliation that extends beyond the realm of planning. |
Stylianos Loukas Vasileiou; Alessandro Previti; William Yeoh; |
| 1440 | Focused Inference and System P Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define a series of query-dependent, syntactically-driven focused inference relations, elaborate on their formal properties, and show that the series converges against System P. |
Marco Wilhelm; Gabriele Kern-Isberner; |
| 1441 | On-the-fly Synthesis for LTL Over Finite Traces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new synthesis framework based on the on-the-fly DFA construction for LTL over finite traces (LTLf ). |
Shengping Xiao; Jianwen Li; Shufang Zhu; Yingying Shi; Geguang Pu; Moshe Vardi; |
| 1442 | Testing Independence Between Linear Combinations for Causal Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model (SEM). |
Hao Zhang; Kun Zhang; Shuigeng Zhou; Jihong Guan; Ji Zhang; |
| 1443 | SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce SWIFT, which minimizes the Wasserstein distance that measures the distance between the input tensor and that of the reconstruction. |
Ardavan Afshar; Kejing Yin; Sherry Yan; Cheng Qian; Joyce Ho; Haesun Park; Jimeng Sun; |
| 1444 | DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we prove the lower bound for top-K subset selection with bandit feedback with possibly correlated rewards. |
Mridul Agarwal; Vaneet Aggarwal; Abhishek Kumar Umrawal; Chris Quinn; |
| 1445 | Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In tabular finite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (TS)-like algorithm, randomized least-squares value iteration (RLSVI). |
Priyank Agrawal; Jinglin Chen; Nan Jiang; |
| 1446 | Semi-supervised Sequence Classification Through Change Point Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a novel framework for semi-supervised learning in such contexts. |
Nauman Ahad; Mark A. Davenport; |
| 1447 | Learning Invariant Representations Using Inverse Contrastive Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our paper, we introduce a class of losses for learning representations that are invariant to some extraneous variable of interest by inverting the class of contrastive losses, i.e., inverse contrastive loss (ICL). |
Aditya Kumar Akash; Vishnu Suresh Lokhande; Sathya N. Ravi; Vikas Singh; |
| 1448 | Learned Bi-Resolution Image Coding Using Generalized Octave Convolutions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a learned bi-resolution image coding approach that is based on the recently developed octave convolutions to factorize the latents into high and low resolution components. |
Mohammad Akbari; Jie Liang; Jingning Han; Chengjie Tu; |
| 1449 | Deep Bayesian Quadrature Policy Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose deep Bayesian quadrature policy gradient (DBQPG), a computationally efficient high-dimensional generalization of Bayesian quadrature, for policy gradient estimation. |
Ravi Tej Akella; Kamyar Azizzadenesheli; Mohammad Ghavamzadeh; Animashree Anandkumar; Yisong Yue; |
| 1450 | ETREE: Learning Tree-structured Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose eTREE, a model that incorporates the (usually ignored) tree structure to enhance the quality of the embeddings. |
Faisal M. Almutairi; Yunlong Wang; Dong Wang; Emily Zhao; Nicholas D. Sidiropoulos; |
| 1451 | Does Explainable Artificial Intelligence Improve Human Decision-Making? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using real datasets, we compare objective human decision accuracy without AI (control), with an AI prediction (no explanation), and AI prediction with explanation. |
Yasmeen Alufaisan; Laura R. Marusich; Jonathan Z. Bakdash; Yan Zhou; Murat Kantarcioglu; |
| 1452 | Decentralized Multi-Agent Linear Bandits with Safety Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For the recently studied problem of linear bandits with unknown linear safety constraints, we propose the first safe decentralized algorithm. |
Sanae Amani; Christos Thrampoulidis; |
| 1453 | Computing An Efficient Exploration Basis for Learning with Univariate Polynomial Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our characterisation of the barycentric spanner is two-fold: we show that the barycentric spanner under a polynomial cost function is the unique solution to a set of nonlinear algebraic equations, as well as the solution to a convex optimization problem. |
Chaitanya Amballa; Manu K. Gupta; Sanjay P. Bhat; |
| 1454 | Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that the problem of noise estimation for multimodal data can be reduced to a multimodal density estimation task. |
Elad Amrani; Rami Ben-Ari; Daniel Rotman; Alex Bronstein; |
| 1455 | An Enhanced Advising Model in Teacher-Student Framework Using State Categorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The contributions of our paper are 4-fold (a) effectively leveraging a teacher’s knowledge by richer advising (b) introduction of ARM to effectively reuse the advice throughout learning (c) ability to achieve significant performance boost even with a coarse state categorization (d) enabling the student to outperform the teacher. |
Daksh Anand; Vaibhav Gupta; Praveen Paruchuri; Balaraman Ravindran; |
| 1456 | On Lipschitz Regularization of Convolutional Layers Using Toeplitz Matrix Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, by leveraging the theory of Toeplitz matrices, we introduce a new upper bound for convolutional layers that is both tight and easy to compute. |
Alexandre Araujo; Benjamin Negrevergne; Yann Chevaleyre; Jamal Atif; |
| 1457 | The Tractability of SHAP-Score-Based Explanations for Classification Over Deterministic and Decomposable Boolean Circuits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a proof of a stronger result over Boolean models: the SHAP-score can be computed in polynomial time over deterministic and decomposable Boolean circuits. |
Marcelo Arenas; Pablo Barceló; Leopoldo Bertossi; Mikaël Monet; |
| 1458 | TabNet: Attentive Interpretable Tabular Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. |
Sercan Ö. Arik; Tomas Pfister; |
| 1459 | Robust Model Compression Using Deep Hypotheses Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we present a new algorithm, the Multiclass Empirical Median Optimization (MEMO) algorithm that finds a deep hypothesis in multi-class tasks, and prove its correctness. |
Omri Armstrong; Ran Gilad-Bachrach; |
| 1460 | Deep Radial-Basis Value Functions for Continuous Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce deep radial-basis value functions (RBVFs): value functions learned using a deep network with a radial-basis function (RBF) output layer. |
Kavosh Asadi; Neev Parikh; Ronald E. Parr; George D. Konidaris; Michael L. Littman; |
| 1461 | DecAug: Out-of-Distribution Generalization Via Decomposed Feature Representation and Semantic Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization. |
Haoyue Bai; Rui Sun; Lanqing Hong; Fengwei Zhou; Nanyang Ye; Han-Jia Ye; S.-H. Gary Chan; Zhenguo Li; |
| 1462 | Correlative Channel-Aware Fusion for Multi-View Time Series Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Existing methods for MVTSC mainly aim to fuse multi-view information at an early stage, e.g., by extracting a common feature subspace among multiple views. |
Yue Bai; Lichen Wang; Zhiqiang Tao; Sheng Li; Yun Fu; |
| 1463 | Deterministic Mini-batch Sequencing for Training Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel algorithm to generate a deterministic sequence of mini-batches to train a deep neural network (rather than a random sequence). |
Subhankar Banerjee; Shayok Chakraborty; |
| 1464 | Relative Variational Intrinsic Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To ensure useful skill diversity, we propose a novel skill learning objective, Relative Variational Intrinsic Control (RVIC), which incentivizes learning skills that are distinguishable in how they change the agent’s relationship to its environment. |
Kate Baumli; David Warde-Farley; Steven Hansen; Volodymyr Mnih; |
| 1465 | A Theory of Independent Mechanisms for Extrapolation in Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a theoretical framework to address this challenging situation by defining a weaker form of identifiability, based on the principle of independence of mechanisms. |
Michel Besserve; Remy Sun; Dominik Janzing; Bernhard Schölkopf; |
| 1466 | ExGAN: Adversarial Generation of Extreme Samples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples. |
Siddharth Bhatia; Arjit Jain; Bryan Hooi; |
| 1467 | Ordinal Historical Dependence in Graphical Event Models with Tree Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the practical challenge of parameter explosion due to the number of potential orders that is super-exponential in the number of parents, we introduce a novel graphical event model based on a parametric tree representation for capturing ordinal historical dependence. |
Debarun Bhattacharjya; Tian Gao; Dharmashankar Subramanian; |
| 1468 | Characterizing The Loss Landscape in Non-Negative Matrix Factorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit the NMF optimization problem and analyze its loss landscape in non-worst-case settings. |
Johan Bjorck; Anmol Kabra; Kilian Q. Weinberger; Carla Gomes; |
| 1469 | Understanding Decoupled and Early Weight Decay Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The goal of this paper is to investigate these two recent empirical observations. |
Johan Bjorck; Kilian Q. Weinberger; Carla Gomes; |
| 1470 | Communication-Aware Collaborative Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. |
Avrim Blum; Shelby Heinecke; Lev Reyzin; |
| 1471 | Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). |
Yoonho Boo; Sungho Shin; Jungwook Choi; Wonyong Sung; |
| 1472 | Fast Training of Provably Robust Neural Networks By SingleProp Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. |
Akhilan Boopathy; Lily Weng; Sijia Liu; Pin-Yu Chen; Gaoyuan Zhang; Luca Daniel; |
| 1473 | Sample-Specific Output Constraints for Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ConstraintNet—a scalable neural network architecture which constrains the output space in each forward pass independently. |
Mathis Brosowsky; Florian Keck; Olaf Dünkel; Marius Zöllner; |
| 1474 | Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To capture and analyze such scenarios, we introduce a novel family of stochastic pairwise constraints, which we incorporate into several essential clustering objectives (radius/median/means). |
Brian Brubach; Darshan Chakrabarti; John P. Dickerson; Aravind Srinivasan; Leonidas Tsepenekas; |
| 1475 | Improving Ensemble Robustness By Collaboratively Promoting and Demoting Adversarial Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose in this work a simple, but effective strategy to collaborate among committee models of an ensemble model. |
Anh Tuan Bui; Trung Le; He Zhao; Paul Montague; Olivier deVel; Tamas Abraham; Dinh Phung; |
| 1476 | Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As alternative, we present an efficient message passing algorithm that computes the probability distribution of the cascade size for the Independent Cascade Model on weighted directed networks and generalizations. |
Rebekka Burkholz; John Quackenbush; |
| 1477 | Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a condition-guided adaptation framework that is empowered by a special attentive progressive adversarial training (APAT) mechanism and a novel self-training policy. |
Bowen Cai; Huan Fu; Rongfei Jia; Binqiang Zhao; Hua Li; Yinghui Xu; |
| 1478 | Time Series Domain Adaptation Via Sparse Associative Structure Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In detail, in the fully dependent time series, a small change of the time lags or the offsets may lead to difficulty in the domain invariant extraction. |
Ruichu Cai; Jiawei Chen; Zijian Li; Wei Chen; Keli Zhang; Junjian Ye; Zhuozhang Li; Xiaoyan Yang; Zhenjie Zhang; |
| 1479 | A Blind Block Term Decomposition of High Order Tensors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: 2) We propose an algebraic method to compute the BBTD. |
Yunfeng Cai; Ping Li; |
| 1480 | Open-Set Recognition with Gaussian Mixture Variational Autoencoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. |
Alexander Cao; Yuan Luo; Diego Klabjan; |
| 1481 | Provably Secure Federated Learning Against Malicious Clients Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, given any base federated learning algorithm, we use the algorithm to learn multiple global models, each of which is learnt using a randomly selected subset of clients. |
Xiaoyu Cao; Jinyuan Jia; Neil Zhenqiang Gong; |
| 1482 | Dual Quaternion Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of learning representations of entities and relations in the knowledge graph for the link prediction task. |
Zongsheng Cao; Qianqian Xu; Zhiyong Yang; Xiaochun Cao; Qingming Huang; |
| 1483 | Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier. |
Miguel Á. Carreira-Perpiñán; Suryabhan Singh Hada; |
| 1484 | Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled samples and a large set of unlabeled samples. |
Paola Cascante-Bonilla; Fuwen Tan; Yanjun Qi; Vicente Ordonez; |
| 1485 | Frivolous Units: Wider Networks Are Not Really That Wide Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We identify two distinct types of “frivolous” units that proliferate when the network’s width increases: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. |
Stephen Casper; Xavier Boix; Vanessa D’Amario; Ling Guo; Martin Schrimpf; Kasper Vinken; Gabriel Kreiman; |
| 1486 | Automated Clustering of High-dimensional Data with A Feature Weighted Mean Shift Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple yet elegant feature-weighted variant of mean shift to efficiently learn the feature importance and thus, extending the merits of mean shift to high-dimensional data. |
Saptarshi Chakraborty; Debolina Paul; Swagatam Das; |
| 1487 | High-Confidence Off-Policy (or Counterfactual) Variance Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we tackle the previously open problem of estimating and bounding, with high confidence, the variance of returns from off-policy data. |
Yash Chandak; Shiv Shankar; Philip S. Thomas; |
| 1488 | A Multi-step-ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we depart from the typical practice of pattern recognition and time-series approaches and focus on employing machine learning to estimate the probabilities of extreme weather occurrences in a multi-step-ahead (MSA) fashion given information on both weather features and the realized occurrences of extreme weather. |
Chia-Yuan Chang; Cheng-Wei Lu; Chuan-Ju Wang; |
| 1489 | Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence is represented by a distinct set of multi-mode codebook embeddings to capture different semantic facets of its meaning. |
Haw-Shiuan Chang; Amol Agrawal; Andrew McCallum; |
| 1490 | On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on unconstrained online optimization. |
Ting-Jui Chang; Shahin Shahrampour; |
| 1491 | Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper sheds light on these empirical findings by theoretically characterizing the high-dimensional asymptotics of model pruning in the overparameterized regime. |
Xiangyu Chang; Yingcong Li; Samet Oymak; Christos Thrampoulidis; |
| 1492 | Differentially Private Decomposable Submodular Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of differentially private constrained maximization of decomposable submodular functions. |
Anamay Chaturvedi; Huy Lê Nguyễn; Lydia Zakynthinou; |
| 1493 | Using Hindsight to Anchor Past Knowledge in Continual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we complement experience replay with a new objective that we call “anchoring”, where the learner uses bilevel optimization to update its knowledge on the current task, while keeping intact the predictions on some anchor points of past tasks. |
Arslan Chaudhry; Albert Gordo; Puneet Dokania; Philip Torr; David Lopez-Paz; |
| 1494 | Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework — deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. |
Tong Che; Xiaofeng Liu; Site Li; Yubin Ge; Ruixiang Zhang; Caiming Xiong; Yoshua Bengio; |
| 1495 | Scalable and Explainable 1-Bit Matrix Completion Via Graph Signal Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the computational and memory issue of performing graph signal operations on large graphs, we construct a scalable Nystrom algorithm which can efficiently compute orthonormal eigenvectors. |
Chao Chen; Dongsheng Li; Junchi Yan; Hanchi Huang; Xiaokang Yang; |
| 1496 | Addressing Action Oscillations Through Learning Policy Inertia Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Policy Inertia Controller (PIC) which serves as a generic plug-in framework to off-the-shelf DRL algorithms, to enable adaptive balance between the optimality and smoothness in a formal way. |
Chen Chen; Hongyao Tang; Jianye Hao; Wulong Liu; Zhaopeng Meng; |
| 1497 | Cross-Layer Distillation with Semantic Calibration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose Semantic Calibration for Cross-layer Knowledge Distillation (SemCKD), which automatically assigns proper target layers of the teacher model for each student layer with an attention mechanism. |
Defang Chen; Jian-Ping Mei; Yuan Zhang; Can Wang; Zhe Wang; Yan Feng; Chun Chen; |
| 1498 | Distributed Ranking with Communications: Approximation Analysis and Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new distributed pairwise ranking with communication (called DLSRank-C) based on the Newton-Raphson iteration, and establish its learning rate analysis in probability. |
Hong Chen; Yingjie Wang; Yulong Wang; Feng Zheng; |
| 1499 | THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by KFAC, we propose a novel Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation method, called THOR, to make the second-order optimization applicable in the real application models. |
Mengyun Chen; Kaixin Gao; Xiaolei Liu; Zidong Wang; Ningxi Ni; Qian Zhang; Lei Chen; Chao Ding; Zhenghai Huang; Min Wang; Shuangling Wang; Fan Yu; Xinyuan Zhao; Dachuan Xu; |
| 1500 | Neural Relational Inference with Efficient Message Passing Mechanisms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces efficient message passing mechanisms to the graph neural networks with structural prior knowledge to address these problems. |
Siyuan Chen; Jiahai Wang; Guoqing Li; |
| 1501 | Fitting The Search Space of Weight-sharing NAS with Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. |
Xin Chen; Lingxi Xie; Jun Wu; Longhui Wei; Yuhui Xu; Qi Tian; |
| 1502 | Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose an Oscillation Postsynaptic Potential (Os-PSP) and phase-locking active function, and further put forward a new spiking neuron model, namely Resonate Spiking Neuron (RSN). |
Yi Chen; Hong Qu; Malu Zhang; Yuchen Wang; |
| 1503 | HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Hypergradient Data Relevance Analysis, or HyDRA, which interprets the predictions made by DNNs as effects of their training data. |
Yuanyuan Chen; Boyang Li; Han Yu; Pengcheng Wu; Chunyan Miao; |
| 1504 | NASGEM: Neural Architecture Search Via Graph Embedding Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. |
Hsin-Pai Cheng; Tunhou Zhang; Yixing Zhang; Shiyu Li; Feng Liang; Feng Yan; Meng Li; Vikas Chandra; Hai Li; Yiran Chen; |
| 1505 | Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an unsupervised multi-view outlier detection method to address these issues. |
Li Cheng; Yijie Wang; Xinwang Liu; |
| 1506 | Self-Progressing Robust Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a different perspective and propose a new framework SPROUT, self-progressing robust training. |
Minhao Cheng; Pin-Yu Chen; Sijia Liu; Shiyu Chang; Cho-Jui Hsieh; Payel Das; |
| 1507 | Continuous-Time Attention for Sequential Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new continuous-time attention method for sequential learning which is tightly integrated with NDE to construct an attentive continuous-time state machine. |
Jen-Tzung Chien; Yi-Hsiang Chen; |
| 1508 | Transfer Learning for Efficient Iterative Safety Validation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We apply transfer learning to improve the efficiency of reinforcement learning based safety validation algorithms when applied to related systems. |
Anthony Corso; Mykel J. Kochenderfer; |
| 1509 | Computationally Tractable Riemannian Manifolds for Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we explore two computationally efficient matrix manifolds, showcasing how to learn and optimize graph embeddings in these Riemannian spaces. |
Calin Cruceru; Gary Becigneul; Octavian-Eugen Ganea; |
| 1510 | Cost-aware Graph Generation: A Deep Bayesian Optimization Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, from the perspective of saving cost, we propose a novel Cost-Aware Graph Generation (CAGG) framework to generate graphs with optimal properties at as low cost as possible. |
Jiaxu Cui; Bo Yang; Bingyi Sun; Jiming Liu; |
| 1511 | Type-augmented Relation Prediction in Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a type-augmented relation prediction (TaRP) method, where we apply both the type information and instance-level information for the relation prediction. |
Zijun Cui; Pavan Kapanipathi; Kartik Talamadupula; Tian Gao; Qiang Ji; |
| 1512 | The Value-Improvement Path: Towards Better Representations for Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we take a novel perspective, arguing that the value prediction problems faced by an RL agent should not be addressed in isolation, but rather as a single, holistic, prediction problem. |
Will Dabney; André Barreto; Mark Rowland; Robert Dadashi; John Quan; Marc G. Bellemare; David Silver; |
| 1513 | Loop Estimator for Discounted Values in Markov Reward Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple and efficient estimator called loop estimator that exploits the regenerative structure of Markov reward processes without explicitly estimating a full model. |
Falcon Z. Dai; Matthew R. Walter; |
| 1514 | Differentially Private Stochastic Coordinate Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. |
Georgios Damaskinos; Celestine Mendler-Dünner; Rachid Guerraoui; Nikolaos Papandreou; Thomas Parnell; |
| 1515 | Generalized Adversarially Learned Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Within our proposed framework, we introduce a novel set of techniques for providing self-supervised feedback to the model based on properties, such as patch-level correspondence and cycle consistency of reconstructions. |
Yatin Dandi; Homanga Bharadhwaj; Abhishek Kumar; Piyush Rai; |
| 1516 | Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of learning the underlying graph of a sparse Ising model with p nodes from n i.i.d. samples. |
Antoine Dedieu; Miguel Lázaro-Gredilla; Dileep George; |
| 1517 | Learning with Retrospection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. |
Xiang Deng; Zhongfei Zhang; |
| 1518 | Mercer Features for Efficient Combinatorial Bayesian Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient approach referred as Mercer Features for Combinatorial Bayesian Optimization (MerCBO). |
Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa; |
| 1519 | Differentially Private and Communication Efficient Collaborative Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to address these problems, we propose two differentially private (DP) and communication efficient algorithms, called Q-DPSGD-1 and Q-DPSGD-2. |
Jiahao Ding; Guannan Liang; Jinbo Bi; Miao Pan; |
| 1520 | Knowledge Refinery: Learning from Decoupled Label Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method called Knowledge Refinery (KR), which enables the neural network to learn the relation of classes on-the-fly without the teacher-student training strategy. |
Qianggang Ding; Sifan Wu; Tao Dai; Hao Sun; Jiadong Guo; Zhang-Hua Fu; Shutao Xia; |
| 1521 | Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two generic methods for improving semi-supervised learning (SSL). |
Kien Do; Truyen Tran; Svetha Venkatesh; |
| 1522 | Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a simple and lightweight variant of the Shuffle-Exchange network, which is based on a residual network employing GELU and Layer Normalization. |
Andis Draguns; Emīls Ozoliņš; Agris Šostaks; Matīss Apinis; Karlis Freivalds; |
| 1523 | A One-Size-Fits-All Solution to Conservative Bandit Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner’s reward performance must be at least as well as a given baseline at any time. |
Yihan Du; Siwei Wang; Longbo Huang; |
| 1524 | Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For CPE-BL, we design the first polynomial-time adaptive algorithm, whose sample complexity matches the lower bound (within a logarithmic factor) for a family of instances and has a light dependence of \Delta_min (the smallest gap between the optimal action and sub-optimal actions). |
Yihan Du; Yuko Kuroki; Wei Chen; |
| 1525 | Knowledge Refactoring for Inductive Program Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. |
Sebastijan Dumancic; Tias Guns; Andrew Cropper; |
| 1526 | Semi-Supervised Metric Learning: A Deep Resurrection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this challenging problem, and revamp SSDML with respect to deep learning. |
Ujjal Kr Dutta; Mehrtash Harandi; C Chandra Shekhar; |
| 1527 | Reinforcement Learning with Trajectory Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we take a first step towards relaxing this assumption and require a weaker form of feedback, which we refer to as \emph{trajectory feedback}. |
Yonathan Efroni; Nadav Merlis; Shie Mannor; |
| 1528 | The Parameterized Complexity of Clustering Incomplete Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study fundamental clustering problems for incomplete data. |
Eduard Eiben; Robert Ganian; Iyad Kanj; Sebastian Ordyniak; Stefan Szeider; |
| 1529 | Learning Prediction Intervals for Model Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Automated performance prediction techniques aim to mitigate this burden, but potential inaccuracy and a lack of trust in their predictions has prevented their widespread adoption. |
Benjamin Elder; Matthew Arnold; Anupama Murthi; Jiří Navrátil; |
| 1530 | Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide new adaptive first-order methods for constrained convex optimization. |
Alina Ene; Huy L. Nguyen; Adrian Vladu; |
| 1531 | Projection-Free Bandit Optimization with Privacy Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. |
Alina Ene; Huy L. Nguyen; Adrian Vladu; |
| 1532 | Learning to Cascade: Confidence Calibration for Improving The Accuracy and Computational Cost of Cascade Inference Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the discussion, we propose a new confidence calibration method, Learning to Cascade. |
Shohei Enomoro; Takeharu Eda; |
| 1533 | Regret Bounds for Batched Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present simple algorithms for batched stochastic multi-armed bandit and batched stochastic linear bandit problems. |
Hossein Esfandiari; Amin Karbasi; Abbas Mehrabian; Vahab Mirrokni; |
| 1534 | Almost Linear Time Density Level Set Estimation Via DBSCAN Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we focus on designing a fast algorithm for lambda-density level set estimation via DBSCAN clustering. |
Hossein Esfandiari; Vahab Mirrokni; Peilin Zhong; |
| 1535 | Deep Graph Spectral Evolution Networks for Graph Topological Evolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, this paper proposes the deep Graph Spectral Evolution Network (GSEN) for modeling the graph topology evolution problem by the composition of newly-developed generalized graph kernels. |
Negar Etemadyrad; Qingzhe Li; Liang Zhao; |
| 1536 | Adversarial Training and Provable Robustness: A Tale of Two Objectives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks. |
Jiameng Fan; Wenchao Li; |
| 1537 | Learning A Gradient-free Riemannian Optimizer on Tangent Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple yet efficient Riemannian meta-optimization method that learns to optimize on tangent spaces of manifolds. |
Xiaomeng Fan; Zhi Gao; Yuwei Wu; Yunde Jia; Mehrtash Harandi; |
| 1538 | Learning to Reweight with Deep Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. |
Yang Fan; Yingce Xia; Lijun Wu; Shufang Xie; Weiqing Liu; Jiang Bian; Tao Qin; Xiang-Yang Li; |
| 1539 | Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. |
Amirreza Farnoosh; Bahar Azari; Sarah Ostadabbas; |
| 1540 | UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by these observations, we propose UAG, the first systematic solution to defend adversarial attacks on GNNs through identifying and exploiting hierarchical uncertainties in GNNs. |
Boyuan Feng; Yuke Wang; Yufei Ding; |
| 1541 | SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. |
Hao-Zhe Feng; Kezhi Kong; Minghao Chen; Tianye Zhang; Minfeng Zhu; Wei Chen; |
| 1542 | Learning to Augment for Data-scarce Domain BERT Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. |
Lingyun Feng; Minghui Qiu; Yaliang Li; Hai-Tao Zheng; Ying Shen; |
| 1543 | Collaborative Group Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. |
Shaoxiong Feng; Hongshen Chen; Xuancheng Ren; Zhuoye Ding; Kan Li; Xu Sun; |
| 1544 | Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. |
Christian Fiedler; Carsten W. Scherer; Sebastian Trimpe; |
| 1545 | Few-Shot One-Class Classification Via Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strategy of the model-agnostic meta-learning (MAML) algorithm to learn a model initialization particularly suited for learning few-shot OCC tasks. |
Ahmed Frikha; Denis Krompaß; Hans-Georg Köpken; Volker Tresp; |
| 1546 | Towards Effective Context for Meta-Reinforcement Learning: An Approach Based on Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Meta-RL framework called CCM (Contrastive learning augmented Context-based Meta-RL). |
Haotian Fu; Hongyao Tang; Jianye Hao; Chen Chen; Xidong Feng; Dong Li; Wulong Liu; |
| 1547 | Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an agreement-discrepancy-selection (ADS) approach, and target at unifying distribution alignment with sample selection by introducing adversarial classifiers to the convolutional neural network (CNN). |
Mengying Fu; Tianning Yuan; Fang Wan; Songcen Xu; Qixiang Ye; |
| 1548 | Generalize A Small Pre-trained Model to Arbitrarily Large TSP Instances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. |
Zhang-Hua Fu; Kai-Bin Qiu; Hongyuan Zha; |
| 1549 | HiGAN: Handwriting Imitation Conditioned on Arbitrary-Length Texts and Disentangled Styles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel handwriting imitation generative adversarial network (HiGAN) to mimic such hallucinations. |
Ji Gan; Weiqiang Wang; |
| 1550 | Diffusion Network Inference from Partial Observations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study how to carry out diffusion network inference without infection timestamps, using only partial observations of the final infection statuses of nodes. |
Ting Gan; Keqi Han; Hao Huang; Shi Ying; Yunjun Gao; Zongpeng Li; |
| 1551 | Stabilizing Q Learning Via Soft Mellowmax Operator Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we address all the above issues with an enhanced Mellowmax operator, named SM2 (Soft Mellowmax). |
Yaozhong Gan; Zhe Zhang; Xiaoyang Tan; |
| 1552 | On The Convergence of Communication-Efficient Local SGD for Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a new communication-efficient distributed SGD method, which can significantly reduce the communication cost by the error-compensated double compression mechanism. |
Hongchang Gao; An Xu; Heng Huang; |
| 1553 | A Trace-restricted Kronecker-Factored Approximation to Natural Gradient Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, inspired by diagonal approximations and factored approximations such as Kronecker-factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC), which can hold the certain trace relationship between the exact and the approximate FIM. |
Kaixin Gao; Xiaolei Liu; Zhenghai Huang; Min Wang; Zidong Wang; Dachuan Xu; Fan Yu; |
| 1554 | Addressing Domain Gap Via Content Invariant Representation for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, we propose a novel domain adaptation approach, called Content Invariant Representation Network, to narrow the domain gap between the source (S) and target (T) domains. |
Li Gao; Lefei Zhang; Qian Zhang; |
| 1555 | Increasing Iterate Averaging for Solving Saddle-Point Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Many problems in machine learning and game theory can be formulated as saddle-point problems, for which various first-order methods have been developed and proven efficient in practice. |
Yuan Gao; Christian Kroer; Donald Goldfarb; |
| 1556 | Uncertainty-Aware Multi-View Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets). |
Yu Geng; Zongbo Han; Changqing Zhang; Qinghua Hu; |
| 1557 | Justicia: A Stochastic SAT Approach to Formally Verify Fairness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a stochastic satisfiability (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. |
Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel; |
| 1558 | The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using causal graphs, we characterize the missingness mechanisms in different real-world scenarios. |
Naman Goel; Alfonso Amayuelas; Amit Deshpande; Amit Sharma; |
| 1559 | Attribute-Guided Adversarial Training for Robustness to Natural Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. |
Tejas Gokhale; Rushil Anirudh; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Chitta Baral; Yezhou Yang; |
| 1560 | Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel on-chip learning framework to release the full potential of ONNs for power-efficient in situ training. |
Jiaqi Gu; Chenghao Feng; Zheng Zhao; Zhoufeng Ying; Ray T. Chen; David Z. Pan; |
| 1561 | Attentive Neural Point Processes for Event Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose ANPP, an Attentive Neural Point Processes framework to solve this problem. |
Yulong Gu; |
| 1562 | Revisiting Iterative Back-Translation from The Perspective of Compositional Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit iterative back-translation, a simple yet effective semi-supervised method, to investigate whether and how it can improve compositional generalization. |
Yinuo Guo; Hualei Zhu; Zeqi Lin; Bei Chen; Jian-Guang Lou; Dongmei Zhang; |
| 1563 | Controllable Guarantees for Fair Outcomes Via Contrastive Information Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate an effective method for controlling parity through mutual information based on contrastive information estimators and show that they outperform approaches that rely on variational bounds based on complex generative models. |
Umang Gupta; Aaron M Ferber; Bistra Dilkina; Greg Ver Steeg; |
| 1564 | Towards Reusable Network Components By Learning Compatible Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we split a network into two components, a features extractor and a target task head, and propose various approaches to accomplish compatibility between them. |
Michael Gygli; Jasper Uijlings; Vittorio Ferrari; |
| 1565 | High-Dimensional Bayesian Optimization Via Tree-Structured Additive Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider generalized additive models in which low-dimensional functions with overlapping subsets of variables are composed to model a high-dimensional target function. |
Eric Han; Ishank Arora; Jonathan Scarlett; |
| 1566 | Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper we first disclose that the consistency assumption is closely related to causality invariance, where causality invariance lies in the main reason why the consistency assumption is valid. |
Tao Han; Wei-Wei Tu; Yu-Feng Li; |
| 1567 | DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. |
Mohammadhosein Hasanbeig; Natasha Yogananda Jeppu; Alessandro Abate; Tom Melham; Daniel Kroening; |
| 1568 | Liquid Time-constant Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new class of time-continuous recurrent neural network models. |
Ramin Hasani; Mathias Lechner; Alexander Amini; Daniela Rus; Radu Grosu; |
| 1569 | Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to characterize the relationship between safety constraints and the number of samples needed to ensure a desired level of accuracy—both objective maximization and constraint satisfaction—in a PAC sense. |
Aria HasanzadeZonuzy; Archana Bura; Dileep Kalathil; Srinivas Shakkottai; |
| 1570 | Analysing The Noise Model Error for Realistic Noisy Label Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the quality of these estimated noise models from the theoretical side by deriving the expected error of the noise model. |
Michael A. Hedderich; Dawei Zhu; Dietrich Klakow; |
| 1571 | Provably Good Solutions to The Knapsack Problem Via Neural Networks of Bounded Size Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contribution is a class of recurrent neural networks (RNNs) with rectified linear units that are iteratively applied to each item of a Knapsack instance and thereby compute optimal or provably good solution values. |
Christoph Hertrich; Martin Skutella; |
| 1572 | Scaling-Up Robust Gradient Descent Techniques Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study a scalable alternative to robust gradient descent (RGD) techniques that can be used when losses and/or gradients can be heavy-tailed, though this will be unknown to the learner. |
Matthew J. Holland; |
| 1573 | Learning Model-Based Privacy Protection Under Budget Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Learning-to-Protect algorithm that automatically learns a model-based protector from a set of non-private learning tasks. |
Junyuan Hong; Haotao Wang; Zhangyang Wang; Jiayu Zhou; |
| 1574 | Graph Game Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. |
Xiaobin Hong; Tong Zhang; Zhen Cui; Yuge Huang; Pengcheng Shen; Shaoxin Li; Jian Yang; |
| 1575 | Topology Distance: A Topology-Based Approach for Evaluating Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a distance complementary to existing measures: Topology Distance (TD), the main idea behind which is to compare the geometric and topological features of the latent manifold of real data with those of generated data. |
Danijela Horak; Simiao Yu; Gholamreza Salimi-Khorshidi; |
| 1576 | Storage Fit Learning with Feature Evolvable Streams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF2EL) which incorporates the issue of rarely-provided labels into feature evolution. |
Bo-Jian Hou; Yu-Hu Yan; Peng Zhao; Zhi-Hua Zhou; |
| 1577 | Reinforcement Learning Based Multi-Agent Resilient Control: From Deep Neural Networks to An Adaptive Law Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The experimental results indicate that both algorithms perform well with appearance of constant and/or random faulty agents, yet the Q-consensus algorithm outperforms the faulty ones running D-DDPG. |
Jian Hou; Fangyuan Wang; Lili Wang; Zhiyong Chen; |
| 1578 | Slimmable Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce slimmable GANs (SlimGANs), which can flexibly switch the width of the generator to accommodate various quality-efficiency trade-offs at runtime. |
Liang Hou; Zehuan Yuan; Lei Huang; Huawei Shen; Xueqi Cheng; Changhu Wang; |
| 1579 | Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in The COVID-19 Era and Beyond Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose and develop an intelligent system named Dr.HIN to protect users against the evolving Android malware attacks in the COVID-19 era and beyond. |
Shifu Hou; Yujie Fan; Mingxuan Ju; Yanfang Ye; Wenqiang Wan; Kui Wang; Yinming Mei; Qi Xiong; Fudong Shao; |
| 1580 | Gaussian Process Priors for View-Aware Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this, we derive a principled framework to combine information coupling between camera poses (translation and orientation) with deep models. |
Yuxin Hou; Ari Heljakka; Arno Solin; |
| 1581 | Boosting Multi-task Learning Through Combination of Task Labels – with Applications in ECG Phenotyping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we proposed an effective multi-task learning framework, CO-TASK, to boost multi-task learning performance by generating auxiliary tasks through COmbination of TASK Labels. |
Ming-En Hsieh; Vincent Tseng; |
| 1582 | OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e.g., pruning sparsity and quantization codebook) of each layer. |
Peng Hu; Xi Peng; Hongyuan Zhu; Mohamed M. Sabry Aly; Jie Lin; |
| 1583 | Multi-scale Graph Fusion for Co-saliency Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. |
Rongyao Hu; Zhenyun Deng; Xiaofeng Zhu; |
| 1584 | Continual Learning By Using Information of Each Class Holistically Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to avoid CF by considering the features of each class holistically rather than only the discriminative information for classifying the classes seen so far. |
Wenpeng Hu; Qi Qin; Mengyu Wang; Jinwen Ma; Bing Liu; |
| 1585 | Predictive Adversarial Learning from Positive and Unlabeled Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It proposes a novel PU learning method called Predictive Adversarial Networks (PAN) based on GAN (Generative Adversarial Networks). |
Wenpeng Hu; Ran Le; Bing Liu; Feng Ji; Jinwen Ma; Dongyan Zhao; Rui Yan; |
| 1586 | Multidimensional Uncertainty-Aware Evidential Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. |
Yibo Hu; Yuzhe Ou; Xujiang Zhao; Jin-Hee Cho; Feng Chen; |
| 1587 | Adversarial Defence By Diversified Simultaneous Training of Deep Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose adversarial defence by encouraging ensemble diversity on learning high-level feature representations and gradient dispersion in simultaneous training of deep ensemble networks. |
Bo Huang; Zhiwei Ke; Yi Wang; Wei Wang; Linlin Shen; Feng Liu; |
| 1588 | Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an approach to effectively accelerating the computation of continuous normalizing flow (CNF), which has been proven to be a powerful tool for the tasks such as variational inference and density estimation. |
Han-Hsien Huang; Mi-Yen Yeh; |
| 1589 | Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we devise an attributes-guided attention module (AGAM) to utilize human-annotated attributes and learn more discriminative features. |
Siteng Huang; Min Zhang; Yachen Kang; Donglin Wang; |
| 1590 | Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate such problem, this paper proposes to adaptively reweight the imaginary transitions, so as to reduce the negative effects of poorly generated trajectories. |
Wenzhen Huang; Qiyue Yin; Junge Zhang; Kaiqi Huang; |
| 1591 | ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit existing adaptive gradient optimization methods with a new interpretation. |
Xunpeng Huang; Runxin Xu; Hao Zhou; Zhe Wang; Zhengyang Liu; Lei Li; |
| 1592 | Personalized Cross-Silo Federated Learning on Non-IID Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. |
Yutao Huang; Lingyang Chu; Zirui Zhou; Lanjun Wang; Jiangchuan Liu; Jian Pei; Yong Zhang; |
| 1593 | Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems. |
Yu-Heng Hung; Ping-Chun Hsieh; Xi Liu; P. R. Kumar; |
| 1594 | Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Complete Layer-wise Adaptive Rate Scaling (CLARS) algorithm for large-batch training. |
Zhouyuan Huo; Bin Gu; Heng Huang; |
| 1595 | Accurate and Robust Feature Importance Estimation Under Distribution Shifts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose PRoFILE (Producing Robust Feature Importances using Loss Estimates), a novel feature importance estimation method that addresses all these challenges. |
Jayaraman J. Thiagarajan; Vivek Narayanaswamy; Rushil Anirudh; Peer-Timo Bremer; Andreas Spanias; |
| 1596 | Variance Penalized On-Policy and Off-Policy Actor-Critic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose on-policy and off-policy actor-critic algorithms that optimize a performance criterion involving both mean and variance in the return. |
Arushi Jain; Gandharv Patil; Ayush Jain; Khimya Khetarpal; Doina Precup; |
| 1597 | Constructing A Fair Classifier with Generated Fair Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to address this issue, we propose a new approach to improve machine learning fairness by generating fair data. |
Taeuk Jang; Feng Zheng; Xiaoqian Wang; |
| 1598 | Neural Utility Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The main idea of this work is to give neural networks inductive biases that are inspired by economic theories. |
Porter Jenkins; Ahmad Farag; J. Stockton Jenkins; Huaxiu Yao; Suhang Wang; Zhenhui Li; |
| 1599 | IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new GAN-based unsupervised model for disentangled representation learning. |
Insu Jeon; Wonkwang Lee; Myeongjang Pyeon; Gunhee Kim; |
| 1600 | Active Bayesian Assessment of Black-Box Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce an active Bayesian approach for assessment of classifier performance to satisfy the desiderata of both reliability and label-efficiency. |
Disi Ji; Robert L. Logan; Padhraic Smyth; Mark Steyvers; |
| 1601 | Show, Attend and Distill: Knowledge Distillation Via Attention-based Feature Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce an effective and efficient feature distillation method utilizing all the feature levels of the teacher without manually selecting the links. |
Mingi Ji; Byeongho Heo; Sungrae Park; |
| 1602 | Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. |
Taoran Ji; Nathan Self; Kaiqun Fu; Zhiqian Chen; Naren Ramakrishnan; Chang-Tien Lu; |
| 1603 | Intrinsic Certified Robustness of Bagging Against Data Poisoning Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We evaluate our method on MNIST and CIFAR10. |
Jinyuan Jia; Xiaoyu Cao; Neil Zhenqiang Gong; |
| 1604 | Clustering Ensemble Meets Low-rank Tensor Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel low-rank tensor approximation based method to solve the problem from a global perspective. |
Yuheng Jia; Hui Liu; Junhui Hou; Qingfu Zhang; |
| 1605 | Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, in order to reduce the underestimation bias, we propose an action candidate based clipped double estimator for Double Q-learning. |
Haobo Jiang; Jin Xie; Jian Yang; |
| 1606 | LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above problems, we propose LightXML, which adopts end-to-end training and dynamical negative labels sampling. |
Ting Jiang; Deqing Wang; Leilei Sun; Huayi Yang; Zhengyang Zhao; Fuzhen Zhuang; |
| 1607 | Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. |
Yuqian Jiang; Suda Bharadwaj; Bo Wu; Rishi Shah; Ufuk Topcu; Peter Stone; |
| 1608 | Power Up! Robust Graph Convolutional Network Via Graph Powering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability. |
Ming Jin; Heng Chang; Wenwu Zhu; Somayeh Sojoudi; |
| 1609 | Balanced Open Set Domain Adaptation Via Centroid Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples. |
Mengmeng Jing; Jingjing Li; Lei Zhu; Zhengming Ding; Ke Lu; Yang Yang; |
| 1610 | Linearly Replaceable Filters for Deep Network Channel Pruning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel channel pruning method, Linearly Replaceable Filter (LRF), which suggests that a filter that can be approximated by the linear combination of other filters is replaceable. |
Donggyu Joo; Eojindl Yi; Sunghyun Baek; Junmo Kim; |
| 1611 | A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the setting of episodic fixed-horizon CMDPs. |
Krishna C. Kalagarla; Rahul Jain; Pierluigi Nuzzo; |
| 1612 | Winning Lottery Tickets in Deep Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs. |
Neha Mukund Kalibhat; Yogesh Balaji; Soheil Feizi; |
| 1613 | Exploration Via State Influence Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It introduces a general intrinsic reward construction method to evaluate the social influence of states dynamically. |
Yongxin Kang; Enmin Zhao; Kai Li; Junliang Xing; |
| 1614 | Deep Probabilistic Canonical Correlation Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). |
Mahdi Karami; Dale Schuurmans; |
| 1615 | Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper develops a new approach for learning generalized heuristics in the absence of symbolic action models using deep neural networks that utilize an input predicate vocabulary but are agnostic to object names and quantities. |
Rushang Karia; Siddharth Srivastava; |
| 1616 | A Recipe for Global Convergence Guarantee in Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an algorithm, which is ensured to have global convergence guarantees in the practical regime beyond the NTK regime, under a verifiable condition called the expressivity condition. |
Kenji Kawaguchi; Qingyun Sun; |
| 1617 | Bayesian Dynamic Mode Decomposition with Variational Matrix Factorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel formulation of a Bayesian DMD model. |
Takahiro Kawashima; Hayaru Shouno; Hideitsu Hino; |
| 1618 | Improving Fairness and Privacy in Selection Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the possibility of using a differentially private exponential mechanism as a post-processing step to improve both fairness and privacy of supervised learning models. |
Mohammad Mahdi Khalili; Xueru Zhang; Mahed Abroshan; Somayeh Sojoudi; |
| 1619 | A Flexible Framework for Communication-Efficient Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a flexible framework which adapts the compression level to the true gradient at each iteration, maximizing the improvement in the objective function that is achieved per communicated bit. |
Sarit Khirirat; Sindri Magnússon; Arda Aytekin; Mikael Johansson; |
| 1620 | GLISTER: Generalization Based Data Subset Selection for Efficient and Robust Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce GLISTER, a GeneraLIzation based data Subset selecTion for Efficient and Robust learning framework. |
Krishnateja Killamsetty; Durga Sivasubramanian; Ganesh Ramakrishnan; Rishabh Iyer; |
| 1621 | Understanding Catastrophic Overfitting in Single-step Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate that catastrophic overfitting is very closely related to the characteristic of single-step adversarial training which uses only adversarial examples with the maximum perturbation, and not all adversarial examples in the adversarial direction, which leads to decision boundary distortion and a highly curved loss surface. |
Hoki Kim; Woojin Lee; Jaewook Lee; |
| 1622 | Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, this paper proposes Disentangled Causal Effect Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling the exogenous uncertainty into two latent variables: either 1) independent to interventions or 2) correlated to interventions without causality. |
Hyemi Kim; Seungjae Shin; JoonHo Jang; Kyungwoo Song; Weonyoung Joo; Wanmo Kang; Il-Chul Moon; |
| 1623 | Split-and-Bridge: Adaptable Class Incremental Learning Within A Single Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel continual learning method, called Split-and-Bridge, which can successfully address the above problem by partially splitting a neural network into two partitions for training the new task separated from the old task and re-connecting them for learning the knowledge across tasks. |
Jong-Yeong Kim; Dong-Wan Choi; |
| 1624 | DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). |
Jungeun Kim; Kookjin Lee; Dongeun Lee; Sheo Yon Jhin; Noseong Park; |
| 1625 | Kernel-convoluted Deep Neural Networks with Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formally investigate this premise, propose a way to impose smoothness constraints explicitly, and extend it to incorporate implicit model constraints. |
Minjin Kim; Young-geun Kim; Dongha Kim; Yongdai Kim; Myunghee Cho Paik; |
| 1626 | Neural Sequence-to-grid Module for Learning Symbolic Rules Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. |
Segwang Kim; Hyoungwook Nam; Joonyoung Kim; Kyomin Jung; |
| 1627 | Visual Concept Reasoning Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. |
Taesup Kim; Sungwoong Kim; Yoshua Bengio; |
| 1628 | Sparsity Aware Normalization for GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze the popular spectral normalization scheme, find a significant drawback and introduce sparsity aware normalization (SAN), a new alternative approach for stabilizing GAN training. |
Idan Kligvasser; Tomer Michaeli; |
| 1629 | HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a simple remedy by noting that subdivision and (affine) coupling can be repeated recursively within the resulting subsets, leading to an efficiently invertible block with dense, triangular Jacobian. |
Jakob Kruse; Gianluca Detommaso; Ullrich Köthe; Robert Scheichl; |
| 1630 | Nearly Linear-Time, Parallelizable Algorithms for Non-Monotone Submodular Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study combinatorial, parallelizable algorithms for maximization of a submodular function, not necessarily monotone, with respect to a cardinality constraint k. |
Alan Kuhnle; |
| 1631 | Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. |
Vyacheslav Kungurtsev; Malcolm Egan; Bapi Chatterjee; Dan Alistarh; |
| 1632 | Positions, Channels, and Layers: Fully Generalized Non-Local Network for Singer Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the limitation, we target at singer identification (SID) task and present a fully generalized non-local (FGNL) module to help identify fine-grained vocals. |
I-Yuan Kuo; Wen-Li Wei; Jen-Chun Lin; |
| 1633 | MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a hierarchical normalizing flow model for generating molecular graphs. |
Maksim Kuznetsov; Daniil Polykovskiy; |
| 1634 | Compressing Deep Convolutional Neural Networks By Stacking Low-dimensional Binary Convolution Filters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we propose a novel method to compress deep CNN model by stacking low-dimensional binary convolution filters. |
Weichao Lan; Liang Lan; |
| 1635 | Hypothesis Disparity Regularized Mutual Information Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a hypothesis disparity regularized mutual information maximization (HDMI) approach to tackle unsupervised hypothesis transfer—as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA)—where the knowledge from a source domain is transferred solely through hypotheses and adapted to the target domain in an unsupervised manner. |
Qicheng Lao; Xiang Jiang; Mohammad Havaei; |
| 1636 | Query Training: Learning A Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we introduce query training (QT), a mechanism to learn a PGM that is optimized for the approximate inference algorithm that will be paired with it. |
Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George; |
| 1637 | Metrics and Continuity in Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce a unified formalism for defining these topologies through the lens of metrics. |
Charline Le Lan; Marc G. Bellemare; Pablo Samuel Castro; |
| 1638 | Lipschitz Lifelong Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel metric between Markov Decision Processes and establish that close MDPs have close optimal value functions. |
Erwan Lecarpentier; David Abel; Kavosh Asadi; Yuu Jinnai; Emmanuel Rachelson; Michael L. Littman; |
| 1639 | Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: (2) We adapt the classic Rademacher analysis of DNNs to incorporate weight sharing—a task of fundamental theoretical importance which was previously attempted only under very restrictive assumptions. |
Antoine Ledent; Waleed Mustafa; Yunwen Lei; Marius Kloft; |
| 1640 | Learnable Dynamic Temporal Pooling for Time Series Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, pointing out that the global pooling layer that is usually adopted by existing CNN classifiers discards the temporal information of high-level features, we present a dynamic temporal pooling (DTP) technique that reduces the temporal size of hidden representations by aggregating the features at the segment-level. |
Dongha Lee; Seonghyeon Lee; Hwanjo Yu; |
| 1641 | Interpretable Embedding Procedure Knowledge Transfer Via Stacked Principal Component Analysis and Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method of generating interpretable embedding procedure (IEP) knowledge based on principal component analysis, and distilling it based on a message passing neural network. |
Seunghyun Lee; Byung Cheol Song; |
| 1642 | Unsupervised Domain Adaptation for Semantic Segmentation By Content Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. |
Suhyeon Lee; Junhyuk Hyun; Hongje Seong; Euntai Kim; |
| 1643 | Memory and Computation-Efficient Kernel SVM Via Binary Embedding and Ternary Model Coefficients Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. |
Zijian Lei; Liang Lan; |
| 1644 | Enhancing Parameter-Free Frank Wolfe with An Extra Subproblem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. |
Bingcong Li; Lingda Wang; Georgios B. Giannakis; Zhizhen Zhao; |
| 1645 | Unsupervised Active Learning Via Subspace Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In view of this, this paper proposes a novel unsupervised Active Learning model via Subspace Learning, called ALSL. |
Changsheng Li; Kaihang Mao; Lingyan Liang; Dongchun Ren; Wei Zhang; Ye Yuan; Guoren Wang; |
| 1646 | LRSC: Learning Representations for Subspace Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel subspace clustering framework through learning precise sample representations. |
Changsheng Li; Chen Yang; Bo Liu; Ye Yuan; Guoren Wang; |
| 1647 | GoT: A Growing Tree Model for Clustering Ensemble Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To handle these issues, in this paper, we propose a growing tree model (GoT). |
Feijiang Li; Yuhua Qian; Jieting Wang; |
| 1648 | VSQL: Variational Shadow Quantum Learning for Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). |
Guangxi Li; Zhixin Song; Xin Wang; |
| 1649 | High Fidelity GAN Inversion Via Prior Multi-Subspace Feature Composition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a Prior multi-Subspace Feature Composition (PmSFC) approach for high-fidelity inversion. |
Guanyue Li; Qianfen Jiao; Sheng Qian; Si Wu; Hau-San Wong; |
| 1650 | ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, in this paper, we propose a novel model called ShapeNet, which embeds shapelet candidates of different lengths into a unified space for shapelet selection. |
Guozhong Li; Byron Choi; Jianliang Xu; Sourav S Bhowmick; Kwok-Pan Chun; Grace Lai-Hung Wong; |
| 1651 | A Bayesian Approach for Subset Selection in Contextual Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel Bayesian approach for subset selection in general CB where the reward functions can be nonlinear. |
Jialian Li; Chao Du; Jun Zhu; |
| 1652 | Self-Paced Two-dimensional PCA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Unlike previous studies, we explicitly differentiate the samples to alleviate the impact of outliers and propose a novel method called Self-Paced 2DPCA (SP2DPCA)algorithm, which progresses from `easy’ to `complex’ samples. |
Jiangxin Li; Zhao Kang; Chong Peng; Wenyu Chen; |
| 1653 | Learning Intact Features By Erasing-Inpainting for Few-shot Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to learn intact features by erasing-inpainting for few-shot classification. |
Junjie Li; Zilei Wang; Xiaoming Hu; |
| 1654 | Token-Aware Virtual Adversarial Training in Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To craft fine-grained perturbations, we propose a Token-Aware Virtual Adversarial Training method. |
Linyang Li; Xipeng Qiu; |