Paper Digest: AAAI 2022 Highlights
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The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2022, it is to be held in Washington DC.
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TABLE 1: Paper Digest: AAAI 2022 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Pinpointing Fine-Grained Relationships Between Hateful Tweets and Replies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recent studies in the hate and counter hate domain have provided the grounds for investigating how to detect this pervasive content in social media. |
Abdullah Albanyan; Eduardo Blanco; |
| 2 | Cross-Modal Coherence for Text-to-Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we train a Cross-Modal Coherence Model for text-to-image retrieval task. |
Malihe Alikhani; Fangda Han; Hareesh Ravi; Mubbasir Kapadia; Vladimir Pavlovic; Matthew Stone; |
| 3 | Enhanced Story Comprehension for Large Language Models Through Dynamic Document-Based Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In order to mitigate the document length limitations that come with finite context windows, we introduce a novel architecture that augments story processing with an external dynamic knowledge graph. |
Berkeley R Andrus; Yeganeh Nasiri; Shilong Cui; Benjamin Cullen; Nancy Fulda; |
| 4 | Diagnostics-Guided Explanation Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Other diagnostic properties are Data Consistency, which measures how similar explanations are for similar input instances, and Confidence Indication, which shows whether the explanation reflects the confidence of the model. In this work, we show how to directly optimise for these diagnostic properties when training a model to generate sentence-level explanations, which markedly improves explanation quality, agreement with human rationales, and downstream task performance on three complex reasoning tasks. |
Pepa Atanasova; Jakob Grue Simonsen; Christina Lioma; Isabelle Augenstein; |
| 5 | Mitigating Reporting Bias in Semi-supervised Temporal Commonsense Inference with Probabilistic Soft Logic Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel neural-logic based Soft Logic Enhanced Event Temporal Reasoning (SLEER) model for acquiring unbiased TCS knowledge, in which the complementary relationship among dimensions are explicitly represented as logic rules and modeled by t-norm fuzzy logics. |
Bibo Cai; Xiao Ding; Bowen Chen; Li Du; Ting Liu; |
| 6 | Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a novel feature-level adversarial training method named FLAT. |
Hanjie Chen; Yangfeng Ji; |
| 7 | Unsupervised Editing for Counterfactual Stories Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose EDUCAT, an editing-based unsupervised approach for counterfactual story rewriting. |
Jiangjie Chen; Chun Gan; Sijie Cheng; Hao Zhou; Yanghua Xiao; Lei Li; |
| 8 | LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose LOREN, an approach for interpretable fact verification. |
Jiangjie Chen; Qiaoben Bao; Changzhi Sun; Xinbo Zhang; Jiaze Chen; Hao Zhou; Yanghua Xiao; Lei Li; |
| 9 | ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a contrastive learning framework named ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. |
Junfan Chen; Richong Zhang; Yongyi Mao; Jie Xu; |
| 10 | From Good to Best: Two-Stage Training for Cross-Lingual Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, without sufficient training data, it is not powerful enough to capture the nuances between the accurate answer and those approximate ones. Based on this observation, we develop a two-stage approach to enhance the model performance. |
Nuo Chen; Linjun Shou; Ming Gong; Jian Pei; |
| 11 | Probing Linguistic Information for Logical Inference in Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a methodology for probing knowledge for inference that logical systems require but often lack in pre-trained language model representations. |
Zeming Chen; Qiyue Gao; |
| 12 | On The Transferability of Pre-trained Language Models: A Study from Artificial Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we study what specific traits in the pre-training data, other than the semantics, make a pre-trained LM superior to their counterparts trained from scratch on downstream tasks. |
Cheng-Han Chiang; Hung-yi Lee; |
| 13 | C2L: Causally Contrastive Learning for Robust Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We thus aim to leverage contrastive learning and counterfactual augmentation for robustness. |
Seungtaek Choi; Myeongho Jeong; Hojae Han; Seung-won Hwang; |
| 14 | Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we concentrate on two contributions to the task: (1) we propose Retrieval Augmented Prompt Tuning (RAPT) as a parameter-efficient method to adapt large pre-trained language models for paraphrase generation; (2) we propose Novelty Conditioned RAPT (NC-RAPT) as a simple model-agnostic method of using specialized prompt tokens for controlled paraphrase generation with varying levels of lexical novelty. |
Jishnu Ray Chowdhury; Yong Zhuang; Shuyi Wang; |
| 15 | Flexible Instance-Specific Rationalization of NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, previous research has shown that there is no clear best scoring method across various text classification tasks while practitioners typically have to make several other ad-hoc choices regarding the length and the type of the rationale (e.g. short or long, contiguous or not). Inspired by this, we propose a simple yet effective and flexible method that allows selecting optimally for each data instance: (1) a feature scoring method; (2) the length; and (3) the type of the rationale. |
George Chrysostomou; Nikolaos Aletras; |
| 16 | InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce InfoLM a family of untrained metrics that can be viewed as a string-based metric that addresses the aforementioned flaws thanks to a pre-trained masked language model. |
Pierre Jean A. Colombo; Chloé Clavel; Pablo Piantanida; |
| 17 | Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel problem — Multimodal Sarcasm Explanation (MuSE) — given a multimodal sarcastic post containing an image and a caption, we aim to generate a natural language explanation to reveal the intended sarcasm. |
Poorav Desai; Tanmoy Chakraborty; Md Shad Akhtar; |
| 18 | Zero-Shot Commonsense Question Answering with Cloze Translation and Consistency Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we instead focus on better utilizing the implicit knowledge stored in pre-trained language models. |
Zi-Yi Dou; Nanyun Peng; |
| 19 | Synthetic Disinformation Attacks on Automated Fact Verification Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Furthermore, the development of modern NLP tools that can produce coherent, fabricated content would allow malicious actors to systematically generate adversarial disinformation for fact-checkers. In this work, we explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings: ADVERSARIAL ADDITION, where we fabricate documents and add them to the evidence repository available to the fact-checking system, and ADVERSARIAL MODIFICATION, where existing evidence source documents in the repository are automatically altered. |
Yibing Du; Antoine Bosselut; Christopher D. Manning; |
| 20 | Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, this process only involves two-tuple data at each stage, and this loose coupling fails to fully exploit the association between triplet data. In this paper, we attempt to model the joint probability of transcription and translation based on the speech input to directly leverage such triplet data. |
Yichao Du; Zhirui Zhang; Weizhi Wang; Boxing Chen; Jun Xie; Tong Xu; |
| 21 | Play The Shannon Game with Language Models: A Human-Free Approach to Summary Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics that use a pretrained language model to estimate the information content shared between a document and its summary. |
Nicholas Egan; Oleg Vasilyev; John Bohannon; |
| 22 | Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Here, we propose to leverage sentiment-carrying discourse markers to generate large-scale weakly-labeled data, which in turn can be used to adapt language models for sentiment analysis. |
Liat Ein-Dor; Ilya Shnayderman; Artem Spector; Lena Dankin; Ranit Aharonov; Noam Slonim; |
| 23 | Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation. |
Steven Y. Feng; Kevin Lu; Zhuofu Tao; Malihe Alikhani; Teruko Mitamura; Eduard Hovy; Varun Gangal; |
| 24 | Language Model Priming for Cross-Lingual Event Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel, language-agnostic approach to "priming" language models for the task of event extraction, providing particularly effective performance in low-resource and zero-shot cross-lingual settings. |
Steven Fincke; Shantanu Agarwal; Scott Miller; Elizabeth Boschee; |
| 25 | Language Modelling Via Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider language modelling (LM) as a multi-label structured prediction task by re-framing training from solely predicting a single ground-truth word to ranking a set of words which could continue a given context. |
Arvid Frydenlund; Gagandeep Singh; Frank Rudzicz; |
| 26 | NAREOR: The Narrative Reordering Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Reordering a narrative can impact the temporal, causal, event-based, and other inferences readers draw from it, which in turn can have strong effects both on its interpretation and interestingness. In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot. |
Varun Gangal; Steven Y. Feng; Malihe Alikhani; Teruko Mitamura; Eduard Hovy; |
| 27 | UNISON: Unpaired Cross-Lingual Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language. |
Jiahui Gao; Yi Zhou; Philip L. H. Yu; Shafiq Joty; Jiuxiang Gu; |
| 28 | AutoBERT-Zero: Evolving BERT Backbone from Scratch Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we make the first attempt to automatically discover novel pre-trained language model (PLM) backbone on a flexible search space containing the most fundamental operations from scratch. |
Jiahui Gao; Hang Xu; Han Shi; Xiaozhe Ren; Philip L. H. Yu; Xiaodan Liang; Xin Jiang; Zhenguo Li; |
| 29 | ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. |
Manas Gaur; Kalpa Gunaratna; Vijay Srinivasan; Hongxia Jin; |
| 30 | Explainable Metaphor Identification Inspired By Conceptual Metaphor Theory Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose the first explainable metaphor identification model, inspired by Conceptual Metaphor Theory. |
Mengshi Ge; Rui Mao; Erik Cambria; |
| 31 | Confidence Calibration for Intent Detection Via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unfortunately, mainstream neural networks are poorly calibrated, with a large gap between accuracy and confidence. To handle this problem defined as confidence calibration, we propose a model using the hyperspherical space and rebalanced accuracy-uncertainty loss. |
Yantao Gong; Cao Liu; Fan Yang; Xunliang Cai; Guanglu Wan; Jiansong Chen; Weipeng Zhang; Houfeng Wang; |
| 32 | SSAST: Self-Supervised Audio Spectrogram Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper focuses on audio and speech classification, and aims to reduce the need for large amounts of labeled data for the AST by leveraging self-supervised learning using unlabeled data. |
Yuan Gong; Cheng-I Lai; Yu-An Chung; James Glass; |
| 33 | Block-Skim: Efficient Question Answering for Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, different from other tasks such as sequence classification, answering the raised question does not necessarily need all the tokens in the context paragraph. Following this motivation, we propose Block-skim, which learns to skim unnecessary context in higher hidden layers to improve and accelerate the Transformer performance. |
Yue Guan; Zhengyi Li; Zhouhan Lin; Yuhao Zhu; Jingwen Leng; Minyi Guo; |
| 34 | Deep Clustering of Text Representations for Supervision-Free Probing of Syntax Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider two notions of syntax: Part of Speech Induction (POSI) and Constituency Labelling (CoLab) in this work. |
Vikram Gupta; Haoyue Shi; Kevin Gimpel; Mrinmaya Sachan; |
| 35 | Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. |
Momchil Hardalov; Arnav Arora; Preslav Nakov; Isabelle Augenstein; |
| 36 | Attention Biasing and Context Augmentation for Zero-Shot Control of Encoder-Decoder Transformers for Natural Language Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero shot. |
Devamanyu Hazarika; Mahdi Namazifar; Dilek Hakkani-Tür; |
| 37 | GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. |
Wanwei He; Yinpei Dai; Yinhe Zheng; Yuchuan Wu; Zheng Cao; Dermot Liu; Peng Jiang; Min Yang; Fei Huang; Luo Si; Jian Sun; Yongbin Li; |
| 38 | Protecting Intellectual Property of Language Generation APIs with Lexical Watermark Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This work targets at protecting IP of NLG APIs by identifying the attackers who have utilized watermarked responses from the victim NLG APIs. |
Xuanli He; Qiongkai Xu; Lingjuan Lyu; Fangzhao Wu; Chenguang Wang; |
| 39 | BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we propose a pre-trained language model, named BROS (BERT Relying On Spatiality), that encodes relative positions of texts in 2D space and learns from unlabeled documents with area-masking strategy. |
Teakgyu Hong; DongHyun Kim; Mingi Ji; Wonseok Hwang; Daehyun Nam; Sungrae Park; |
| 40 | Non-autoregressive Translation with Layer-Wise Prediction and Deep Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose DSLP, a highly efficient and high-performance model for machine translation. |
Chenyang Huang; Hao Zhou; Osmar R. Zaïane; Lili Mou; Lei Li; |
| 41 | Word Level Robustness Enhancement: Fight Perturbation with Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we design a robustness enhancement method to defend against word substitution perturbation, whose basic idea is to fight perturbation with perturbation. |
Pei Huang; Yuting Yang; Fuqi Jia; Minghao Liu; Feifei Ma; Jian Zhang; |
| 42 | Predicting Above-Sentence Discourse Structure Using Distant Supervision from Topic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. |
Patrick Huber; Linzi Xing; Giuseppe Carenini; |
| 43 | Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, existing conversational agents and datasets do not consider such comprehensive information, and thus they have a limitation in generating the utterances where the knowledge and persona are fused properly. To address this issue, we introduce a call For Customized conversation (FoCus) dataset where the customized answers are built with the user’s persona and Wikipedia knowledge. |
Yoonna Jang; Jungwoo Lim; Yuna Hur; Dongsuk Oh; Suhyune Son; Yeonsoo Lee; Donghoon Shin; Seungryong Kim; Heuiseok Lim; |
| 44 | Towards Building ASR Systems for The Next Billion Users Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we make multiple contributions towards building ASR systems for low resource languages from the Indian subcontinent. |
Tahir Javed; Sumanth Doddapaneni; Abhigyan Raman; Kaushal Santosh Bhogale; Gowtham Ramesh; Anoop Kunchukuttan; Pratyush Kumar; Mitesh M. Khapra; |
| 45 | Span-Based Semantic Role Labeling with Argument Pruning and Second-Order Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a framework consisting of two networks: a predicate-agnostic argument pruning network that reduces the number of candidate arguments to O(n), and a semantic role labeling network with an optional second-order decoder that is unfolded from an approximate inference algorithm. |
Zixia Jia; Zhaohui Yan; Haoyi Wu; Kewei Tu; |
| 46 | Incorporating Constituent Syntax for Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a simple yet effective graph-based method to incorporate constituent syntactic structures. |
Fan Jiang; Trevor Cohn; |
| 47 | XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose XLM-K, a cross-lingual language model incorporating multilingual knowledge in pre-training. |
Xiaoze Jiang; Yaobo Liang; Weizhu Chen; Nan Duan; |
| 48 | Hierarchical Context Tagging for Utterance Rewriting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This can occur in languages like English that introduce tokens such as prepositions into the rewrite for grammaticality. We propose a hierarchical context tagger (HCT) that mitigates this issue by predicting slotted rules (e.g., "besides _") whose slots are later filled with context spans. |
Lisa Jin; Linfeng Song; Lifeng Jin; Dong Yu; Daniel Gildea; |
| 49 | Search and Learn: Improving Semantic Coverage for Data-to-Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In other words, important input slots tend to be missing in the generated text. To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage. |
Shailza Jolly; Zi Xuan Zhang; Andreas Dengel; Lili Mou; |
| 50 | Braid: Weaving Symbolic and Neural Knowledge Into Coherent Logical Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. |
Aditya Kalyanpur; Tom Breloff; David A Ferrucci; |
| 51 | Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we investigate whether it is possible to pre-train an audio-text multimodal model with extremely low-resource parallel data and extra non-parallel unimodal data. |
Yu Kang; Tianqiao Liu; Hang Li; Yang Hao; Wenbiao Ding; |
| 52 | Bridging The Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. |
Gaoussou Youssouf Kebe; Luke E. Richards; Edward Raff; Francis Ferraro; Cynthia Matuszek; |
| 53 | ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: As a result, they fail to generate samples with plausible and diverse sentence structures. Motivated by this, we present the data Augmentation using Lexicalized Probabilistic context-free grammars (ALP) that generates augmented samples with diverse syntactic structures with plausible grammar. |
Hazel H. Kim; Daecheol Woo; Seong Joon Oh; Jeong-Won Cha; Yo-Sub Han; |
| 54 | CAISE: Conversational Agent for Image Search and Editing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Thus, we propose a dataset of an automated Conversational Agent for Image Search and Editing (CAISE). |
Hyounghun Kim; Doo Soon Kim; Seunghyun Yoon; Franck Dernoncourt; Trung Bui; Mohit Bansal; |
| 55 | Dual Task Framework for Improving Persona-Grounded Dialogue Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. |
Minju Kim; Beong-woo Kwak; Youngwook Kim; Hong-in Lee; Seung-won Hwang; Jinyoung Yeo; |
| 56 | Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel method that jointly learns volitionality and subject animacy at a low cost, heuristically labeling events in a raw corpus. |
Hirokazu Kiyomaru; Sadao Kurohashi; |
| 57 | From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens (i.e. words or subwords). In this paper, we analyze the impact and utility of such matrices in the context of neural machine translation (NMT). |
Krtin Kumar; Peyman Passban; Mehdi Rezagholizadeh; Yiusing Lau; Qun Liu; |
| 58 | SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. |
Harrison Lee; Raghav Gupta; Abhinav Rastogi; Yuan Cao; Bin Zhang; Yonghui Wu; |
| 59 | Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Intuitively, a model that produces helpful explanations should be more robust against adversarial attacks, because we cannot trust the model that outputs explanations but changes its prediction under small perturbations. To this end, we propose a joint classification and rationale extraction model named AT-BMC. |
Dongfang Li; Baotian Hu; Qingcai Chen; Tujie Xu; Jingcong Tao; Yunan Zhang; |
| 60 | Text Revision By On-the-Fly Representation Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present an iterative in-place editing approach for text revision, which requires no parallel data. |
Jingjing Li; Zichao Li; Tao Ge; Irwin King; Michael R. Lyu; |
| 61 | Unified Named Entity Recognition As Word-Word Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. |
Jingye Li; Hao Fei; Jiang Liu; Shengqiong Wu; Meishan Zhang; Chong Teng; Donghong Ji; Fei Li; |
| 62 | Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we combine the pros and alleviate the cons of both models by proposing a novel Sequence-to-Action (S2A) module. |
Jiquan Li; Junliang Guo; Yongxin Zhu; Xin Sheng; Deqiang Jiang; Bo Ren; Linli Xu; |
| 63 | Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel model named dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which performs explicit and implicit reasoning over a key-value knowledge memory module and a spatial-aware image graph, respectively. |
Mingxiao Li; Marie-Francine Moens; |
| 64 | Knowledge Bridging for Empathetic Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. |
Qintong Li; Piji Li; Zhaochun Ren; Pengjie Ren; Zhumin Chen; |
| 65 | Contrast and Generation Make BART A Good Dialogue Emotion Recognizer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Meanwhile, we utilize an auxiliary response generation task to enhance the model’s ability of handling context information, thereby forcing the model to recognize emotions with similar semantics in diverse contexts. To achieve these objectives, we use the pre-trained encoder-decoder model BART as our backbone model since it is very suitable for both understanding and generation tasks. |
Shimin Li; Hang Yan; Xipeng Qiu; |
| 66 | A Semi-supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While quality labeled dialogue data requires human annotation and is usually expensive to obtain, unlabeled data is easier to collect from various sources. In this paper, we propose a novel semi-supervised teacher-student learning framework to tackle this task. |
Qian Lin; Hwee Tou Ng; |
| 67 | DiffSinger: Singing Voice Synthesis Via Shallow Diffusion Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose DiffSinger, an acoustic model for SVS based on the diffusion probabilistic model. |
Jinglin Liu; Chengxi Li; Yi Ren; Feiyang Chen; Zhou Zhao; |
| 68 | KGR4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, inspired by the process of humans creating sentences, we propose a novel Knowledge-enhanced Commonsense Generation framework, termed KGR4, consisting of four stages: Retrieval, Retrospect, Refine, Rethink. |
Xin Liu; Dayiheng Liu; Baosong Yang; Haibo Zhang; Junwei Ding; Wenqing Yao; Weihua Luo; Haiying Zhang; Jinsong Su; |
| 69 | Improving Biomedical Information Retrieval with Neural Retrievers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. |
Man Luo; Arindam Mitra; Tejas Gokhale; Chitta Baral; |
| 70 | The King Is Naked: On The Notion of Robustness for Natural Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we argue for semantic robustness, which is better aligned with the human concept of linguistic fidelity. |
Emanuele La Malfa; Marta Kwiatkowska; |
| 71 | Selecting Optimal Context Sentences for Event-Event Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce a novel method to better model document-level context with important context sentences for event-event relation extraction. |
Hieu Man; Nghia Trung Ngo; Linh Ngo Van; Thien Huu Nguyen; |
| 72 | Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-Based Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. |
Elman Mansimov; Yi Zhang; |
| 73 | CINS: Comprehensive Instruction for Few-Shot Learning in Task-Oriented Dialog Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To better utilize the power of PLMs, this paper proposes Comprehensive Instruction (CINS) that exploits PLMs with extra task-specific instructions. |
Fei Mi; Yasheng Wang; Yitong Li; |
| 74 | Semantic Self-Segmentation for Abstractive Summarization of Long Documents in Low-Resource Regimes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel semantic self-segmentation (Se3) approach for long document summarization to address the critical problems of low-resource regimes, namely to process inputs longer than the GPU memory capacity and produce accurate summaries despite the availability of only a few dozens of training instances. |
Gianluca Moro; Luca Ragazzi; |
| 75 | Eye of The Beholder: Improved Relation Generalization for Text-Based Reinforcement Learning Agents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While the recent use of text-based resources for increasing an agent’s knowledge and improving its generalization have shown promise, we posit in this paper that there is much yet to be learned from visual representations of these same worlds. Specifically, we propose to retrieve images that represent specific instances of text observations from the world and train our agents on such images. |
Keerthiram Murugesan; Subhajit Chaudhury; Kartik Talamadupula; |
| 76 | Improving Neural Cross-Lingual Abstractive Summarization Via Employing Optimal Transport Distance for Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The matter worsens when performing on languages with separate morphological or structural features, making the cross-lingual alignment more challenging, resulting in the performance drop. To overcome this problem, we propose a novel Knowledge-Distillation-based framework for Cross-Lingual Summarization, seeking to explicitly construct cross-lingual correlation by distilling the knowledge of the monolingual summarization teacher into the cross-lingual summarization student. |
Thong Thanh Nguyen; Anh Tuan Luu; |
| 77 | HiTKG: Towards Goal-Oriented Conversations Via Multi-Hierarchy Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present HiTKG, a hierarchical transformer-based graph walker that leverages multiscale inputs to make precise and flexible predictions on KG paths. |
Jinjie Ni; Vlad Pandelea; Tom Young; Haicang Zhou; Erik Cambria; |
| 78 | Is Discourse Role Important for Emotion Recognition in Conversation? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel method to exploit latent discourse role information of an utterance to determine the emotion it conveys in a conversation. |
Donovan Ong; Jian Su; Bin Chen; Anh Tuan Luu; Ashok Narendranath; Yue Li; Shuqi Sun; Yingzhan Lin; Haifeng Wang; |
| 79 | Improved Text Classification Via Contrastive Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks. |
Lin Pan; Chung-Wei Hang; Avirup Sil; Saloni Potdar; |
| 80 | LeSICiN: A Heterogeneous Graph-Based Approach for Automatic Legal Statute Identification from Indian Legal Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we take the first step towards utilising both the text and the legal citation network for the LSI task. |
Shounak Paul; Pawan Goyal; Saptarshi Ghosh; |
| 81 | Transformer Uncertainty Estimation with Hierarchical Stochastic Attention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel way to enable transformers to have the capability of uncertainty estimation and, meanwhile, retain the original predictive performance. |
Jiahuan Pei; Cheng Wang; György Szarvas; |
| 82 | STEPS: Semantic Typing of Event Processes with A Sequence-to-Sequence Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we advance the field by reformulating the free-form event process typing task as a sequence generation problem and put forward STEPS, an end-to-end approach for producing user intent in terms of actions and objects only, dispensing with the need for their definitions. |
Sveva Pepe; Edoardo Barba; Rexhina Blloshmi; Roberto Navigli; |
| 83 | Sparse Structure Learning Via Graph Neural Networks for Inductive Document Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three challenges:(1) word ambiguity, (2) word synonymity, and (3) dynamic contextual dependency. To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification. |
Yinhua Piao; Sangseon Lee; Dohoon Lee; Sun Kim; |
| 84 | STEM: Unsupervised STructural EMbedding for Stance Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel framework for stance detection. |
Ron Korenblum Pick; Vladyslav Kozhukhov; Dan Vilenchik; Oren Tsur; |
| 85 | ValueNet: A New Dataset for Human Value Driven Dialogue System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. |
Liang Qiu; Yizhou Zhao; Jinchao Li; Pan Lu; Baolin Peng; Jianfeng Gao; Song-Chun Zhu; |
| 86 | Post-OCR Document Correction with Large Ensembles of Character Sequence-to-Sequence Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel method to extend sequence-to-sequence models to accurately process sequences much longer than the ones used during training while being sample- and resource-efficient, supported by thorough experimentation. |
Juan Antonio Ramirez-Orta; Eduardo Xamena; Ana Maguitman; Evangelos Milios; Axel J. Soto; |
| 87 | MuMuQA: Multimedia Multi-Hop News Question Answering Via Cross-Media Knowledge Extraction and Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a new QA evaluation benchmark with 1,384 questions over news articles that require cross-media grounding of objects in images onto text. |
Revant Gangi Reddy; Xilin Rui; Manling Li; Xudong Lin; Haoyang Wen; Jaemin Cho; Lifu Huang; Mohit Bansal; Avirup Sil; Shih-Fu Chang; Alexander Schwing; Heng Ji; |
| 88 | Pushing The Limits of Rule Reasoning in Transformers Through Natural Language Satisfiability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The key idea is to draw insights from empirical sampling of hard propositional SAT problems and from complexity-theoretic studies of language. |
Kyle Richardson; Ashish Sabharwal; |
| 89 | SFSRNet: Super-resolution for Single-Channel Audio Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The problem concerning downsampling is that it usually results in information loss. In this paper, we tackle this problem by introducing SFSRNet which contains a super-resolution (SR) network. |
Joel Rixen; Matthias Renz; |
| 90 | CEM: Commonsense-Aware Empathetic Response Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user’s emotion, cognitive understanding of the user’s situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user’s situation and uses this additional information to further enhance the empathy expression in generated responses. |
Sahand Sabour; Chujie Zheng; Minlie Huang; |
| 91 | Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning Over Text Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hence, we propose Weakly Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. |
Amrita Saha; Shafiq Joty; Steven C.H. Hoi; |
| 92 | Are Vision-Language Transformers Learning Multimodal Representations? A Probing Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we compare pre-trained and fine-tuned representations at a vision, language and multimodal level. |
Emmanuelle Salin; Badreddine Farah; Stéphane Ayache; Benoit Favre; |
| 93 | Entailment Relation Aware Paraphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward entailing, or reverse entailing) with respect to a given input. |
Abhilasha Sancheti; Balaji Vasan Srinivasan; Rachel Rudinger; |
| 94 | Visual Definition Modeling: Challenging Vision & Language Models to Define Words and Objects Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we draw on the established Definition Modeling paradigm and enhance it by grounding, for the first time, textual definitions to visual representations. |
Bianca Scarlini; Tommaso Pasini; Roberto Navigli; |
| 95 | Active Learning on Pre-trained Language Model with Task-Independent Triplet Loss Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a task-independent batch acquisition method using triplet loss to distinguish hard samples in an unlabeled data pool with similar features but difficult to identify labels. |
Seungmin Seo; Donghyun Kim; Youbin Ahn; Kyong-Ho Lee; |
| 96 | OneRel: Joint Entity and Relation Extraction with One Module in One Step Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. |
Yu-Ming Shang; Heyan Huang; Xianling Mao; |
| 97 | KATG: Keyword-Bias-Aware Adversarial Text Generation for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Keyword-bias-aware Adversarial Text Generation model (KATG) that implicitly generates adversarial sentences using a generator-discriminator structure. |
Lingfeng Shen; Shoushan Li; Ying Chen; |
| 98 | Unsupervised Deep Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a novel method for keyphrase generation, AutoKeyGen, without the supervision of any annotated doc-keyphrase pairs. |
Xianjie Shen; Yinghan Wang; Rui Meng; Jingbo Shang; |
| 99 | Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these pre-trained language models have weaker encoding abilities over table cells and schema. To mitigate this issue, in this work, we present an intermediate pre-training framework, Generation-focused Table-based Intermediate Pre-training (GENTAP), that jointly learns representations of natural language questions and tables. |
Peng Shi; Patrick Ng; Feng Nan; Henghui Zhu; Jun Wang; Jiarong Jiang; Alexander Hanbo Li; Rishav Chakravarti; Donald Weidner; Bing Xiang; Zhiguo Wang; |
| 100 | StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a new Question-Answering dataset called StepGame for robust multi-step spatial reasoning in texts. |
Zhengxiang Shi; Qiang Zhang; Aldo Lipani; |
| 101 | MINIMAL: Mining Models for Universal Adversarial Triggers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel data-free approach, MINIMAL, to mine input-agnostic adversarial triggers from models. |
Yaman Kumar Singla; Swapnil Parekh; Somesh Singh; Changyou Chen; Balaji Krishnamurthy; Rajiv Ratn Shah; |
| 102 | Hierarchical Heterogeneous Graph Attention Network for Syntax-Aware Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we essentially incorporate the constituent structure into the single document summarization via the Graph Neural Networks to learn the semantic meaning of tokens. |
Zixing Song; Irwin King; |
| 103 | Supervising Model Attention with Human Explanations for Robust Natural Language Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Natural Language Inference (NLI) models are known to learn from biases and artefacts within their training data, impacting how well they generalise to other unseen datasets. Existing de-biasing approaches focus on preventing the models from learning these biases, which can result in restrictive models and lower performance. |
Joe Stacey; Yonatan Belinkov; Marek Rei; |
| 104 | Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we propose HDAE, a hyperbolic disentangled aspect extractor in which a hyperbolic aspect classifier captures words’ latent hierarchies, and an aspect-disentangled representation models the distinct latent semantics of each seed word. |
Chang-Yu Tai; Ming-Yao Li; Lun-Wei Ku; |
| 105 | Procedural Text Understanding Via Scene-Wise Evolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new scene-wise paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner. |
Jialong Tang; Hongyu Lin; Meng Liao; Yaojie Lu; Xianpei Han; Le Sun; Weijian Xie; Jin Xu; |
| 106 | Debiasing NLU Models Via Causal Intervention and Counterfactual Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we provide a new perspective with causal inference to find out the bias. |
Bing Tian; Yixin Cao; Yong Zhang; Chunxiao Xing; |
| 107 | Chess As A Testbed for Language Model State Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Approximating this full attention results in a significant performance drop. We propose this testbed as a benchmark for future work on the development and analysis of transformer language models. |
Shubham Toshniwal; Sam Wiseman; Karen Livescu; Kevin Gimpel; |
| 108 | Contrast-Enhanced Semi-supervised Text Classification with Few Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a certainty-driven sample selection method and a contrast-enhanced similarity graph to utilize data more efficiently in self-training, alleviating the annotation-starving problem. |
Austin Cheng-Yun Tsai; Sheng-Ya Lin; Li-Chen Fu; |
| 109 | Hybrid Autoregressive Inference for Scalable Multi-Hop Explanation Regeneration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we present SCAR (for Scalable Autoregressive Inference), a hybrid framework that iteratively combines a Transformer-based bi-encoder with a sparse model of explanatory power, designed to leverage explicit inference patterns in the explanations. |
Marco Valentino; Mokanarangan Thayaparan; Deborah Ferreira; André Freitas; |
| 110 | DetIE: Multilingual Open Information Extraction Inspired By Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a different approach to the problem that can be equally or more successful. |
Michael Vasilkovsky; Anton Alekseev; Valentin Malykh; Ilya Shenbin; Elena Tutubalina; Dmitriy Salikhov; Mikhail Stepnov; Andrey Chertok; Sergey Nikolenko; |
| 111 | Hybrid Neural Networks for On-Device Directional Hearing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present DeepBeam, a hybrid model that combines traditional beamformers with a custom lightweight neural net. |
Anran Wang; Maruchi Kim; Hao Zhang; Shyamnath Gollakota; |
| 112 | Non-parametric Online Learning from Human Feedback for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel non-parametric online learning method without changing the model structure. |
Dongqi Wang; Haoran Wei; Zhirui Zhang; Shujian Huang; Jun Xie; Jiajun Chen; |
| 113 | Parameter Differentiation Based Multilingual Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel parameter differentiation based method that allows the model to determine which parameters should be language-specific during training. |
Qian Wang; Jiajun Zhang; |
| 114 | DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel disentangled representation based model DisenCite to automatically generate the citation text through integrating paper text and citation graph. |
Yifan Wang; Yiping Song; Shuai Li; Chaoran Cheng; Wei Ju; Ming Zhang; Sheng Wang; |
| 115 | HEAL: A Knowledge Graph for Distress Management Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, such resources are limited in the context of emotional distress. To address this, we introduce HEAL, a knowledge graph developed based on 1M distress narratives and their corresponding consoling responses curated from Reddit. |
Anuradha Welivita; Pearl Pu; |
| 116 | Deep Fusing Pre-trained Models Into Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework to deep fuse the pre-trained representation into NMT, fully exploring the potential of PTMs in NMT. |
Rongxiang Weng; Heng Yu; Weihua Luo; Min Zhang; |
| 117 | VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce VAST, the Valence-Assessing Semantics Test, a novel intrinsic evaluation task for contextualized word embeddings (CWEs). |
Robert Wolfe; Aylin Caliskan; |
| 118 | A Label Dependence-Aware Sequence Generation Model for Multi-Level Implicit Discourse Relation Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we consider multi-level IDRR as a conditional label sequence generation task and propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. |
Changxing Wu; Liuwen Cao; Yubin Ge; Yang Liu; Min Zhang; Jinsong Su; |
| 119 | Fast and Constrained Absent Keyphrase Generation By Prompt-Based Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a constrained absent keyphrase generation method in a prompt-based learning fashion. |
Huanqin Wu; Baijiaxin Ma; Wei Liu; Tao Chen; Dan Nie; |
| 120 | GraphMemDialog: Optimizing End-to-End Task-Oriented Dialog Systems Using Graph Memory Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Graph Memory Network (GMN) based Seq2Seq model, GraphMemDialog, to effectively learn the inherent structural information hidden in dialog history, and to model the dynamic interaction between dialog history and KBs. |
Jie Wu; Ian G Harris; Hongzhi Zhao; |
| 121 | Mastering The Explicit Opinion-Role Interaction: Syntax-Aided Neural Transition System for Unified Opinion Role Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we investigate a novel solution by revisiting the transition architecture, and augmenting it with a pointer network (PointNet). |
Shengqiong Wu; Hao Fei; Fei Li; Meishan Zhang; Yijiang Liu; Chong Teng; Donghong Ji; |
| 122 | A Graph Convolutional Network with Adaptive Graph Generation and Channel Selection for Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: With this work, we propose a novel graph convolutional method that combines an adaptive graph generation technique and a multi-channel selection strategy. |
Zhipeng Xie; Yumin Tu; |
| 123 | Leashing The Inner Demons: Self-Detoxification for Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on our findings, we propose a simple yet effective unsupervised method for language models to “detoxify” themselves without an additional large corpus or external discriminator. |
Canwen Xu; Zexue He; Zhankui He; Julian McAuley; |
| 124 | Zero-Shot Cross-Lingual Machine Reading Comprehension Via Inter-sentence Dependency Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In our approach, we build the Inter-Sentence Dependency Graph (ISDG) connecting dependency trees to form global syntactic relations across sentences. |
Liyan Xu; Xuchao Zhang; Bo Zong; Yanchi Liu; Wei Cheng; Jingchao Ni; Haifeng Chen; Liang Zhao; Jinho D. Choi; |
| 125 | From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To maintain both task-agnostic and task-specific knowledge in our pruned model, we propose ContrAstive Pruning (CAP) under the paradigm of pre-training and fine-tuning. |
Runxin Xu; Fuli Luo; Chengyu Wang; Baobao Chang; Jun Huang; Songfang Huang; Fei Huang; |
| 126 | Sequence Level Contrastive Learning for Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In text summarization, the output summary is a shorter form of the input document and they have similar meanings. In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as different views of the same mean representation and maximize the similarities between them during training. |
Shusheng Xu; Xingxing Zhang; Yi Wu; Furu Wei; |
| 127 | Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To mitigate the first issue, we propose a novel language model (LM) pretraining task, Knowledge Base Assimilation, to synthesize pretraining data from the edges of a graph of expert- and layman-style medical terminology terms into an LM during self-supervised learning. |
Wenda Xu; Michael Saxon; Misha Sra; William Yang Wang; |
| 128 | Text Is No More Enough! A Benchmark for Profile-Based Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Abstract: Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates … |
Xiao Xu; Libo Qin; Kaiji Chen; Guoxing Wu; Linlin Li; Wanxiang Che; |
| 129 | SAS: Self-Augmentation Strategy for Language Model Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a self-augmentation strategy (SAS) where a single network is utilized for both regular pre-training and contextualized data augmentation for the training in later epochs. |
Yifei Xu; Jingqiao Zhang; Ru He; Liangzhu Ge; Chao Yang; Cheng Yang; Ying Nian Wu; |
| 130 | Hybrid Curriculum Learning for Emotion Recognition in Conversation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance. Motivated by recent studies which have proven that feeding training examples in a meaningful order rather than considering them randomly can boost the performance of models, we propose an ERC-oriented hybrid curriculum learning framework. |
Lin Yang; YI Shen; Yue Mao; Longjun Cai; |
| 131 | NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. |
Linyi Yang; Jiazheng Li; Ruihai Dong; Yue Zhang; Barry Smyth; |
| 132 | Tracing Text Provenance Via Context-Aware Lexical Substitution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the limitations mentioned above, we propose a natural language watermarking scheme based on context-aware lexical substitution (LS). |
Xi Yang; Jie Zhang; Kejiang Chen; Weiming Zhang; Zehua Ma; Feng Wang; Nenghai Yu; |
| 133 | Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, such models would better mimic human-level conversation capabilities. We evaluate two baseline models on this task, including the classification-based two-stage models and the two-in-one fused models. |
Tom Young; Frank Xing; Vlad Pandelea; Jinjie Ni; Erik Cambria; |
| 134 | JAKET: Joint Pre-training of Knowledge Graph and Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. |
Donghan Yu; Chenguang Zhu; Yiming Yang; Michael Zeng; |
| 135 | KID-Review: Knowledge-Guided Scientific Review Generation with Oracle Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we present an end-to-end knowledge-guided review generation framework for scientific papers grounded in cognitive psychology research that a better understanding of text requires different types of knowledge. |
Weizhe Yuan; Pengfei Liu; |
| 136 | Reference-Based Speech Enhancement Via Feature Alignment and Fusion Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Different from them, we observe that the speeches of the same speaker are correlated in terms of frame-level short-time Fourier Transform (STFT) spectrogram. Therefore, we propose reference-based speech enhancement via a feature alignment and fusion network (FAF-Net). |
Huanjing Yue; Wenxin Duo; Xiulian Peng; Jingyu Yang; |
| 137 | MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We are motivated to design a general and robust framework, MDD-Eval, to address the problem. |
Chen Zhang; Luis Fernando D’Haro; Thomas Friedrichs; Haizhou Li; |
| 138 | Efficient Dialog Policy Learning By Reasoning with Contextual Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop a deep reinforcement learning framework for goal-oriented dialog policy learning that learns user preferences from user goal data, while leveraging commonsense knowledge from people. |
Haodi Zhang; Zhichao Zeng; Keting Lu; Kaishun Wu; Shiqi Zhang; |
| 139 | Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a hierarchical cross-modality semantic correlation learning model (HCSCL) to learn the intra- and inter-modal correlation existing in the multimodal data. |
Litian Zhang; Xiaoming Zhang; Junshu Pan; |
| 140 | Adversarial Data Augmentation for Task-Specific Knowledge Distillation of Pre-trained Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose AD^2, a novel and effective data augmentation approach to improving the task-specific knowledge transfer when compressing large pre-trained transformer models. |
Minjia Zhang; Niranjan Uma Naresh; Yuxiong He; |
| 141 | Text-Based Interactive Recommendation Via Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A direct application of policy learning with such fixed experience suffers from the distribution shift. To tackle this issue, we develop a behavior-agnostic off-policy correction framework to make offline interactive recommendation possible. |
Ruiyi Zhang; Tong Yu; Yilin Shen; Hongxia Jin; |
| 142 | DKPLM: Decomposable Knowledge-Enhanced Pre-trained Language Model for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel KEPLM named DKPLM that decomposes knowledge injection process of the pre-trained language models in pre-training, fine-tuning and inference stages, which facilitates the applications of KEPLMs in real-world scenarios. |
Taolin Zhang; Chengyu Wang; Nan Hu; Minghui Qiu; Chengguang Tang; Xiaofeng He; Jun Huang; |
| 143 | Frequency-Aware Contrastive Learning for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we propose a frequency-aware token-level contrastive learning method, in which the hidden state of each decoding step is pushed away from the counterparts of other target words, in a soft contrastive way based on the corresponding word frequencies. |
Tong Zhang; Wei Ye; Baosong Yang; Long Zhang; Xingzhang Ren; Dayiheng Liu; Jinan Sun; Shikun Zhang; Haibo Zhang; Wen Zhao; |
| 144 | Probing Word Syntactic Representations in The Brain By A Feature Elimination Method Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes an alternative framework to study how different word syntactic features are represented in the brain. |
Xiaohan Zhang; Shaonan Wang; Nan Lin; Jiajun Zhang; Chengqing Zong; |
| 145 | Unsupervised Sentence Representation Via Contrastive Learning with Mixing Negatives Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we prove that hard negatives are essential for maintaining strong gradient signals in the training process while random sampling negative examples is ineffective for sentence representation. |
Yanzhao Zhang; Richong Zhang; Samuel Mensah; Xudong Liu; Yongyi Mao; |
| 146 | RetGen: A Joint Framework for Retrieval and Grounded Text Generation Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data where information-relevant documents are provided for context. We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. |
Yizhe Zhang; Siqi Sun; Xiang Gao; Yuwei Fang; Chris Brockett; Michel Galley; Jianfeng Gao; Bill Dolan; |
| 147 | BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce BiRdQA, a bilingual multiple-choice question answering dataset with 6614 English riddles and 8751 Chinese riddles. |
Yunxiang Zhang; Xiaojun Wan; |
| 148 | UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, we introduce a visual guided decoder to better integrate textual and visual modalities in guiding abstractive text generation. |
Zhengkun Zhang; Xiaojun Meng; Yasheng Wang; Xin Jiang; Qun Liu; Zhenglu Yang; |
| 149 | DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: There is still a lack of corresponding research and powerful tools to understand and process such long dialogues. Therefore, in this work, we present a pre-training framework for long dialogue understanding and summarization. |
Ming Zhong; Yang Liu; Yichong Xu; Chenguang Zhu; Michael Zeng; |
| 150 | Idiomatic Expression Paraphrasing Without Strong Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Idiomatic expressions (IEs) play an essential role in natural language. In this paper, we study the task of idiomatic sentence paraphrasing (ISP), which aims to paraphrase a sentence with an IE by replacing the IE with its literal paraphrase. |
Jianing Zhou; Ziheng Zeng; Hongyu Gong; Suma Bhat; |
| 151 | Multilingual Code Snippets Training for Program Translation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce CoST, a new multilingual Code Snippet Translation dataset that contains parallel data from 7 commonly used programming languages. |
Ming Zhu; Karthik Suresh; Chandan K Reddy; |
| 152 | Learning Unseen Emotions from Gestures Via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. |
Abhishek Banerjee; Uttaran Bhattacharya; Aniket Bera; |
| 153 | Optimized Potential Initialization for Low-Latency Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim to achieve high-performance converted SNNs with extremely low latency (fewer than 32 time-steps). |
Tong Bu; Jianhao Ding; Zhaofei Yu; Tiejun Huang; |
| 154 | Planning with Biological Neurons and Synapses Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: A program in this framework essentially sets up a dynamical system of neurons and synapses that eventually, with high probability, accomplishes the task. The purpose of this work is to establish empirically that reasonably large programs in the Assembly Calculus can execute correctly and reliably; and that rather realistic — if idealized — higher cognitive functions, such as planning in the blocks world, can be implemented successfully by such programs. |
Francesco d’Amore; Daniel Mitropolsky; Pierluigi Crescenzi; Emanuele Natale; Christos H. Papadimitriou; |
| 155 | Backprop-Free Reinforcement Learning with Active Neural Generative Coding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose active neural generative coding, a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. |
Alexander G. Ororbia; Ankur Mali; |
| 156 | VECA: A New Benchmark and Toolkit for General Cognitive Development Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present the VECA(Virtual Environment for Cognitive Assessment), which consists of two main components: (i) a first benchmark to assess the overall cognitive development of an AI agent, and (ii) a novel toolkit to generate diverse and distinct cognitive tasks. |
Kwanyoung Park; Hyunseok Oh; Youngki Lee; |
| 157 | Bridging Between Cognitive Processing Signals and Linguistic Features Via A Unified Attentional Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a data-driven method to investigate the relationship between cognitive processing signals and linguistic features. |
Yuqi Ren; Deyi Xiong; |
| 158 | Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the efficient computation of cell assembly in the biological brain, whereby memory-based coding is much more complex than readout, we propose a multiscale dynamic coding improved spiking actor network (MDC-SAN) for reinforcement learning to achieve effective decision-making. |
Duzhen Zhang; Tielin Zhang; Shuncheng Jia; Bo Xu; |
| 159 | Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. |
Niaz Ahmad; Jawad Khan; Jeremy Yuhyun Kim; Youngmoon Lee; |
| 160 | Logic Rule Guided Attribution with Dynamic Ablation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we construct the ‘if-then’ logic rules that are sufficiently precise locally. |
Jianqiao An; Yuandu Lai; Yahong Han; |
| 161 | Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. |
Jinseok Bae; Hojun Jang; Cheol-Hui Min; Hyungun Choi; Young Min Kim; |
| 162 | Deformable Part Region Learning for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a deformable part region learning in order to allow decomposed part regions to be deformable according to geometric transformation of an object. |
Seung-Hwan Bae; |
| 163 | Towards End-to-End Image Compression and Analysis with Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. |
Yuanchao Bai; Xu Yang; Xianming Liu; Junjun Jiang; Yaowei Wang; Xiangyang Ji; Wen Gao; |
| 164 | Handwritten Mathematical Expression Recognition Via Attention Aggregation Based Bi-directional Mutual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an Attention aggregation based Bi-directional Mutual learning Network (ABM) which consists of one shared encoder and two parallel inverse decoders (L2R and R2L). |
Xiaohang Bian; Bo Qin; Xiaozhe Xin; Jianwu Li; Xuefeng Su; Yanfeng Wang; |
| 165 | ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. |
Le Minh Binh; Simon Woo; |
| 166 | LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we discuss a promising solution to the Open-set Long-Tailed Recognition (OLTR) task utilizing metric learning. |
Jiarui Cai; Yizhou Wang; Hung-Min Hsu; Jenq-Neng Hwang; Kelsey Magrane; Craig S Rose; |
| 167 | Prior Gradient Mask Guided Pruning-Aware Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We proposed a Prior Gradient Mask Guided Pruning-aware Fine-Tuning (PGMPF) framework to accelerate deep Convolutional Neural Networks (CNNs). |
Linhang Cai; Zhulin An; Chuanguang Yang; Yangchun Yan; Yongjun Xu; |
| 168 | Context-Aware Transfer Attacks for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a new approach to generate context-aware attacks for object detectors. |
Zikui Cai; Xinxin Xie; Shasha Li; Mingjun Yin; Chengyu Song; Srikanth V. Krishnamurthy; Amit K. Roy-Chowdhury; M. Salman Asif; |
| 169 | OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Herein, we propose a novel out-of-distribution (OoD) HDR image compression framework (OoDHDR-codec). |
Linfeng Cao; Aofan Jiang; Wei Li; Huaying Wu; Nanyang Ye; |
| 170 | Visual Consensus Modeling for Video-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method to mine the commonsense knowledge shared between the video and text modalities for video-text retrieval, namely visual consensus modeling. |
Shuqiang Cao; Bairui Wang; Wei Zhang; Lin Ma; |
| 171 | Proximal PanNet: A Model-Based Deep Network for Pansharpening Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. |
Xiangyong Cao; Yang Chen; Wenfei Cao; |
| 172 | CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper observed that DETR performs surprisingly well even on small objects when measuring Average Precision (AP) at decreased Intersection-over-Union (IoU) thresholds. Motivated by this observation, we propose a simple way to improve DETR by refining the coarse features and predicted locations. |
Xipeng Cao; Peng Yuan; Bailan Feng; Kun Niu; |
| 173 | A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: That is, a CNN has an inductive bias to naturally focus on objects, named as Tobias ("The object is at sight") in this paper. |
Yun-Hao Cao; Jianxin Wu; |
| 174 | Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. |
Seunggyu Chang; Jungchan Cho; Songhwai Oh; |
| 175 | Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. |
Chao Chen; Yibing Zhan; Baosheng Yu; Liu Liu; Yong Luo; Bo Du; |
| 176 | SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We observe that the prevailing set abstraction design for down-sampling points may maintain too much unimportant background information that can affect feature learning for detecting objects. To tackle this issue, we propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA). |
Chen Chen; Zhe Chen; Jing Zhang; Dacheng Tao; |
| 177 | Comprehensive Regularization in A Bi-directional Predictive Network for Video Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As such, we propose a novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. |
Chengwei Chen; Yuan Xie; Shaohui Lin; Angela Yao; Guannan Jiang; Wei Zhang; Yanyun Qu; Ruizhi Qiao; Bo Ren; Lizhuang Ma; |
| 178 | Keypoint Message Passing for Video-Based Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose to overcome the limitations of normal convolutions with a human-oriented graph method. |
Di Chen; Andreas Doering; Shanshan Zhang; Jian Yang; Juergen Gall; Bernt Schiele; |
| 179 | DCAN: Improving Temporal Action Detection Via Dual Context Aggregation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose the end-to-end proposal generation method named Dual Context Aggregation Network (DCAN) to aggregate context on two levels, namely, boundary level and proposal level, for generating high-quality action proposals, thereby improving the performance of temporal action detection. |
Guo Chen; Yin-Dong Zheng; Limin Wang; Tong Lu; |
| 180 | Geometry-Contrastive Transformer for Generalized 3D Pose Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a customized 3D mesh Transformer model for the pose transfer task. |
Haoyu Chen; Hao Tang; Zitong Yu; Nicu Sebe; Guoying Zhao; |
| 181 | Explore Inter-contrast Between Videos Via Composition for Weakly Supervised Temporal Sentence Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most existing methods use the fused visual-linguistic feature to reconstruct the query, where the least reconstruction error determines the target segment. This work introduces a novel approach that explores the inter-contrast between videos in a composed video by selecting components from two different videos and fusing them into a single video. |
Jiaming Chen; Weixin Luo; Wei Zhang; Lin Ma; |
| 182 | Adaptive Image-to-Video Scene Graph Generation Via Knowledge Reasoning and Adversarial Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We tackle the second challenge by hierarchical adversarial learning to reduce the data distribution discrepancy between images and video frames. |
Jin Chen; Xiaofeng Ji; Xinxiao Wu; |
| 183 | Text Gestalt: Stroke-Aware Scene Text Image Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. |
Jingye Chen; Haiyang Yu; Jianqi Ma; Bin Li; Xiangyang Xue; |
| 184 | Towards High-Fidelity Face Self-Occlusion Recovery Via Multi-View Residual-Based GAN Inversion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While recovering face self-occlusions based on 3D face reconstruction, e.g., 3D Morphable Model (3DMM) and its variants provides an effective solution, most of the existing methods show apparent limitations in expressing high-fidelity, natural, and diverse facial details. To overcome these limitations, we propose in this paper a new generative adversarial network (MvInvert) for natural face self-occlusion recovery without using paired image-texture data. |
Jinsong Chen; Hu Han; Shiguang Shan; |
| 185 | ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Particularly, the existing filter-based denoising methods cannot be directly applied to suppress the noise in event stream, since there is no spatial correlation. To address this issue, this paper presents a novel progressive framework, in which a Motion Estimation (ME) module and an Event Denoising (ED) module are jointly optimized in a mutually reinforced manner. |
Jinze Chen; Yang Wang; Yang Cao; Feng Wu; Zheng-Jun Zha; |
| 186 | Attacking Video Recognition Models with Bullet-Screen Comments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Compared with images, attacking videos is much more challenging as it needs to consider not only spatial cues but also temporal cues. To close this gap, we introduce a novel adversarial attack in this paper, the bullet-screen comment (BSC) attack, which attacks video recognition models with BSCs. |
Kai Chen; Zhipeng Wei; Jingjing Chen; Zuxuan Wu; Yu-Gang Jiang; |
| 187 | VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. |
Minghui Chen; Cheng Wen; Feng Zheng; Fengxiang He; Ling Shao; |
| 188 | TransZero: Attribute-Guided Transformer for Zero-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero. |
Shiming Chen; Ziming Hong; Yang Liu; Guo-Sen Xie; Baigui Sun; Hao Li; Qinmu Peng; Ke Lu; Xinge You; |
| 189 | Structured Semantic Transfer for Multi-Label Recognition with Partial Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. |
Tianshui Chen; Tao Pu; Hefeng Wu; Yuan Xie; Liang Lin; |
| 190 | SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To our knowledge, this problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. |
Wei-Ting Chen; I-Hsiang Chen; Chih-Yuan Yeh; Hao-Hsiang Yang; Jian-Jiun Ding; Sy-Yen Kuo; |
| 191 | Imagine By Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Humans can imagine a sample in new poses, scenes and view angles with their prior knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. |
Xiaohua Chen; Yucan Zhou; Dayan Wu; Wanqian Zhang; Yu Zhou; Bo Li; Weiping Wang; |
| 192 | Guide Local Feature Matching By Overlap Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we introduce a novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region. |
Ying Chen; Dihe Huang; Shang Xu; Jianlin Liu; Yong Liu; |
| 193 | Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition. |
Yingjie Chen; Diqi Chen; Tao Wang; Yizhou Wang; Yun Liang; |
| 194 | Deep One-Class Classification Via Interpolated Gaussian Descriptor Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. |
Yuanhong Chen; Yu Tian; Guansong Pang; Gustavo Carneiro; |
| 195 | Towards Ultra-Resolution Neural Style Transfer Via Thumbnail Instance Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000×10000 pixels) style transfer for the first time. |
Zhe Chen; Wenhai Wang; Enze Xie; Tong Lu; Ping Luo; |
| 196 | DeTarNet: Decoupling Translation and Rotation By Siamese Network for Point Cloud Registration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a neural network named DetarNet to decouple the translation t and rotation R, so as to overcome the performance degradation due to their mutual interference in point cloud registration. |
Zhi Chen; Fan Yang; Wenbing Tao; |
| 197 | LCTR: On Awakening The Local Continuity of Transformer for Weakly Supervised Object Localization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. |
Zhiwei Chen; Changan Wang; Yabiao Wang; Guannan Jiang; Yunhang Shen; Ying Tai; Chengjie Wang; Wei Zhang; Liujuan Cao; |
| 198 | Efficient Virtual View Selection for 3D Hand Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a new virtual view selection and fusion module for 3D hand pose estimation from single depth. |
Jian Cheng; Yanguang Wan; Dexin Zuo; Cuixia Ma; Jian Gu; Ping Tan; Hongan Wang; Xiaoming Deng; Yinda Zhang; |
| 199 | Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. |
Ta-Ying Cheng; Hsuan-Ru Yang; Niki Trigoni; Hwann-Tzong Chen; Tyng-Luh Liu; |
| 200 | PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we tackle the cross-domain problem in gaze estimation. |
Yihua Cheng; Yiwei Bao; Feng Lu; |
| 201 | (2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These approaches often ignore the fact that videos are essentially sequences of 2D “views” of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. |
Anoop Cherian; Chiori Hori; Tim K. Marks; Jonathan Le Roux; |
| 202 | Event-Image Fusion Stereo Using Cross-Modality Feature Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a deep network that combines the features of an image with the features of an event to generate a dense disparity map. |
Hoonhee Cho; Kuk-Jin Yoon; |
| 203 | Style-Guided and Disentangled Representation for Robust Image-to-Image Translation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on two ideas, this paper proposes Style-Guided and Disentangled Representation for Robust Image-to-Image Translation (SRIT). |
Jaewoong Choi; Daeha Kim; Byung Cheol Song; |
| 204 | Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the self-training strategy may also suffer from sample selection bias and be impacted by the label noise of the pseudo-labeled samples. In this work, we provide a rigorous theoretical analysis on how these two issues affect the model generalization ability when applying the self-training strategy for the SFUDA problem. |
Tong Chu; Yahao Liu; Jinhong Deng; Wen Li; Lixin Duan; |
| 205 | Model-Based Image Signal Processors Via Learnable Dictionaries Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. |
Marcos V. Conde; Steven McDonagh; Matteo Maggioni; Ales Leonardis; Eduardo Pérez-Pellitero; |
| 206 | MMA: Multi-Camera Based Global Motion Averaging Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a tailor-made multi-camera based motion averaging system, where the fixed relative poses are utilized to improve the accuracy and robustness of SfM. |
Hainan Cui; Shuhan Shen; |
| 207 | GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-fitting. Several prior studies address this issue by expanding the distribution of the limited training data via massive and hand-crafted data augmentation. |
Kaiwen Cui; Jiaxing Huang; Zhipeng Luo; Gongjie Zhang; Fangneng Zhan; Shijian Lu; |
| 208 | Unbiased IoU for Spherical Image Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an unbiased IoU as a novel evaluation criterion for spherical image object detection, which is based on the unbiased representations and utilize unbiased analytical method for IoU calculation. |
Feng Dai; Bin Chen; Hang Xu; Yike Ma; Xiaodong Li; Bailan Feng; Peng Yuan; Chenggang Yan; Qiang Zhao; |
| 209 | InsCLR: Improving Instance Retrieval with Self-Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we identify that the learnt representations for instance retrieval should be invariant to large variations in viewpoint and background etc., whereas self-augmented positives applied by the current SSL methods can not provide strong enough signals for learning robust instance-level representations. To overcome this problem, we propose InsCLR, a new SSL method that builds on the instance-level contrast, to learn the intra-class invariance by dynamically mining meaningful pseudo positive samples from both mini-batches and a memory bank during training. |
Zelu Deng; Yujie Zhong; Sheng Guo; Weilin Huang; |
| 210 | Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recently, many deep learning methods have shown great success in providing model-free solutions to many event-based problems, such as optical flow estimation. |
Ziluo Ding; Rui Zhao; Jiyuan Zhang; Tianxiao Gao; Ruiqin Xiong; Zhaofei Yu; Tiejun Huang; |
| 211 | Construct Effective Geometry Aware Feature Pyramid Network for Multi-Scale Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Geometry-aware Feature Pyramid Network (GaFPN), which mainly consists of the novel Geometry-aware Mapping Module and Geometry-aware Predictor Head.The Geometry-aware Mapping Module is proposed to make full use of all pyramid features to obtain better proposal features by the weight-generation subnetwork. |
Jinpeng Dong; Yuhao Huang; Songyi Zhang; Shitao Chen; Nanning Zheng; |
| 212 | Complementary Attention Gated Network for Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a complementary attention gated network (CAGN) for pedestrian trajectory prediction, in which a dual-path architecture including normal and inverse attention is proposed to capture both frequent and peculiar modals in spatial and temporal patterns, respectively. |
Jinghai Duan; Le Wang; Chengjiang Long; Sanping Zhou; Fang Zheng; Liushuai Shi; Gang Hua; |
| 213 | SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. |
Zhaoxin Fan; Zhenbo Song; Hongyan Liu; Zhiwu Lu; Jun He; Xiaoyong Du; |
| 214 | Backdoor Attacks on The DNN Interpretation System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we design a backdoor attack that alters the saliency map produced by the network for an input image with a specific trigger pattern while not losing the prediction performance significantly. |
Shihong Fang; Anna Choromanska; |
| 215 | Learning to Learn Transferable Attack Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. |
Shuman Fang; Jie Li; Xianming Lin; Rongrong Ji; |
| 216 | Perceptual Quality Assessment of Omnidirectional Images Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we conduct a comprehensive study on the perceptual quality of omnidirectional images from both subjective and objective perspectives. |
Yuming Fang; Liping Huang; Jiebin Yan; Xuelin Liu; Yang Liu; |
| 217 | PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose PatchUp, a hidden state block-level regularization technique for Convolutional Neural Networks (CNNs), that is applied on selected contiguous blocks of feature maps from a random pair of samples. |
Mojtaba Faramarzi; Mohammad Amini; Akilesh Badrinaaraayanan; Vikas Verma; Sarath Chandar; |
| 218 | DuMLP-Pin: A Dual-MLP-Dot-Product Permutation-Invariant Network for Set Feature Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel global aggregation permutation-invariant network based on dual MLP dot-product, called DuMLP-Pin, which is capable of being employed to extract features for set inputs, including unordered or unstructured pixel, attribute, and point cloud data sets. |
Jiajun Fei; Ziyu Zhu; Wenlei Liu; Zhidong Deng; Mingyang Li; Huanjun Deng; Shuo Zhang; |
| 219 | Attention-Aligned Transformer for Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present A2 – an attention-aligned Transformer for image captioning, which guides attention learning in a perturbation-based self-supervised manner, without any annotation overhead. |
Zhengcong Fei; |
| 220 | Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose the first completely automatic model diagnosing and treating tool, termed as Model Doctor. |
Zunlei Feng; Jiacong Hu; Sai Wu; XiaoTian Yu; Jie Song; Mingli Song; |
| 221 | OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. |
Chunyang Fu; Ge Li; Rui Song; Wei Gao; Shan Liu; |
| 222 | DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel task and approach for document-to-slide generation. |
Tsu-Jui Fu; William Yang Wang; Daniel McDuff; Yale Song; |
| 223 | Unsupervised Underwater Image Restoration: From A Homology Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an UnSupervised Underwater Image Restoration method (USUIR) by leveraging the homology property between a raw underwater image and a re-degraded image. |
Zhenqi Fu; Huangxing Lin; Yan Yang; Shu Chai; Liyan Sun; Yue Huang; Xinghao Ding; |
| 224 | Playing Lottery Tickets with Vision and Language Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In parallel, work on the lottery ticket hypothesis (LTH) has shown that deep neural networks contain small matching subnetworks that can achieve on par or even better performance than the dense networks when trained in isolation. In this work, we perform the first empirical study to assess whether such trainable subnetworks also exist in pre-trained VL models. |
Zhe Gan; Yen-Chun Chen; Linjie Li; Tianlong Chen; Yu Cheng; Shuohang Wang; Jingjing Liu; Lijuan Wang; Zicheng Liu; |
| 225 | Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Meanwhile, how to take full advantage of the intermediate features under the constraints of limited parameters and calculations is also a huge challenge. To alleviate these issues, we propose a lightweight yet efficient Feature Distillation Interaction Weighted Network (FDIWN). |
Guangwei Gao; Wenjie Li; Juncheng Li; Fei Wu; Huimin Lu; Yi Yu; |
| 226 | Weakly-Supervised Salient Object Detection Using Point Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel weakly-supervised salient object detection method using point supervision. |
Shuyong Gao; Wei Zhang; Yan Wang; Qianyu Guo; Chenglong Zhang; Yangji He; Wenqiang Zhang; |
| 227 | Latent Space Explanation By Intervention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This study aims to reveal hidden concepts by employing an intervention mechanism that shifts the predicted class based on discrete variational autoencoders. |
Itai Gat; Guy Lorberbom; Idan Schwartz; Tamir Hazan; |
| 228 | Lifelong Person Re-identification By Pseudo Task Knowledge Preservation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The model tends to learn task-specific knowledge with task-wise domain gap, which results in stability and plasticity dilemma. To overcome this problem, we cast LReID as a domain adaptation problem and propose a pseudo task knowledge preservation framework to alleviate the domain gap. |
Wenhang Ge; Junlong Du; Ancong Wu; Yuqiao Xian; Ke Yan; Feiyue Huang; Wei-Shi Zheng; |
| 229 | Adversarial Robustness in Multi-Task Learning: Promises and Illusions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we evaluate the design choices that impact the robustness of multi-task deep learning networks. |
Salah Ghamizi; Maxime Cordy; Mike Papadakis; Yves Le Traon; |
| 230 | Deep Confidence Guided Distance for 3D Partial Shape Registration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel non-iterative learnable method for partial-to-partial 3D shape registration. |
Dvir Ginzburg; Dan Raviv; |
| 231 | Predicting Physical World Destinations for Commands Given to Self-Driving Cars Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In an attempt to alleviate this issue, recent works have taken a natural language-oriented approach by allowing the passenger to give commands that refer to specific objects in the visual scene. Nevertheless, this is only half the task as the car should also understand the physical destination of the command, which is what we focus on in this paper. |
Dusan Grujicic; Thierry Deruyttere; Marie-Francine Moens; Matthew B. Blaschko; |
| 232 | Towards Light-Weight and Real-Time Line Segment Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). |
Geonmo Gu; Byungsoo Ko; SeoungHyun Go; Sung-Hyun Lee; Jingeun Lee; Minchul Shin; |
| 233 | Exploiting Fine-Grained Face Forgery Clues Via Progressive Enhancement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the exploitation of frequency information is coarse-grained, and more importantly, their vanilla learning process struggles to extract fine-grained forgery traces. To address this issue, we propose a progressive enhancement learning framework to exploit both the RGB and fine-grained frequency clues. |
Qiqi Gu; Shen Chen; Taiping Yao; Yang Chen; Shouhong Ding; Ran Yi; |
| 234 | Delving Into The Local: Dynamic Inconsistency Learning for DeepFake Video Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these works impose supervisions on sparsely sampled video frames but overlook the local mo- tions among adjacent frames, which instead encode rich in- consistency information that can serve as an efficient indica- tor for DeepFake video detection. To mitigate this issue, we delves into the local motion and propose a novel sampling unit named snippet which contains a few successive videos frames for local temporal inconsistency learning. |
Zhihao Gu; Yang Chen; Taiping Yao; Shouhong Ding; Jilin Li; Lizhuang Ma; |
| 235 | Assessing A Single Image in Reference-Guided Image Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a general learning-based framework, Reference-guided Image Synthesis Assessment (RISA) to quantitatively evaluate the quality of a single generated image. |
Jiayi Guo; Chaoqun Du; Jiangshan Wang; Huijuan Huang; Pengfei Wan; Gao Huang; |
| 236 | Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR) is proposed. |
Tianyu Guo; Hong Liu; Zhan Chen; Mengyuan Liu; Tao Wang; Runwei Ding; |
| 237 | Convolutional Neural Network Compression Through Generalized Kronecker Product Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we reduce memory usage and floating-point operations required by convolutional layers in CNNs. |
Marawan Gamal Abdel Hameed; Marzieh S. Tahaei; Ali Mosleh; Vahid Partovi Nia; |
| 238 | Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection. |
Guangxing Han; Shiyuan Huang; Jiawei Ma; Yicheng He; Shih-Fu Chang; |
| 239 | Delving Into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an approach named probabilistic uncertainty guided progressive label refinery (P2LR) for domain adaptive person re-identification. |
Jian Han; Ya-Li Li; Shengjin Wang; |
| 240 | Laneformer: Object-Aware Row-Column Transformers for Lane Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving. |
Jianhua Han; Xiajun Deng; Xinyue Cai; Zhen Yang; Hang Xu; Chunjing Xu; Xiaodan Liang; |
| 241 | Modify Self-Attention Via Skeleton Decomposition for Effective Point Cloud Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel skeleton decomposition-based self-attention (SD-SA) which has no sequence length limit and exhibits favorable scalability in long-sequence models. |
Jiayi Han; Longbin Zeng; Liang Du; Xiaoqing Ye; Weiyang Ding; Jianfeng Feng; |
| 242 | Generalizable Person Re-identification Via Self-Supervised Batch Norm Test-Time Adaption Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. |
Ke Han; Chenyang Si; Yan Huang; Liang Wang; Tieniu Tan; |
| 243 | RRL: Regional Rotate Layer in Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These methods either increase the workload of training or increase the number of model parameters. To address this problem, this paper proposes a module that can be inserted into the existing networks, and directly incorporates the rotation invariance into the feature extraction layers of the CNNs. |
Zongbo Hao; Tao Zhang; Mingwang Chen; Zou Kaixu; |
| 244 | QueryProp: Object Query Propagation for High-Performance Video Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper argues that with a more effective and efficient feature propagation framework, video object detectors can gain improvement in terms of both accuracy and speed. |
Fei He; Naiyu Gao; Jian Jia; Xin Zhao; Kaiqi Huang; |
| 245 | Flow-Based Unconstrained Lip to Speech Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although these methods have achieved promising performance, they are prone to bring issues including high inference latency and mel-spectrogram over-smoothness. To tackle these problems, we propose a novel flow-based non-autoregressive lip-to-speech model (GlowLTS) to break autoregressive constraints and achieve faster inference. |
Jinzheng He; Zhou Zhao; Yi Ren; Jinglin Liu; Baoxing Huai; Nicholas Yuan; |
| 246 | TransFG: A Transformer Architecture for Fine-Grained Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most existing works mainly tackle this problem by reusing the backbone network to extract features of detected discriminative regions. |
Ju He; Jie-Neng Chen; Shuai Liu; Adam Kortylewski; Cheng Yang; Yutong Bai; Changhu Wang; |
| 247 | Self-Supervised Robust Scene Flow Estimation Via The Alignment of Probability Density Functions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. |
Pan He; Patrick Emami; Sanjay Ranka; Anand Rangarajan; |
| 248 | SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. |
Qingdong He; Zhengning Wang; Hao Zeng; Yi Zeng; Yijun Liu; |
| 249 | SECRET: Self-Consistent Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We argue that the consistency between different feature spaces is the key to the pseudo labels’ quality. |
Tao He; Leqi Shen; Yuchen Guo; Guiguang Ding; Zhenhua Guo; |
| 250 | Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arbitrary shape scene text. To address this issue, we make the first attempt to perform textual reasoning based on visual semantics in this paper. |
Yue He; Chen Chen; Jing Zhang; Juhua Liu; Fengxiang He; Chaoyue Wang; Bo Du; |
| 251 | Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning Via Ranked Positives Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. |
David T. Hoffmann; Nadine Behrmann; Juergen Gall; Thomas Brox; Mehdi Noroozi; |
| 252 | Uncertainty-Driven Dehazing Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. |
Ming Hong; Jianzhuang Liu; Cuihua Li; Yanyun Qu; |
| 253 | Shadow Generation for Composite Image in Real-World Scenes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we focus on generating plausible shadow for the foreground object in the composite image. |
Yan Hong; Li Niu; Jianfu Zhang; |
| 254 | Shape-Adaptive Selection and Measurement for Oriented Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples. |
Liping Hou; Ke Lu; Jian Xue; Yuqiu Li; |
| 255 | H^2-MIL: Exploring Hierarchical Representation with Heterogeneous Multiple Instance Learning for Whole Slide Image Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a novel graph neural network-based multiple instance learning framework (i.e., H^2-MIL) to learn hierarchical representation from a heterogeneous graph with different resolutions for WSI analysis. |
Wentai Hou; Lequan Yu; Chengxuan Lin; Helong Huang; Rongshan Yu; Jing Qin; Liansheng Wang; |
| 256 | Elastic-Link for Binarized Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an “Elastic-Link” (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. |
Jie Hu; Ziheng Wu; Vince Tan; Zhilin Lu; Mengze Zeng; Enhua Wu; |
| 257 | FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a frame inference-based detection framework (FInfer) to solve the problem of high-visual-quality Deepfake detection. |
Juan Hu; Xin Liao; Jinwen Liang; Wenbo Zhou; Zheng Qin; |
| 258 | Bi-volution: A Static and Dynamic Coupled Filter Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, SOTA dynamic convolution operators are sensitive to input noises (e.g., Gaussian noise, shot noise, e.t.c.) and lack sufficient spatial contextual information in filter generation. To alleviate this inherent weakness, we propose a lightweight and heterogeneous-structure (i.e., static and dynamic) operator, named Bi-volution. |
Xiwei Hu; Xuanhong Chen; Bingbing Ni; Teng Li; Yutian Liu; |
| 259 | AFDetV2: Rethinking The Necessity of The Second Stage for Object Detection from Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this scenario, the second stage mainly rescores the boxes such that the boxes with better localization get selected. From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements. |
Yihan Hu; Zhuangzhuang Ding; Runzhou Ge; Wenxin Shao; Li Huang; Kun Li; Qiang Liu; |
| 260 | Divide-and-Regroup Clustering for Domain Adaptive Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, the temporal continuity prior is beneficial, because it offers clue for distinguishing some look-alike person (who are temporally far away from each other). These two insight motivate us to propose a novel Divide-And-Regroup Clustering (DARC) pipeline for re-ID UDA. |
Zhengdong Hu; Yifan Sun; Yi Yang; Jianguang Zhou; |
| 261 | CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Despite achieving impressive results, these adversarial watermarks have low image-level and model-level transferability, meaning that they can protect only one facial image from one specific deepfake model. To address these issues, we propose a novel solution that can generate a Cross-Model Universal Adversarial Watermark (CMUA-Watermark), protecting a large number of facial images from multiple deepfake models. |
Hao Huang; Yongtao Wang; Zhaoyu Chen; Yuze Zhang; Yuheng Li; Zhi Tang; Wei Chu; Jingdong Chen; Weisi Lin; Kai-Kuang Ma; |
| 262 | Deconfounded Visual Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. |
Jianqiang Huang; Yu Qin; Jiaxin Qi; Qianru Sun; Hanwang Zhang; |
| 263 | Learning to Model Pixel-Embedded Affinity for Homogeneous Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a pixel-embedded affinity modeling method for homogeneous instance segmentation, which is able to preserve the semantic information of instances and improve the distinguishability of adjacent instances. |
Wei Huang; Shiyu Deng; Chang Chen; Xueyang Fu; Zhiwei Xiong; |
| 264 | Channelized Axial Attention – Considering Channel Relation Within Spatial Attention for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. |
Ye Huang; Di Kang; Wenjing Jia; Liu Liu; Xiangjian He; |
| 265 | UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det). |
Yecheng Huang; Jiaxin Chen; Di Huang; |
| 266 | Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person re-identification towards learning modality-invariant and discriminative representations. |
Zhipeng Huang; Jiawei Liu; Liang Li; Kecheng Zheng; Zheng-Jun Zha; |
| 267 | MuMu: Cooperative Multitask Learning-Based Guided Multimodal Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a cooperative multitask learning-based guided multimodal fusion approach, MuMu, to extract robust multimodal representations for human activity recognition (HAR). |
Md Mofijul Islam; Tariq Iqbal; |
| 268 | An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose the concept of local activation, and devise a metric on the local activation to detect artifact generations without additional supervision. |
Haedong Jeong; Jiyeon Han; Jaesik Choi; |
| 269 | FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, we design a framework to generalize the deepfake detector for both the known and unseen GAN models. |
Yonghyun Jeong; Doyeon Kim; Youngmin Ro; Jongwon Choi; |
| 270 | Learning Disentangled Attribute Representations for Robust Pedestrian Attribute Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, this mechanism leads to low-confidence predictions and non-robustness of the model in the inference stage. In this paper, we investigate why this is the case. |
Jian Jia; Naiyu Gao; Fei He; Xiaotang Chen; Kaiqi Huang; |
| 271 | Degrade Is Upgrade: Learning Degradation for Low-Light Image Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by the color image formulation (diffuse illumination color plus environment illumination color), we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. To this end, we propose a novel Degradation-to-Refinement Generation Network (DRGN). |
Kui Jiang; Zhongyuan Wang; Zheng Wang; Chen Chen; Peng Yi; Tao Lu; Chia-Wen Lin; |
| 272 | HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we concentrate on handling both local and global drifts and introduce a new harmonizing framework called HarmoFL. |
Meirui Jiang; Zirui Wang; Qi Dou; |
| 273 | Coarse-to-Fine Generative Modeling for Graphic Layouts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we seek to improve the performance of layout generation by incorporating the concept of regions, which consist of a smaller number of elements and appears like a simple layout, into the generation process. |
Zhaoyun Jiang; Shizhao Sun; Jihua Zhu; Jian-Guang Lou; Dongmei Zhang; |
| 274 | DarkVisionNet: Low-Light Imaging Via RGB-NIR Fusion with Deep Inconsistency Prior Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP). |
Shuangping Jin; Bingbing Yu; Minhao Jing; Yi Zhou; Jiajun Liang; Renhe Ji; |
| 275 | LAGConv: Local-Context Adaptive Convolution Kernels with Global Harmonic Bias for Pansharpening Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel strategy to generate local-context adaptive (LCA) convolution kernels and introduce a new global harmonic (GH) bias mechanism, exploiting image local specificity as well as integrating global information, dubbed LAGConv. |
Zi-Rong Jin; Tian-Jing Zhang; Tai-Xiang Jiang; Gemine Vivone; Liang-Jian Deng; |
| 276 | Learning The Dynamics of Visual Relational Reasoning Via Reinforced Path Routing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to learn the reasoning dynamics of visual relational reasoning by casting it as a path routing task. |
Chenchen Jing; Yunde Jia; Yuwei Wu; Chuanhao Li; Qi Wu; |
| 277 | Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most existing approaches do not explicitly embed the high-order spatio-temporal importance to joints’ spatial connection topology and intensity, and they do not have direct objectives on their attention module to jointly learn when and where to focus on in the action sequence. To address these problems, we propose the To-a-T Spatio-Temporal Focus (STF), a skeleton-based action recognition framework that utilizes the spatio-temporal gradient to focus on relevant spatio-temporal features. |
Lipeng Ke; Kuan-Chuan Peng; Siwei Lyu; |
| 278 | MODNet: Real-Time Trimap-Free Portrait Matting Via Objective Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present a light-weight matting objective decomposition network (MODNet) for portrait matting in real-time with a single input image. |
Zhanghan Ke; Jiayu Sun; Kaican Li; Qiong Yan; Rynson W.H. Lau; |
| 279 | Learning Mixture of Domain-Specific Experts Via Disentangled Factors for Autonomous Driving Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, the problem in behavior cloning is divided into several domain-specific subspaces, with experts becoming specialized on each domain-specific policy. |
Inhan Kim; Joonyeong Lee; Daijin Kim; |
| 280 | Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a versatile pedestrian detector that shows robust detection performance in any single modality. |
Jung Uk Kim; Sungjune Park; Yong Man Ro; |
| 281 | Semantic Feature Extraction for Generalized Zero-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we put forth a new GZSL technique that improves the GZSL classification performance greatly. |
Junhan Kim; Kyuhong Shim; Byonghyo Shim; |
| 282 | Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we try to alleviate the aforementioned two challenges in lip reading by proposing a Multi-head Visual-audio Memory (MVM). |
Minsu Kim; Jeong Hun Yeo; Yong Man Ro; |
| 283 | Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. |
Sunwoo Kim; Soohyun Kim; Seungryong Kim; |
| 284 | PrivateSNN: Privacy-Preserving Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive information contained in a dataset. |
Youngeun Kim; Yeshwanth Venkatesha; Priyadarshini Panda; |
| 285 | NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. |
Yujin Kim; Dogyun Park; Dohee Kim; Suhyun Kim; |
| 286 | Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. |
Junho Koh; Jaekyum Kim; Jin Hyeok Yoo; Yecheol Kim; Dongsuk Kum; Jun Won Choi; |
| 287 | Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination. Motivated by this, we propose a novel feature space Amplitude Spectrum Transformation (AST). |
Jogendra Nath Kundu; Akshay R Kulkarni; Suvaansh Bhambri; Varun Jampani; Venkatesh Babu Radhakrishnan; |
| 288 | Siamese Network with Interactive Transformer for Video Object Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames. |
Meng Lan; Jing Zhang; Fengxiang He; Lefei Zhang; |
| 289 | Adversarial Attack for Asynchronous Event-Based Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our algorithm achieves an attack success rate of 97.95% on the N-Caltech101 dataset. |
Wooju Lee; Hyun Myung; |
| 290 | Iteratively Selecting An Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In our paper, we propose Easy Frame Selector (EFS). |
Youngjo Lee; Hongje Seong; Euntai Kim; |
| 291 | SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds. |
Bing Li; Cheng Zheng; Silvio Giancola; Bernard Ghanem; |
| 292 | Shrinking Temporal Attention in Transformers for Video Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a Shrinking Temporal Attention Transformer (STAT), which efficiently builts spatiotemporal attention maps considering the attenuation of spatial attention in short and long temporal sequences. |
Bonan Li; Pengfei Xiong; Congying Han; Tiande Guo; |
| 293 | DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we reformulate it by a two-stage process, i.e., a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligned movements. |
Buyu Li; Yongchi Zhao; Shi Zhelun; Lu Sheng; |
| 294 | Interpretable Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a generic method to modify a traditional GAN into an interpretable GAN, which ensures that filters in an intermediate layer of the generator encode disentangled localized visual concepts. |
Chao Li; Kelu Yao; Jin Wang; Boyu Diao; Yongjun Xu; Quanshi Zhang; |
| 295 | Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we address the cross-modal object tracking problem and contribute a new video dataset, including 654 cross-modal image sequences with over 481K frames in total, and the average video length is more than 735 frames. |
Chenglong Li; Tianhao Zhu; Lei Liu; Xiaonan Si; Zilin Fan; Sulan Zhai; |
| 296 | You Only Infer Once: Cross-Modal Meta-Transfer for Referring Video Object Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present YOFO (You Only inFer Once), a new paradigm for referring video object segmentation (RVOS) that operates in an one-stage manner. |
Dezhuang Li; Ruoqi Li; Lijun Wang; Yifan Wang; Jinqing Qi; Lu Zhang; Ting Liu; Qingquan Xu; Huchuan Lu; |
| 297 | Knowledge Distillation for Object Detection Via Rank Mimicking and Prediction-Guided Feature Imitation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we elaborately study the behaviour difference between the teacher and student detection models, and obtain two intriguing observations: First, the teacher and student rank their detected candidate boxes quite differently, which results in their precision discrepancy. |
Gang Li; Xiang Li; Yujie Wang; Shanshan Zhang; Yichao Wu; Ding Liang; |
| 298 | Rethinking Pseudo Labels for Semi-supervised Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce certainty-aware pseudo labels tailored for object detection, which can effectively estimate the classification and localization quality of derived pseudo labels. |
Hengduo Li; Zuxuan Wu; Abhinav Shrivastava; Larry S. Davis; |
| 299 | Action-Aware Embedding Enhancement for Image-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Action-aware Memory-Enhanced embedding (AME) method for image-text retrieval, which aims to emphasize the action information when mapping the images and texts into a shared embedding space. |
Jiangtong Li; Li Niu; Liqing Zhang; |
| 300 | Retinomorphic Object Detection in Asynchronous Visual Streams Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel problem setting, retinomorphic object detection, which is the first trial that integrates foveal-like and peripheral-like visual streams. |
Jianing Li; Xiao Wang; Lin Zhu; Jia Li; Tiejun Huang; Yonghong Tian; |
| 301 | Learning from Weakly-Labeled Web Videos Via Exploring Sub-concepts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, for video action recognition, the action of interest might only exist in arbitrary clips of untrimmed web videos, resulting in high label noises in the temporal space. To address this challenge, we introduce a new method for pre-training video action recognition models using queried web videos. |
Kunpeng Li; Zizhao Zhang; Guanhang Wu; Xuehan Xiong; Chen-Yu Lee; Zhichao Lu; Yun Fu; Tomas Pfister; |
| 302 | Learning Universal Adversarial Perturbation By Adversarial Example Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The existing universal attack methods fail to exploit the differences and connections between the instance and universal levels to produce dominant perturbations. To address this challenge, we propose a new universal attack method that unifies instance-specific and universal attacks from a feature perspective to generate a more dominant UAP. |
Maosen Li; Yanhua Yang; Kun Wei; Xu Yang; Heng Huang; |
| 303 | Logit Perturbation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on a unified viewpoint between positive/negative data augmentation and loss variations incurred by logit perturbation, a new method is proposed to explicitly learn to perturb logits. |
Mengyang Li; Fengguang Su; Ou Wu; Ji Zhang; |
| 304 | Neighborhood-Adaptive Structure Augmented Metric Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By exploiting the heterogeneity of local structures in the embedding space, we propose a Neighborhood-Adaptive Structure Augmented metric learning framework (NASA), where the neighborhood structure is realized as a structure embedding, and learned along with the sample embedding in a self-supervised manner. |
Pandeng Li; Yan Li; Hongtao Xie; Lei Zhang; |
| 305 | Stereo Neural Vernier Caliper Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a new object-centric framework for learning-based stereo 3D object detection. |
Shichao Li; Zechun Liu; Zhiqiang Shen; Kwang-Ting Cheng; |
| 306 | EditVAE: Unsupervised Parts-Aware Controllable 3D Point Cloud Shape Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In particular, we introduce a latent representation of the point cloud which can be decomposed into a disentangled representation for each part of the shape. |
Shidi Li; Miaomiao Liu; Christian Walder; |
| 307 | Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the inference stage, we propose to use the video-level anomaly probability to suppress the fluctuation of snippet-level anomaly scores. |
Shuo Li; Fang Liu; Licheng Jiao; |
| 308 | TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects — action duration misalignment and action evolution misalignment. |
Shuyuan Li; Huabin Liu; Rui Qian; Yuxi Li; John See; Mengjuan Fei; Xiaoyuan Yu; Weiyao Lin; |
| 309 | Best-Buddy GANs for Highly Detailed Image Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Besides, we propose a region-aware adversarial learning strategy that directs our model to focus on generating details for textured areas adaptively. |
Wenbo Li; Kun Zhou; Lu Qi; Liying Lu; Jiangbo Lu; |
| 310 | SCAN: Cross Domain Object Detection with Semantic Conditioned Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, the category-agnostic alignment leads to the disagreement of class-specific distributions in the two domains, further causing inevitable classification errors. To overcome these two challenges, we propose a novel Semantic Conditioned AdaptatioN (SCAN) framework such that well-modeled unbiased semantics can support semantic conditioned adaptation for precise domain adaptive object detection. |
Wuyang Li; Xinyu Liu; Xiwen Yao; Yixuan Yuan; |
| 311 | Hybrid Instance-Aware Temporal Fusion for Online Video Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an online video instance segmentation framework with a novel instance-aware temporal fusion method. |
Xiang Li; Jinglu Wang; Xiao Li; Yan Lu; |
| 312 | Close The Loop: A Unified Bottom-Up and Top-Down Paradigm for Joint Image Deraining and Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we focus on a very practical problem: image segmentation under rain conditions. |
Yi Li; Yi Chang; Changfeng Yu; Luxin Yan; |
| 313 | Uncertainty Estimation Via Response Scaling for Pseudo-Mask Noise Mitigation in Weakly-Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Thus, in this paper, we simulate noisy variations of response by scaling the prediction map in multiple times for uncertainty estimation. |
Yi Li; Yiqun Duan; Zhanghui Kuang; Yimin Chen; Wayne Zhang; Xiaomeng Li; |
| 314 | Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel Multi-modal Perception Tracker (MPT) for speaker tracking using both audio and visual modalities. |
Yidi Li; Hong Liu; Hao Tang; |
| 315 | Defending Against Model Stealing Via Verifying Embedded External Features Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified external features. |
Yiming Li; Linghui Zhu; Xiaojun Jia; Yong Jiang; Shu-Tao Xia; Xiaochun Cao; |
| 316 | Towards An Effective Orthogonal Dictionary Convolution Strategy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we propose a novel Orthogonal Dictionary Convolution Strategy (ODCS) on CNNs to improve orthogonality effect by optimizing the network architecture and changing the regularized object. |
Yishi Li; Kunran Xu; Rui Lai; Lin Gu; |
| 317 | ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper describes an energy-based learning method that predicts the activities of multiple agents simultaneously. |
Yuke Li; Pin Wang; Lixiong Chen; Zheng Wang; Ching-Yao Chan; |
| 318 | Equal Bits: Enforcing Equally Distributed Binary Network Weights Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Here, we show that quantizing using optimal transport can guarantee any bit ratio, including equal ratios. |
Yunqiang Li; Silvia-Laura Pintea; Jan C van Gemert; |
| 319 | SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-training for Spatial-Aware Visual Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Due to the discrepancy between the two-dimensional image plane and the three-dimensional space, such pre-trained models fail to perceive spatial information and serve as sub-optimal solutions for 3D-related tasks. To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks. |
Zhenyu Li; Zehui Chen; Ang Li; Liangji Fang; Qinhong Jiang; Xianming Liu; Junjun Jiang; Bolei Zhou; Hang Zhao; |
| 320 | Improving Human-Object Interaction Detection Via Phrase Learning and Label Composition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose PhraseHOI, containing a HOI branch and a novel phrase branch, to leverage language prior and improve relation expression. |
Zhimin Li; Cheng Zou; Yu Zhao; Boxun Li; Sheng Zhong; |
| 321 | Rethinking The Optimization of Average Precision: Only Penalizing Negative Instances Before Positive Ones Is Enough Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, we claim that only penalizing negative instances before positive ones is enough, because the loss only comes from these negative instances. To this end, we propose a novel loss, namely Penalizing Negative instances before Positive ones (PNP), which can directly minimize the number of negative instances before each positive one. |
Zhuo Li; Weiqing Min; Jiajun Song; Yaohui Zhu; Liping Kang; Xiaoming Wei; Xiaolin Wei; Shuqiang Jiang; |
| 322 | Reliability Exploration with Self-Ensemble Learning for Domain Adaptive Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Reliability Exploration with Self-ensemble Learning (RESL) framework for domain adaptive person ReID. |
Zongyi Li; Yuxuan Shi; Hefei Ling; Jiazhong Chen; Qian Wang; Fengfan Zhou; |
| 323 | Deconfounding Physical Dynamics with Global Causal Relation and Confounder Transmission for Counterfactual Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we work on the confounders that have effect on the physical dynamics, including masses, friction coefficients, etc., to bridge relations between the intervened variable and the affected variable whose future state may be altered. |
Zongzhao Li; Xiangyu Zhu; Zhen Lei; Zhaoxiang Zhang; |
| 324 | One More Check: Making “Fake Background” Be Tracked Again Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we set out to restore the bounding boxes misclassified as “fake background” by proposing a re-check network. |
Chao Liang; Zhipeng Zhang; Xue Zhou; Bing Li; Weiming Hu; |
| 325 | Semantically Contrastive Learning for Low-Light Image Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we respond to the intriguing learning-related question — if leveraging both accessible unpaired over/underexposed images and high-level semantic guidance, can improve the performance of cutting-edge LLE models? |
Dong Liang; Ling Li; Mingqiang Wei; Shuo Yang; Liyan Zhang; Wenhan Yang; Yun Du; Huiyu Zhou; |
| 326 | Self-Supervised Spatiotemporal Representation Learning By Exploiting Video Continuity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. |
Hanwen Liang; Niamul Quader; Zhixiang Chi; Lizhe Chen; Peng Dai; Juwei Lu; Yang Wang; |
| 327 | Inharmonious Region Localization By Magnifying Domain Discrepancy Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we tend to transform the input image to another color space to magnify the domain discrepancy between inharmonious region and background, so that the model can identify the inharmonious region more easily. |
Jing Liang; Li Niu; Penghao Wu; Fengjun Guo; Teng Long; |
| 328 | Distribution Aware VoteNet for 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we revise the common regression method by predicting the distribution of the 3D box and then present a distribution-aware regression (DAR) module for box refinement and localization quality estimation. |
Junxiong Liang; Pei An; Jie Ma; |
| 329 | Contrastive Instruction-Trajectory Learning for Vision-Language Navigation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a Contrastive Instruction-Trajectory Learning (CITL) framework that explores invariance across similar data samples and variance across different ones to learn distinctive representations for robust navigation. |
Xiwen Liang; Fengda Zhu; Yi Zhu; Bingqian Lin; Bing Wang; Xiaodan Liang; |
| 330 | Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: From the viewpoint of causal inference, such bag contextual prior works as a confounder and may result in model robustness and interpretability issues. Focusing on this problem, we propose a novel interventional multi-instance learning (IMIL) framework to achieve deconfounded instance-level prediction. |
Tiancheng Lin; Hongteng Xu; Canqian Yang; Yi Xu; |
| 331 | A Causal Debiasing Framework for Unsupervised Salient Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Spatial distribution bias means that the position distribution of all salient objects in a dataset is concentrated on the center of the image plane, which could be harmful to off-center objects prediction. This paper proposes a causal based debiasing framework to disentangle the model from the impact of such biases. |
Xiangru Lin; Ziyi Wu; Guanqi Chen; Guanbin Li; Yizhou Yu; |
| 332 | A Causal Inference Look at Unsupervised Video Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Unsupervised video anomaly detection, a task that requires no labeled normal/abnormal training data in any form, is challenging yet of great importance to both industrial … |
Xiangru Lin; Yuyang Chen; Guanbin Li; Yizhou Yu; |
| 333 | Unpaired Multi-Domain Stain Transfer for Kidney Histopathological Images Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, due to the interference of colors among multiple stains, it is not easy to perform multiple staining simultaneously on one biological tissue. To address this problem, we propose a network based on unpaired training data to virtually generate multiple types of staining from one staining. |
Yiyang Lin; Bowei Zeng; Yifeng Wang; Yang Chen; Zijie Fang; Jian Zhang; Xiangyang Ji; Haoqian Wang; Yongbing Zhang; |
| 334 | Dynamic Spatial Propagation Network for Depth Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our solution is to estimate independent affinity matrices in each SPN iteration, but it is over-parameterized and heavy calculation.This paper introduces an efficient model that learns the affinity among neighboring pixels with an attention-based, dynamic approach. |
Yuankai Lin; Tao Cheng; Qi Zhong; Wending Zhou; Hua Yang; |
| 335 | Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: 2) Due to the static filters, current convolution based disparity refinement modules often produce over-smooth results. In this paper, we present two schemes to address these issues, where some traditional wisdoms are integrated. |
Biyang Liu; Huimin Yu; Yangqi Long; |
| 336 | FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a FL based framework called FedFR to improve the generic face representation in a privacy-aware manner. |
Chih-Ting Liu; Chien-Yi Wang; Shao-Yi Chien; Shang-Hong Lai; |
| 337 | Memory-Guided Semantic Learning Network for Temporal Sentence Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although existing methods train well-designed deep networks with large amount of data, we find that they can easily forget the rarely appeared cases during training due to the off-balance data distribution, which influences the model generalization and leads to unsatisfactory performance. To tackle this issue, we propose a memory-augmented network, called Memory-Guided Semantic Learning Network (MGSL-Net), that learns and memorizes the rarely appeared content in TSG task. |
Daizong Liu; Xiaoye Qu; Xing Di; Yu Cheng; Zichuan Xu; Pan Zhou; |
| 338 | Exploring Motion and Appearance Information for Temporal Sentence Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the object-level features extracted by Faster R-CNN suffer from missing motion analysis since the object detection model lacks temporal modeling. To solve this issue, we propose a novel Motion-Appearance Reasoning Network (MARN), which incorporates both motion-aware and appearance-aware object features to better reason object relations for modeling the activity among successive frames. |
Daizong Liu; Xiaoye Qu; Pan Zhou; Yang Liu; |
| 339 | Unsupervised Temporal Video Grounding with Deep Semantic Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore whether a video grounding model can be learned without any paired annotations. |
Daizong Liu; Xiaoye Qu; Yinzhen Wang; Xing Di; Kai Zou; Yu Cheng; Zichuan Xu; Pan Zhou; |
| 340 | SpikeConverter: An Efficient Conversion Framework Zipping The Gap Between Artificial Neural Networks and Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To better correlate ANN and SNN for better performance, we propose a conversion framework to mitigate the gap between the activation value of source ANN and the generated spike train of target SNN. |
Fangxin Liu; Wenbo Zhao; Yongbiao Chen; Zongwu Wang; Li Jiang; |
| 341 | Perceiving Stroke-Semantic Context: Hierarchical Contrastive Learning for Robust Scene Text Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce Perceiving Stroke-Semantic Context (PerSec), a new approach to self-supervised representation learning tailored for Scene Text Recognition (STR) task. |
Hao Liu; Bin Wang; Zhimin Bao; Mobai Xue; Sheng Kang; Deqiang Jiang; Yinsong Liu; Bo Ren; |
| 342 | AnchorFace: Boosting TAR@FAR for Practical Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we call the predefined FAR as Anchor FAR, and we argue that the existing FR loss functions cannot guarantee the optimal TAR under the Anchor FAR, which impedes further improvements of FR systems. |
Jiaheng Liu; Haoyu Qin; Yichao Wu; Ding Liang; |
| 343 | Memory-Based Jitter: Improving Visual Recognition on Long-Tailed Data with Diversity in Memory Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A radical solution is to augment the tail classes with higher diversity. To this end, we introduce a simple and reliable method named Memory-based Jitter (MBJ). |
Jialun Liu; Wenhui Li; Yifan Sun; |
| 344 | Debiased Batch Normalization Via Gaussian Process for Generalizable Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Debiased Batch Normalization via Gaussian Process approach (GDNorm) for generalizable person re-identification, which models the feature statistic estimation from BN layers as a dynamically self-refining Gaussian process to alleviate the bias to unseen domain for improving the generalization. |
Jiawei Liu; Zhipeng Huang; Liang Li; Kecheng Zheng; Zheng-Jun Zha; |
| 345 | Parallel and High-Fidelity Text-to-Lip Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a parallel decoding model for fast and high-fidelity text-to-lip generation (ParaLip). |
Jinglin Liu; Zhiying Zhu; Yi Ren; Wencan Huang; Baoxing Huai; Nicholas Yuan; Zhou Zhao; |
| 346 | SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-trained Siamese Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frames. |
Lin Liu; Shanxin Yuan; Jianzhuang Liu; Xin Guo; Youliang Yan; Qi Tian; |
| 347 | Single-Domain Generalization in Medical Image Segmentation Via Test-Time Adaptation from Shape Dictionary Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. |
Quande Liu; Cheng Chen; Qi Dou; Pheng-Ann Heng; |
| 348 | Learning to Predict 3D Lane Shape and Camera Pose from A Single Image Via Geometry Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, mainstream 3D lane detectors rely on perfect camera poses provided by other sensors, which is expensive and encounters multi-sensor calibration issues. To overcome this problem, we propose to predict 3D lanes by estimating camera pose from a single image with a two-stage framework. |
Ruijin Liu; Dapeng Chen; Tie Liu; Zhiliang Xiong; Zejian Yuan; |
| 349 | OVIS: Open-Vocabulary Visual Instance Search Via Visual-Semantic Aligned Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the task of open-vocabulary visual instance search (OVIS). |
Sheng Liu; Kevin Lin; Lijuan Wang; Junsong Yuan; Zicheng Liu; |
| 350 | Feature Generation and Hypothesis Verification for Reliable Face Anti-spoofing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a Feature Generation and Hypothesis Verification framework to alleviate the two issues. |
Shice Liu; Shitao Lu; Hongyi Xu; Jing Yang; Shouhong Ding; Lizhuang Ma; |
| 351 | Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The existing methods either have difficulties in balancing the tasks of image enhancement and object detection, or often ignore the latent information beneficial for detection. To alleviate this problem, we propose a novel Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively enhanced for better detection performance. |
Wenyu Liu; Gaofeng Ren; Runsheng Yu; Shi Guo; Jianke Zhu; Lei Zhang; |
| 352 | Visual Sound Localization in The Wild By Cross-Modal Interference Erasing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose the Interference Eraser (IEr) framework, which tackles the problem of audiovisual sound source localization in the wild. |
Xian Liu; Rui Qian; Hang Zhou; Di Hu; Weiyao Lin; Ziwei Liu; Bolei Zhou; Xiaowei Zhou; |
| 353 | Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a simple yet effective formulation for monocular 3D object detection without exploiting any extra information. |
Xianpeng Liu; Nan Xue; Tianfu Wu; |
| 354 | Highlighting Object Category Immunity for The Generalization of Human-Object Interaction Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: With mPD as a cue, we propose Object Category (OC) Immunity to boost HOI generalization. |
Xinpeng Liu; Yong-Lu Li; Cewu Lu; |
| 355 | DMN4: Few-Shot Learning Via Discriminative Mutual Nearest Neighbor Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we argue that a Mutual Nearest Neighbor (MNN) relation should be established to explicitly select the query descriptors that are most relevant to each task and discard less relevant ones from aggregative clutters in FSL. |
Yang Liu; Tu Zheng; Jie Song; Deng Cai; Xiaofei He; |
| 356 | Multi-Knowledge Aggregation and Transfer for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel multi-knowledge aggregation and transfer (MKAT) framework to comprehensively distill knowledge within an intermediate layer for semantic segmentation. |
Yuang Liu; Wei Zhang; Jun Wang; |
| 357 | Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a spatially-adaptive semantic alignment framework with perceptual motion consistency for coherent video cartoonization in an unsupervised manner. |
Zhenhuan Liu; Liang Li; Huajie Jiang; Xin Jin; Dandan Tu; Shuhui Wang; Zheng-Jun Zha; |
| 358 | Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: On the other hand, for existing SSL methods, it is burdensome and infeasible to use different downstream-task-customized datasets in pre-training for different tasks. To address this issue, we propose a novel SSL paradigm called Scalable Dynamic Routing (SDR), which can be trained once and deployed efficiently to different downstream tasks with task-customized pre-trained models. |
Zhili LIU; Jianhua Han; Lanqing Hong; Hang Xu; Kai Chen; Chunjing Xu; Zhenguo Li; |
| 359 | Pose Guided Image Generation from Misaligned Sources Via Residual Flow Based Correction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques. Therefore, we propose a new general approach which models multiple types of variations among sources, such as view angles, poses, facial expressions, in a unified framework, so that it can be employed on datasets of vastly different nature. |
Jiawei Lu; He Wang; Tianjia Shao; Yin Yang; Kun Zhou; |
| 360 | PMAL: Open Set Recognition Via Robust Prototype Mining Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel Prototype Mining And Learning (PMAL) framework. |
Jing Lu; Yunlu Xu; Hao Li; Zhanzhan Cheng; Yi Niu; |
| 361 | Barely-Supervised Learning: Semi-supervised Learning with Very Few Labeled Images Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels. |
Thomas Lucas; Philippe Weinzaepfel; Gregory Rogez; |
| 362 | Learning Optical Flow with Adaptive Graph Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. |
Ao Luo; Fan Yang; Kunming Luo; Xin Li; Haoqiang Fan; Shuaicheng Liu; |
| 363 | A Fusion-Denoising Attack on InstaHide with Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This leads to a natural question: is InstaHide with data augmentation secure? In this paper, we provide a negative answer to this question, by devising an attack for recovering private images from the outputs of InstaHide even when data augmentation is present. |
Xinjian Luo; Xiaokui Xiao; Yuncheng Wu; Juncheng Liu; Beng Chin Ooi; |
| 364 | Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: So far, our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited. In this study, we address this deficiency in both binary segmentation of biological microscopy images and multi-class segmentation of natural images. |
Yaoru Luo; Guole Liu; Yuanhao Guo; Ge Yang; |
| 365 | Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (spac), a novel reinforcement learning-based framework that performs step-wise registration. |
Ziwei Luo; Jing Hu; Xin Wang; Shu Hu; Bin Kong; Youbing Yin; Qi Song; Xi Wu; Siwei Lyu; |
| 366 | Adaptive Poincaré Point to Set Distance for Few-Shot Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to learn a context-aware hyperbolic metric to characterize the distance between a point and a set associated with a learned set to set distance. |
Rongkai Ma; Pengfei Fang; Tom Drummond; Mehrtash Harandi; |
| 367 | Generative Adaptive Convolutions for Real-World Noisy Image Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. |
Ruijun Ma; Shuyi Li; Bob Zhang; Zhengming Li; |
| 368 | REMOTE: Reinforced Motion Transformation Network for Semi-supervised 2D Pose Estimation in Videos Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a semi-supervised REinforced MOtion Transformation nEtwork (REMOTE) to leverage a few labeled frames and temporal pose variations in videos, which enables effective learning of 2D pose estimation in sparsely annotated videos. |
Xianzheng Ma; Hossein Rahmani; Zhipeng Fan; Bin Yang; Jun Chen; Jun Liu; |
| 369 | Learning from The Target: Dual Prototype Network for Few Shot Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Along with the prototype extracted from the support set, we propose to build the pseudo-prototype based on foreground features in the query image. |
Binjie Mao; Xinbang Zhang; Lingfeng Wang; Qian Zhang; Shiming Xiang; Chunhong Pan; |
| 370 | MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, we propose a framework that a priori models physical attributes of the face such as 3D shape, albedo, pose, and lighting explicitly, thus providing disentanglement by design. |
Safa C. Medin; Bernhard Egger; Anoop Cherian; Ye Wang; Joshua B. Tenenbaum; Xiaoming Liu; Tim K. Marks; |
| 371 | Towards Bridging Sample Complexity and Model Capacity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, we introduce a simple indicator to evaluate the sample complexity based on continuous mapping. |
Shibin Mei; Chenglong Zhao; Shengchao Yuan; Bingbing Ni; |
| 372 | Towards Accurate Facial Motion Retargeting with Identity-Consistent and Expression-Exclusive Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a result, these methods may not achieve promising performance. To address this, we propose an identity-consistent constraint to learn accurate identities by encouraging consistent identity prediction across multiple frames. |
Langyuan Mo; Haokun Li; Chaoyang Zou; Yubing Zhang; Ming Yang; Yihong Yang; Mingkui Tan; |
| 373 | Can Vision Transformers Learn Without Natural Images? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we experimentally verify that the results of formula-driven supervised learning (FDSL) framework are comparable with, and can even partially outperform, sophisticated self-supervised learning (SSL) methods like SimCLRv2 and MoCov2 without using any natural images in the pre-training phase. |
Kodai Nakashima; Hirokatsu Kataoka; Asato Matsumoto; Kenji Iwata; Nakamasa Inoue; Yutaka Satoh; |
| 374 | Federated Learning for Face Recognition with Gradient Correction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition and guarantees higher privacy. |
Yifan Niu; Weihong Deng; |
| 375 | Restorable Image Operators with Quasi-Invertible Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most image operators often smooth out details or generate textures after the processing, which removes the original content and raises challenges for restoring the original image. To resolve this issue, we propose a quasi-invertible model that learns common image processing operators in a restorable fashion: the learned image operators can generate visually pleasing results with the original content embedded. |
Hao Ouyang; Tengfei Wang; Qifeng Chen; |
| 376 | TEACh: Task-Driven Embodied Agents That Chat Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human-human, interactive dialogues to complete household tasks in simulation. |
Aishwarya Padmakumar; Jesse Thomason; Ayush Shrivastava; Patrick Lange; Anjali Narayan-Chen; Spandana Gella; Robinson Piramuthu; Gokhan Tur; Dilek Hakkani-Tur; |
| 377 | Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to performance degradation. To address this problem, we propose a novel label-efficient hybrid-supervised framework, which considers each weakly-annotated instance individually and learns its weight guided by the gradient direction of the strongly-annotated instances, so that the high-quality prior in the strongly-annotated instances is better exploited and the weakly-annotated instances are depicted more precisely. |
Junwen Pan; Qi Bi; Yanzhan Yang; Pengfei Zhu; Cheng Bian; |
| 378 | Less Is More: Pay Less Attention in Vision Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we propose a hierarchical Transformer where we use pure multi-layer perceptrons (MLPs) to encode rich local patterns in the early stages while applying self-attention modules to capture longer dependencies in deeper layers. |
Zizheng Pan; Bohan Zhuang; Haoyu He; Jing Liu; Jianfei Cai; |
| 379 | Unsupervised Representation for Semantic Segmentation By Implicit Cycle-Attention Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We first explore and present two factors that have significant effects on segmentation under the contrastive learning framework: 1) the difficulty and diversity of the positive contrastive pairs, 2) the balance of global and local features. With the intention of optimizing these factors, we propose the cycle-attention contrastive learning (CACL). |
Bo Pang; Yizhuo Li; Yifan Zhang; Gao Peng; Jiajun Tang; Kaiwen Zha; Jiefeng Li; Cewu Lu; |
| 380 | Graph-Based Point Tracker for 3D Object Tracking in Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, a new deep learning network named as graph-based point tracker (GPT) is proposed for 3D object tracking in point clouds. |
Minseong Park; Hongje Seong; Wonje Jang; Euntai Kim; |
| 381 | SyncTalkFace: Talking Face Generation with Precise Lip-Syncing Via Audio-Lip Memory Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, they struggle to synthesize fine details of the lips varying at the phoneme level as they do not sufficiently provide visual information of the lips at the video synthesis step. To overcome this limitation, our work proposes Audio-Lip Memory that brings in visual information of the mouth region corresponding to input audio and enforces fine-grained audio-visual coherence. |
Se Jin Park; Minsu Kim; Joanna Hong; Jeongsoo Choi; Yong Man Ro; |
| 382 | Vision Transformers Are Robust Learners Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we study the robustness of the Vision Transformer (ViT) (Dosovitskiy et al. 2021) against common corruptions and perturbations, distribution shifts, and natural adversarial examples. |
Sayak Paul; Pin-Yu Chen; |
| 383 | Self-Supervised Category-Level 6D Object Pose Estimation with Deep Implicit Shape Representation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing category-level 6D pose estimation methods usually require supervised training with a sufficient number of 6D pose annotations of objects which makes them difficult to be applied in real scenarios. To address this problem, we propose a self-supervised framework for category-level 6D pose estimation in this paper. |
Wanli Peng; Jianhang Yan; Hongtao Wen; Yi Sun; |
| 384 | Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels, which can get rid of pre-training models and thus does not depend on sufficient annotations. |
Tao Pu; Tianshui Chen; Hefeng Wu; Liang Lin; |
| 385 | ReX: An Efficient Approach to Reducing Memory Cost in Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel approach named recurrent aggregation operator (ReX), which uses recurrent neural networks (RNNs) to effectively aggregate intra-patch features within a large receptive field to get delicate local representations, while bypassing large early activations. |
Xuwei Qian; Renlong Hang; Qingshan Liu; |
| 386 | CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. |
Yu Qiao; Jincheng Zhu; Chengjiang Long; Zeyao Zhang; Yuxin Wang; Zhenjun Du; Xin Yang; |
| 387 | Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. |
Jie Qin; Jie Wu; Xuefeng Xiao; Lujun Li; Xingang Wang; |
| 388 | TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. |
Linhao Qu; Shaolei Liu; Manning Wang; Zhijian Song; |
| 389 | Deep Implicit Statistical Shape Models for 3D Medical Image Delineation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present deep implicit statistical shape models (DISSMs), a new approach that marries the representation power of deep networks with the benefits of SSMs. |
Ashwin Raju; Shun Miao; Dakai Jin; Le Lu; Junzhou Huang; Adam P. Harrison; |
| 390 | Decompose The Sounds and Pixels, Recompose The Events Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a framework centering around a novel architecture called the Event Decomposition Recomposition Network (EDRNet) to tackle the Audio-Visual Event (AVE) localization problem in the supervised and weakly supervised settings. |
Varshanth R. Rao; Md Ibrahim Khalil; Haoda Li; Peng Dai; Juwei Lu; |
| 391 | Learning from Label Proportions with Prototypical Contrastive Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a new model that jointly uses prototypical contrastive learning and bag-level cluster proportions to implement efficient LLP classification. |
Laura Elena Cué La Rosa; Dário Augusto Borges Oliveira; |
| 392 | Beyond Learning Features: Training A Fully-Functional Classifier with ZERO Instance-Level Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We attempt to train deep neural networks for classification without using any labeled data. |
Deepak Babu Sam; Abhinav Agarwalla; Venkatesh Babu Radhakrishnan; |
| 393 | Reference-Guided Pseudo-Label Generation for Medical Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Producing densely annotated data is a difficult and tedious task for medical imaging applications. To address this problem, we propose a novel approach to generate supervision for semi-supervised semantic segmentation. |
Constantin Marc Seibold; Simon Reiß; Jens Kleesiek; Rainer Stiefelhagen; |
| 394 | Information-Theoretic Bias Reduction Via Causal View of Spurious Correlation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic bias by taking advantage of conditional mutual information. |
Seonguk Seo; Joon-Young Lee; Bohyung Han; |
| 395 | Improving Scene Graph Classification By Exploiting Knowledge from Texts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we investigate whether textual scene descriptions can substitute for annotated image data. |
Sahand Sharifzadeh; Sina Moayed Baharlou; Martin Schmitt; Hinrich Schütze; Volker Tresp; |
| 396 | Reliable Inlier Evaluation for Unsupervised Point Cloud Registration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. |
Yaqi Shen; Le Hui; Haobo Jiang; Jin Xie; Jian Yang; |
| 397 | Explainable Survival Analysis with Convolution-Involved Vision Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to develop a novel survival analysis model to fully utilize the complete WSI information. |
Yifan Shen; Li Liu; Zhihao Tang; Zongyi Chen; Guixiang Ma; Jiyan Dong; Xi Zhang; Lin Yang; Qingfeng Zheng; |
| 398 | Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This drawback hinders the model from learning subtle variance and fine-grained information. To address this, in this work we aim to involve the soft distance concept on label space in the contrastive-based unsupervised learning task and let the model be aware of the soft degree of similarity between positive or negative pairs through mixing the input data space, to further work collaboratively for the input and loss spaces. |
Zhiqiang Shen; Zechun Liu; Zhuang Liu; Marios Savvides; Trevor Darrell; Eric Xing; |
| 399 | On The Efficacy of Small Self-Supervised Contrastive Models Without Distillation Signals Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the issue of training self-supervised small models without distillation signals. |
Haizhou Shi; Youcai Zhang; Siliang Tang; Wenjie Zhu; Yaqian Li; Yandong Guo; Yueting Zhuang; |
| 400 | Social Interpretable Tree for Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. |
Liushuai Shi; Le Wang; Chengjiang Long; Sanping Zhou; Fang Zheng; Nanning Zheng; Gang Hua; |
| 401 | P^3-Net: Part Mobility Parsing from Point Cloud Sequences Via Learning Explicit Point Correspondence Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a novel approach to parse 3D part mobility from point cloud sequences. |
Yahao Shi; Xinyu Cao; Feixiang Lu; Bin Zhou; |
| 402 | Improving Zero-Shot Phrase Grounding Via Reasoning on External Knowledge and Spatial Relations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we design a novel phrase grounding architecture that builds multi-modal knowledge graphs using external knowledge and then performs graph reasoning and spatial relation reasoning to localize the referred nouns phrases. |
Zhan Shi; Yilin Shen; Hongxia Jin; Xiaodan Zhu; |
| 403 | Iterative Contrast-Classify for Semi-supervised Temporal Action Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. |
Dipika Singhania; Rahul Rahaman; Angela Yao; |
| 404 | JPV-Net: Joint Point-Voxel Representations for Accurate 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to exploit the strengths of both two representations, and present a novel two-stage detector, named Joint Point-Voxel Network (JPV-Net). |
Nan Song; Tianyuan Jiang; Jian Yao; |
| 405 | Fully Attentional Network for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, such practices tend to condense feature dependencies along the other dimensions, hence causing attention missing, which might lead to inferior results for small/thin categories or inconsistent segmentation inside large objects. To address this problem, we propose a new approach, namely Fully Attentional Network (FLANet), to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency. |
Qi Song; Jie Li; Chenghong Li; Hao Guo; Rui Huang; |
| 406 | Self-Supervised Object Localization with Joint Graph Partition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Since collecting bounding-box labels is time-consuming and laborious, many researchers focus on weakly supervised object localization (WSOL). As the recent appealing self-supervised learning technique shows its powerful function in visual tasks, in this paper, we take the early attempt to explore unsupervised object localization by self-supervision. |
Yukun Su; Guosheng Lin; Yun Hao; Yiwen Cao; Wenjun Wang; Qingyao Wu; |
| 407 | Correlation Field for Boosting 3D Object Detection in Structured Scenes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a simple but effective online crop-and-paste data augmentation pipeline for structured 3D point cloud scenes, named CorrelaBoost. |
Jianhua Sun; Hao-Shu Fang; Xianghui Zhu; Jiefeng Li; Cewu Lu; |
| 408 | Boost Supervised Pretraining for Visual Transfer Learning: Implications of Self-Supervised Contrastive Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our core finding is that it is the amount of information effectively perceived by the learning model that is crucial to transfer learning, instead of absolute size of the dataset. Based on this finding, we propose Classification Activation Map guided contrastive (CAMtrast) learning which better utilizes the label supervsion to strengthen supervised pretraining, by making the networks perceive more information from the training images. |
Jinghan Sun; Dong Wei; Kai Ma; Liansheng Wang; Yefeng Zheng; |
| 409 | Dual Contrastive Learning for General Face Forgery Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Previous works always formulate face forgery detection as a classification problem based on cross-entropy loss, which emphasizes category-level differences rather than the essential discrepancies between real and fake faces, limiting model generalization in unseen domains. To address this issue, we propose a novel face forgery detection framework, named Dual Contrastive Learning (DCL), which specially constructs positive and negative paired data and performs designed contrastive learning at different granularities to learn generalized feature representation. |
Ke Sun; Taiping Yao; Shen Chen; Shouhong Ding; Jilin Li; Rongrong Ji; |
| 410 | SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing methods focus on the former and ignore the latter, resulting in a failure to achieve desired results. To solve the above problems, we propose a unified Symmetric Semantic-Aware Transformer (SSAT) network, which incorporates semantic correspondence learning to realize makeup transfer and removal simultaneously. |
Zhaoyang Sun; Yaxiong Chen; Shengwu Xiong; |
| 411 | Adversarial Bone Length Attack on Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we show that adversarial attacks can be performed on skeleton-based action recognition models, even in a significantly low-dimensional setting without any temporal manipulation. |
Nariki Tanaka; Hiroshi Kera; Kazuhiko Kawamoto; |
| 412 | Sparse MLP for Image Recognition: Is Self-Attention Really Necessary? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we explore whether the core self-attention module in Transformer is the key to achieving excellent performance in image recognition. |
Chuanxin Tang; Yucheng Zhao; Guangting Wang; Chong Luo; Wenxuan Xie; Wenjun Zeng; |
| 413 | Not All Voxels Are Equal: Semantic Scene Completion from The Point-Voxel Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We revisit Semantic Scene Completion (SSC), a useful task to predict the semantic and occupancy representation of 3D scenes, in this paper. |
Jiaxiang Tang; Xiaokang Chen; Jingbo Wang; Gang Zeng; |
| 414 | Transfer Learning for Color Constancy Via Statistic Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recently, although the deep learning approaches have remarkably improved on single-camera data, these models still suffer from the seriously insufficient data problem, resulting in shallow model capacity and degradation in multi-camera settings. In this paper, to alleviate this problem, we present a Transfer Learning Color Constancy (TLCC) method that leverages cross-camera RAW data and massive unlabeled sRGB data to support training. |
Yuxiang Tang; Xuejing Kang; Chunxiao Li; Zhaowen Lin; Anlong Ming; |
| 415 | TVT: Three-Way Vision Transformer Through Multi-Modal Hypersphere Learning for Zero-Shot Sketch-Based Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the zero-shot sketch-based image retrieval (ZS-SBIR) task, which retrieves natural images related to sketch queries from unseen categories. |
Jialin Tian; Xing Xu; Fumin Shen; Yang Yang; Heng Tao Shen; |
| 416 | GuidedMix-Net: Semi-supervised Semantic Segmentation By Using Labeled Images As Reference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. |
Peng Tu; Yawen Huang; Feng Zheng; Zhenyu He; Liujuan Cao; Ling Shao; |
| 417 | MTLDesc: Looking Wider to Describe Better Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we focus on making local descriptors “look wider to describe better” by learning local Descriptors with More Than Local information (MTLDesc). |
Changwei Wang; Rongtao Xu; Yuyang Zhang; Shibiao Xu; Weiliang Meng; Bin Fan; Xiaopeng Zhang; |
| 418 | Active Boundary Loss for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a novel active boundary loss for semantic segmentation. |
Chi Wang; Yunke Zhang; Miaomiao Cui; Peiran Ren; Yin Yang; Xuansong Xie; Xian-Sheng Hua; Hujun Bao; Weiwei Xu; |
| 419 | Online-Updated High-Order Collaborative Networks for Single Image Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a high-order collaborative network with multi-scale compact constraints and a bidirectional scale-content similarity mining module to exploit features from deep networks externally and internally for rain streaks removal. |
Cong Wang; Jinshan Pan; Xiao-Ming Wu; |
| 420 | FCA: Learning A 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. |
Donghua Wang; Tingsong Jiang; Jialiang Sun; Weien Zhou; Zhiqiang Gong; Xiaoya Zhang; Wen Yao; Xiaoqian Chen; |
| 421 | When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We should pay more attentions to the remaining parts of ViT in the future work. |
Guangting Wang; Yucheng Zhao; Chuanxin Tang; Chong Luo; Wenjun Zeng; |
| 422 | Self-Supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour/shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. |
Hao Wang; Euijoon Ahn; Jinman Kim; |
| 423 | UCTransNet: Rethinking The Skip Connections in U-Net from A Channel-Wise Perspective with Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Based on our findings, we propose a new segmentation framework, named UCTransNet (with a proposed CTrans module in U-Net), from the channel perspective with attention mechanism. |
Haonan Wang; Peng Cao; Jiaqi Wang; Osmar R. Zaiane; |
| 424 | Renovate Yourself: Calibrating Feature Representation of Misclassified Pixels for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These methods usually treat the misclassified and correctly classified pixels equally, hence misleading the optimization process and causing inconsistent intra-class pixel feature representations in the embedding space during learning. In this paper, we propose the auxiliary representation calibration head (RCH), which consists of the image decoupling, prototype clustering, error calibration modules and a metric loss function, to calibrate these error-prone feature representations for better intra-class consistency and segmentation performance. |
Hualiang Wang; Huanpeng Chu; Siming FU; Zuozhu Liu; Haoji Hu; |
| 425 | Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, pixels in the same map inevitably share semantics to be closer than they actually are, which may affect the discrimination of pixels in the same map and lead to the unfair comparison to pixels in other maps. To address these issues, we propose a separated region-level contrastive learning scheme, namely SepaReg, the core of which is to separate each image into regions and encode each region separately. |
Jiacheng Wang; Xiaomeng Li; Yiming Han; Jing Qin; Liansheng Wang; Zhou Qichao; |
| 426 | Contrastive Quantization with Code Memory for Unsupervised Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper provides a novel solution to unsupervised deep quantization, namely Contrastive Quantization with Code Memory (MeCoQ). |
Jinpeng Wang; Ziyun Zeng; Bin Chen; Tao Dai; Shu-Tao Xia; |
| 427 | Learning Temporally and Semantically Consistent Unpaired Video-to-Video Translation Through Pseudo-Supervision from Synthetic Optical Flow Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation. In this work, we propose a novel paradigm that regularizes the spatiotemporal consistency by synthesizing motions in input videos with the generated optical flow instead of estimating them. |
Kaihong Wang; Kumar Akash; Teruhisa Misu; |
| 428 | Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Learning (CDCL). |
Li Wang; Dong Li; Han Liu; JinZhang Peng; Lu Tian; Yi Shan; |
| 429 | Scaled ReLU Matters for Training Vision Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We verify, both theoretically and empirically, that scaled ReLU in conv-stem not only improves training stabilization, but also increases the diversity of patch tokens, thus boosting peak performance with a large margin via adding few parameters and flops. |
Pichao Wang; Xue Wang; Hao Luo; Jingkai Zhou; Zhipeng Zhou; Fan Wang; Hao Li; Rong Jin; |
| 430 | CQA-Face: Contrastive Quality-Aware Attentions for Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This can cause performance drops when emphasized facial parts are invisible under heavy occlusions (e.g. face masks) or large pose variations; 2) Different facial parts may appear at various quality caused by occlusion, blur, or illumination changes. In this paper, we propose contrastive quality-aware attentions, called CQA-Face, to address these two issues. |
Qiangchang Wang; Guodong Guo; |
| 431 | Category-Specific Nuance Exploration Network for Fine-Grained Object Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A potential limitation of these methods is that they only focus on common parts across the dataset (e.g. head, body or even leg) by introducing additional prior knowledge, but the retrieval of a fine-grained object may rely on category-specific nuances that contribute to category prediction. To handle this limitation, we propose an end-to-end Category-specific Nuance Exploration Network (CNENet) that elaborately discovers category-specific nuances that contribute to category prediction, and semantically aligns these nuances grouped by subcategory without any additional prior knowledge, to directly emphasize the discrepancy among subcategories. |
Shijie Wang; Zhihui Wang; Haojie Li; Wanli Ouyang; |
| 432 | Detail-Preserving Transformer for Light Field Image Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we put forth a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task. |
Shunzhou Wang; Tianfei Zhou; Yao Lu; Huijun Di; |
| 433 | One-Shot Talking Face Generation from Single-Speaker Audio-Visual Correlation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and then transferring audio-driven motion fields to a reference image. |
Suzhen Wang; Lincheng Li; Yu Ding; Xin Yu; |
| 434 | Pose-Guided Feature Disentangling for Occluded Person Re-identification Based on Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Some existing pose-guided methods solve this problem by aligning body parts according to graph matching, but these graph-based methods are not intuitive and complicated. Therefore, we propose a transformer-based Pose-guided Feature Disentangling (PFD) method by utilizing pose information to clearly disentangle semantic components (e.g. human body or joint parts) and selectively match non-occluded parts correspondingly. |
Tao Wang; Hong Liu; Pinhao Song; Tianyu Guo; Wei Shi; |
| 435 | FFNet: Frequency Fusion Network for Semantic Scene Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Yet, they ignore the large discrepancy of RGB-D data and the uncertainty measurements of depth data. To solve this problem, we propose the Frequency Fusion Network (FFNet), a novel method for boosting semantic scene completion by better utilizing RGB-D data. |
Xuzhi Wang; Di Lin; Liang Wan; |
| 436 | Privacy-Preserving Face Recognition in The Frequency Domain Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In order to further protect the remaining frequency components, we propose a fast masking method. |
Yinggui Wang; Jian Liu; Man Luo; Le Yang; Li Wang; |
| 437 | Anchor DETR: Query Design for Transformer-Based Detector Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel query design for the transformer-based object detection. |
Yingming Wang; Xiangyu Zhang; Tong Yang; Jian Sun; |
| 438 | Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel GAN Prior based degradation-aware feature interpolation network, dubbed Panini-Net, for FR tasks by explicitly learning the abstract representations to distinguish various degradations. |
Yinhuai Wang; Yujie Hu; Jian Zhang; |
| 439 | End-to-End Transformer Based Model for Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we build a pure Transformer-based model, which integrates image captioning into one stage and realizes end-to-end training. |
Yiyu Wang; Jungang Xu; Yingfei Sun; |
| 440 | Learning to Detect 3D Facial Landmarks Via Heatmap Regression with Graph Convolutional Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel 3D facial landmark detection method, which directly locates the coordinates of landmarks from 3D point cloud with a well-customized graph convolutional network. |
Yuan Wang; Min Cao; Zhenfeng Fan; Silong Peng; |
| 441 | Low-Light Image Enhancement with Normalizing Flow Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. |
Yufei Wang; Renjie Wan; Wenhan Yang; Haoliang Li; Lap-Pui Chau; Alex Kot; |
| 442 | Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Based on MMN, we present a winner solution for the HC-STVG challenge of the 3rd PIC workshop. |
Zhenzhi Wang; Limin Wang; Tao Wu; Tianhao Li; Gangshan Wu; |
| 443 | Texture Reformer: Towards Fast and Universal Interactive Texture Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present the texture reformer, a fast and universal neural-based framework for interactive texture transfer with user-specified guidance. |
Zhizhong Wang; Lei Zhao; Haibo Chen; Ailin Li; Zhiwen Zuo; Wei Xing; Dongming Lu; |
| 444 | Interact, Embed, and EnlargE: Boosting Modality-Specific Representations for Multi-Modal Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing multi-modal methods ignore the importance of modality-specific information in the feature fusion stage. To this end, we propose a novel method to boost modality-specific representations for multi-modal person Re-ID: Interact, Embed, and EnlargE (IEEE). |
Zi Wang; Chenglong Li; Aihua Zheng; Ran He; Jin Tang; |
| 445 | Can Semantic Labels Assist Self-Supervised Visual Representation Learning? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we defend the usefulness of semantic labels but point out that fully-supervised and self-supervised methods are pursuing different kinds of features. |
Longhui Wei; Lingxi Xie; Jianzhong He; Xiaopeng Zhang; Qi Tian; |
| 446 | Rethinking The Two-Stage Framework for Grounded Situation Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: 2) All semantic roles are detected in an autoregressive manner, which fails to model the complex semantic relations between different roles. To this end, we propose a novel SituFormerfor GSR which consists of a Coarse-to-Fine Verb Model (CFVM) and a Transformer-based Noun Model (TNM). |
Meng Wei; Long Chen; Wei Ji; Xiaoyu Yue; Tat-Seng Chua; |
| 447 | Boosting The Transferability of Video Adversarial Examples Via Temporal Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Nevertheless, most existing adversarial attack methods have poor transferability when attacking other video models and transfer-based attacks on video models are still unexplored. To this end, we propose to boost the transferability of video adversarial examples for black-box attacks on video recognition models. |
Zhipeng Wei; Jingjing Chen; Zuxuan Wu; Yu-Gang Jiang; |
| 448 | Towards Transferable Adversarial Attacks on Vision Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we posit that adversarial attacks on transformers should be specially tailored for their architecture, jointly considering both patches and self-attention, in order to achieve high transferability. |
Zhipeng Wei; Jingjing Chen; Micah Goldblum; Zuxuan Wu; Tom Goldstein; Yu-Gang Jiang; |
| 449 | L-CoDe:Language-Based Colorization Using Color-Object Decoupled Conditions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose L-CoDe, a Language-based Colorization network using color-object Decoupled conditions. |
Shuchen Weng; Hao Wu; Zheng Chang; Jiajun Tang; Si Li; Boxin Shi; |
| 450 | Neural Interferometry: Image Reconstruction from Astronomical Interferometers Using Transformer-Conditioned Neural Fields Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel deep learning approach in which the representation in the Fourier domain of an astronomical source is learned implicitly using a neural field representation. |
Benjamin Wu; Chao Liu; Benjamin Eckart; Jan Kautz; |
| 451 | TDv2: A Novel Tree-Structured Decoder for Offline Mathematical Expression Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a novel tree decoder (TDv2) to fully utilize the tree structure labels. |
Changjie Wu; Jun Du; Yunqing Li; Jianshu Zhang; Chen Yang; Bo Ren; Yiqing Hu; |
| 452 | Learning Token-Based Representation for Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. |
Hui Wu; Min Wang; Wengang Zhou; Yang Hu; Houqiang Li; |
| 453 | Multi-Modal Answer Validation for Knowledge-Based VQA Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Using more knowledge sources increases the chance of retrieving more irrelevant or noisy facts, making it challenging to comprehend the facts and find the answer. To address this challenge, we propose Multi-modal Answer Validation using External knowledge (MAVEx), where the idea is to validate a set of promising answer candidates based on answer-specific knowledge retrieval. |
Jialin Wu; Jiasen Lu; Ashish Sabharwal; Roozbeh Mottaghi; |
| 454 | Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a Neighborhood Consensus Contrastive Learning (NCCL) method. |
Shengsen Wu; Liang Chen; Yihang Lou; Yan Bai; Tao Bai; Minghua Deng; Ling-Yu Duan; |
| 455 | Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Consequently, their receptive fields in a single attention layer are not large enough, resulting in insufficient context modeling. To address this issue, we propose a Pale-Shaped self-Attention (PS-Attention), which performs self-attention within a pale-shaped region. |
Sitong Wu; Tianyi Wu; Haoru Tan; Guodong Guo; |
| 456 | Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training. |
Xinyi Wu; Zhenyao Wu; Yuhang Lu; Lili Ju; Song Wang; |
| 457 | Multi-Centroid Representation Network for Domain Adaptive Person Re-ID Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. |
Yuhang Wu; Tengteng Huang; Haotian Yao; Chi Zhang; Yuanjie Shao; Chuchu Han; Changxin Gao; Nong Sang; |
| 458 | Efficient Non-local Contrastive Attention for Image Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel Efficient Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling and leverage more relevant non-local features. |
Bin Xia; Yucheng Hang; Yapeng Tian; Wenming Yang; Qingmin Liao; Jie Zhou; |
| 459 | Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-Based Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an Accelerated Multi-Scale Aggregation network (AMSA) for Reference-based Super-Resolution, including Coarse-to-Fine Embedded PatchMatch (CFE-PatchMatch) and Multi-Scale Dynamic Aggregation (MSDA) module. |
Bin Xia; Yapeng Tian; Yucheng Hang; Wenming Yang; Qingmin Liao; Jie Zhou; |
| 460 | Cross-Domain Collaborative Normalization Via Structural Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop a novel normalization technique, named Collaborative Normalization (CoN), for eliminating domain discrepancy and accelerating the model training of neural networks for UDA. |
Haifeng Xia; Zhengming Ding; |
| 461 | ReMoNet: Recurrent Multi-Output Network for Efficient Video Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper aims to develop a lightweight deep video denoising method that is friendly to resource-constrained mobile devices. |
Liuyu Xiang; Jundong Zhou; Jirui Liu; Zerun Wang; Haidong Huang; Jie Hu; Jungong Han; Yuchen Guo; Guiguang Ding; |
| 462 | Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, the study focused on 2D images and its counterpart in 3D point clouds segmentation lags far behind due to the lack of large-scale synthetic datasets and effective transfer methods. We address this issue by collecting SynLiDAR, a large-scale synthetic LiDAR dataset that contains point-wise annotated point clouds with accurate geometric shapes and comprehensive semantic classes. |
Aoran Xiao; Jiaxing Huang; Dayan Guan; Fangneng Zhan; Shijian Lu; |
| 463 | Video As Conditional Graph Hierarchy for Multi-Granular Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues. |
Junbin Xiao; Angela Yao; Zhiyuan Liu; Yicong Li; Wei Ji; Tat-Seng Chua; |
| 464 | AdaptivePose: Human Parts As Adaptive Points Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency. |
Yabo Xiao; Xiao Juan Wang; Dongdong Yu; Guoli Wang; Qian Zhang; Mingshu HE; |
| 465 | Learning Quality-Aware Representation for Multi-Person Pose Regression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the aforementioned issues, we propose to learn the pose regression quality-aware representation. |
Yabo Xiao; Dongdong Yu; Xiao Juan Wang; Lei Jin; Guoli Wang; Qian Zhang; |
| 466 | Attribute-Based Progressive Fusion Network for RGBT Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we disentangle the fusion process via the challenge attributes, and thus propose a novel Attribute-based Progressive Fusion Network (APFNet) to increase the fusion capacity with a small number of parameters while reducing the dependence on large-scale training data. |
Yun Xiao; MengMeng Yang; Chenglong Li; Lei Liu; Jin Tang; |
| 467 | Detailed Facial Geometry Recovery from Multi-View Images By Learning An Implicit Function Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel architecture to recover extremely detailed 3D faces within dozens of seconds. |
Yunze Xiao; Hao Zhu; Haotian Yang; Zhengyu Diao; Xiangju Lu; Xun Cao; |
| 468 | FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Data association is important in the point cloud registration. In this work, we propose to solve the partial-to-partial registration from a new perspective, by introducing multi-level feature interactions between the source and the reference clouds at the feature extraction stage, such that the registration can be realized without the attentions or explicit mask estimation for the overlapping detection as adopted previously. |
Hao Xu; Nianjin Ye; Guanghui Liu; Bing Zeng; Shuaicheng Liu; |
| 469 | Rendering-Aware HDR Environment Map Prediction from A Single Image Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a two-stage deep learning-based method to predict an HDR environment map from a single narrow field-of-view LDR image. |
Jun-Peng Xu; Chenyu Zuo; Fang-Lue Zhang; Miao Wang; |
| 470 | Topology-Aware Convolutional Neural Network for Efficient Skeleton-Based Action Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: One reason is that CNNs are considered poor in modeling the irregular skeleton topology. To alleviate this limitation, we propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. |
Kailin Xu; Fanfan Ye; Qiaoyong Zhong; Di Xie; |
| 471 | Transcoded Video Restoration By Temporal Spatial Auxiliary Network Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a new method, temporal spatial auxiliary network (TSAN), for transcoded video restoration. |
Li Xu; Gang He; Jinjia Zhou; Jie Lei; Weiying Xie; Yunsong Li; Yu-Wing Tai; |
| 472 | DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing works learn the shared feature space within multi-source domains but ignore the characteristic of the feature itself (e.g., the feature sensitivity to the domain-specific style). Therefore, we propose the Domain-invariant Representation Learning (DIRL) for domain generalization which utilizes the feature sensitivity as the feature prior to guide the enhancement of the model generalization capability. |
Qi Xu; Liang Yao; Zhengkai Jiang; Guannan Jiang; Wenqing Chu; Wenhui Han; Wei Zhang; Chengjie Wang; Ying Tai; |
| 473 | Behind The Curtain: Learning Occluded Shapes for 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To tackle the challenge, we present a novel LiDAR-based 3D object detection model, dubbed Behind the Curtain Detector (BtcDet), which learns the object shape priors and estimates the complete object shapes that are partially occluded (curtained) in point clouds. |
Qiangeng Xu; Yiqi Zhong; Ulrich Neumann; |
| 474 | Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. |
Rui Xu; Zongyan Han; Le Hui; Jianjun Qian; Jin Xie; |
| 475 | Dual Attention Networks for Few-Shot Fine-Grained Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, to generate fine-grained tailored representations for few-shot recognition, we propose a Dual Attention Network (Dual Att-Net) consisting of two dual branches of both hard- and soft-attentions. |
Shu-Lin Xu; Faen Zhang; Xiu-Shen Wei; Jianhua Wang; |
| 476 | Sparse Cross-Scale Attention Network for Efficient LiDAR Panoptic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the long-range geometry relationship has not been sufficiently modeled by local feature learning from the above methods. To this end, we present SCAN, a novel sparse cross-scale attention network to first align multi-scale sparse features with global voxel-encoded attention to capture the long-range relationship of instance context, which is able to boost the regression accuracy of the over-segmented large objects. |
Shuangjie Xu; Rui Wan; Maosheng Ye; Xiaoyi Zou; Tongyi Cao; |
| 477 | Towards Fully Sparse Training: Information Restoration with Spatial Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on the observation of spatial similarity among activations, we propose pruning activations with fixed 2:4 masks. |
Weixiang Xu; Xiangyu He; Ke Cheng; Peisong Wang; Jian Cheng; |
| 478 | Hierarchical Image Generation Via Transformer-Based Sequential Patch Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To synthesize images with preferred objects and interactions, a controllable way is to generate the image from a scene graph and a large pool of object crops, where the spatial arrangements of the objects in the image are defined by the scene graph while their appearances are determined by the retrieved crops from the pool. In this paper, we propose a novel framework with such a semi-parametric generation strategy. |
Xiaogang Xu; Ning Xu; |
| 479 | Reliable Propagation-Correction Modulation for Video Object Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We aim to suppress error propagation through a correction mechanism with high reliability. |
Xiaohao Xu; Jinglu Wang; Xiao Li; Yan Lu; |
| 480 | Adaptive Hypergraph Neural Network for Multi-Person Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel two-stage hypergraph-based framework, dubbed ADaptive Hypergraph Neural Network (AD-HNN) to estimate multiple human poses from a single image, with a keypoint localization network and an Adaptive-Pose Hypergraph Neural Network (AP-HNN) added onto the former network. |
Xixia Xu; Qi Zou; Xue Lin; |
| 481 | Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To tackle the limitations and expand the applicable scenario of token pruning, we present Evo-ViT, a self-motivated slow-fast token evolution approach for vision transformers. |
Yifan Xu; Zhijie Zhang; Mengdan Zhang; Kekai Sheng; Ke Li; Weiming Dong; Liqing Zhang; Changsheng Xu; Xing Sun; |
| 482 | MobileFaceSwap: A Lightweight Framework for Video Face Swapping Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information. |
Zhiliang Xu; Zhibin Hong; Changxing Ding; Zhen Zhu; Junyu Han; Jingtuo Liu; Errui Ding; |
| 483 | Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a vision-language pre-training model, Clinical-BERT, for the medical domain, and devise three domain-specific tasks: Clinical Diagnosis (CD), Masked MeSH Modeling (MMM), Image-MeSH Matching (IMM), together with one general pre-training task: Masked Language Modeling (MLM), to pre-train the model. |
Bin Yan; Mingtao Pei; |
| 484 | Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a solution, in this paper we propose to use word embeddings as a form of prior knowledge about the meaning of the labels. |
Kun Yan; Chenbin Zhang; Jun Hou; Ping Wang; Zied Bouraoui; Shoaib Jameel; Steven Schockaert; |
| 485 | Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Considering the large appearance differences between the synthetic and real-world scenarios, directly training with synthetic data will lead to performance degradation on real-world scenarios. To mitigate this problem, we propose a novel unsupervised domain adaptive SOD method to adapt between these two domains by uncertainty-aware self-training. |
Pengxiang Yan; Ziyi Wu; Mengmeng Liu; Kun Zeng; Liang Lin; Guanbin Li; |
| 486 | Transmission-Guided Bayesian Generative Model for Smoke Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This is caused by both knowledge level uncertainty due to limited training data for accurate smoke segmentation and labeling level uncertainty representing the difficulty in labeling ground-truth. To effectively model the two types of uncertainty, we introduce a Bayesian generative model to simultaneously estimate the posterior distribution of model parameters and its predictions. |
Siyuan Yan; Jing Zhang; Nick Barnes; |
| 487 | Cross-Species 3D Face Morphing Via Alignment-Aware Controller Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It remains challenging how to preserve target structural information and source fine-grained facial details simultaneously. To this end, we propose an Alignment-aware 3D Face Morphing (AFM) framework, which builds semantic-adaptive correspondence between source and target faces across species, via an alignment-aware controller mesh (Explicit Controller, EC) with explicit source/target mesh binding. |
Xirui Yan; Zhenbo Yu; Bingbing Ni; Hang Wang; |
| 488 | Exploring Visual Context for Weakly Supervised Person Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper inventively considers weakly supervised person search with only bounding box annotations. We propose to address this novel task by investigating three levels of context clues (i.e., detection, memory and scene) in unconstrained natural images. |
Yichao Yan; Jinpeng Li; Shengcai Liao; Jie Qin; Bingbing Ni; Ke Lu; Xiaokang Yang; |
| 489 | Cross-Modal Mutual Learning for Audio-Visual Speech Recognition and Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a key characteristic in audio-visual speech recognition (AVSR), relating linguistic information observed across visual and audio data has been a challenge, benefiting not only audio/visual speech recognition (ASR/VSR) but also for manipulating data within/across modalities. In this paper, we present a feature disentanglement-based framework for jointly addressing the above tasks. |
Chih-Chun Yang; Wan-Cyuan Fan; Cheng-Fu Yang; Yu-Chiang Frank Wang; |
| 490 | Mutual Contrastive Learning for Visual Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. |
Chuanguang Yang; Zhulin An; Linhang Cai; Yongjun Xu; |
| 491 | Temporal Action Proposal Generation with Background Constraint Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we innovatively propose a general auxiliary Background Constraint idea to further suppress low-quality proposals, by utilizing the background prediction score to restrict the confidence of proposals. |
Haosen Yang; Wenhao Wu; Lining Wang; Sheng Jin; Boyang Xia; Hongxun Yao; Hujie Huang; |
| 492 | Cross-Modal Federated Human Activity Recognition Via Modality-Agnostic and Modality-Specific Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new task of cross-modal federated human activity recognition (CMF-HAR), which is conducive to promote the large-scale use of the HAR model on more local devices. |
Xiaoshan Yang; Baochen Xiong; Yi Huang; Changsheng Xu; |
| 493 | Polygon-to-Polygon Distance Loss for Rotated Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we define a new distance formulation between two convex polygons describing the overlapping degree and non-overlapping degree. |
Yang Yang; Jifeng Chen; Xiaopin Zhong; Yuanlong Deng; |
| 494 | An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: For example, the retrieved knowledge might be noisy and irrelevant to the question, and the re-embedded knowledge features during reasoning might deviate from their original meanings in the knowledge base (KB). To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA. |
Zhengyuan Yang; Zhe Gan; Jianfeng Wang; Xiaowei Hu; Yumao Lu; Zicheng Liu; Lijuan Wang; |
| 495 | ACGNet: Action Complement Graph Network for Weakly-Supervised Temporal Action Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Existing approaches typically leverage off-the-shelf segment-level features, which suffer from spatial incompleteness and temporal incoherence, thus limiting their performance. In this paper, we tackle this problem from a new perspective by enhancing segment-level representations with a simple yet effective graph convolutional network, namely action complement graph network (ACGNet). |
Zichen Yang; Jie Qin; Di Huang; |
| 496 | Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. |
Huifeng Yao; Xiaowei Hu; Xiaomeng Li; |
| 497 | Image Difference Captioning with Pre-training and Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The major challenges of this task lie in two aspects: 1) fine-grained visual differences that require learning stronger vision and language association and 2) high-cost of manual annotations that leads to limited supervised data. To address these challenges, we propose a new modeling framework following the pre-training-finetuning paradigm. |
Linli Yao; Weiying Wang; Qin Jin; |
| 498 | Safe Distillation Box Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework, termed as Safe Distillation Box~(SDB), that allows us to wrap a pre-trained model in a virtual box for intellectual property protection. |
Jingwen Ye; Yining Mao; Jie Song; Xinchao Wang; Cheng Jin; Mingli Song; |
| 499 | Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While these approaches mainly focus on learning node and edge attributes, they completely ignore the 3D geometry of the underlying 3D objects depicted in the 2D images. We fill this gap by proposing a trainable framework that takes advantage of graph neural networks for learning a deformable 3D geometry model from inhomogeneous image collections, i.e. a set of images that depict different instances of objects from the same category. |
Zhenzhang Ye; Tarun Yenamandra; Florian Bernard; Daniel Cremers; |
| 500 | Content-Variant Reference Image Quality Assessment Via Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Although recent no-reference (NR-IQA) methods have made great progress to predict image quality free from the reference image, they still have the potential to achieve better performance since HQ image information is not fully exploited. In contrast, full-reference (FR-IQA) methods tend to provide more reliable quality evaluation, but its practicability is affected by the requirement for pixel-level aligned reference images. |
Guanghao Yin; Wei Wang; Zehuan Yuan; Chuchu Han; Wei Ji; Shouqian Sun; Changhu Wang; |
| 501 | Width & Depth Pruning for Vision Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite achieving remarkable results, these methods take one dimension of network width into consideration and ignore network depth, which is another important dimension for pruning vision transformers. Therefore, we propose a Width & Depth Pruning (WDPruning) framework that reduces both width and depth dimensions simultaneously. |
Fang Yu; Kun Huang; Meng Wang; Yuan Cheng; Wei Chu; Li Cui; |
| 502 | Anisotropic Fourier Features for Neural Image-Based Rendering and Relighting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an anisotropic random Fourier features (RFF) mapping scheme to tackle spectral biases. |
Huangjie Yu; Anpei Chen; Xin Chen; Lan Xu; Ziyu Shao; Jingyi Yu; |
| 503 | Self-Labeling Framework for Novel Category Discovery Over Domains Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a self-labeling framework to cluster all target samples, including those in the ”unknown” categories. |
Qing Yu; Daiki Ikami; Go Irie; Kiyoharu Aizawa; |
| 504 | Efficient Compact Bilinear Pooling Via Kronecker Product Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an efficient compact bilinear pooling method to solve the inefficiency problem inherited in bilinear pooling thoroughly. |
Tan Yu; Yunfeng Cai; Ping Li; |
| 505 | Hybrid Graph Neural Networks for Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This design is problematic because the classifier needs to adapt quickly to new tasks while the embedding does not. To overcome this problem, in this paper we propose a novel hybrid GNN (HGNN) model consisting of two GNNs, an instance GNN and a prototype GNN. |
Tianyuan Yu; Sen He; Yi-Zhe Song; Tao Xiang; |
| 506 | SOIT: Segmenting Objects with Instance-Aware Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents an end-to-end instance segmentation framework, termed SOIT, that Segments Objects with Instance-aware Transformers. |
Xiaodong Yu; Dahu Shi; Xing Wei; Ye Ren; Tingqun Ye; Wenming Tan; |
| 507 | MSML: Enhancing Occlusion-Robustness By Multi-Scale Segmentation-Based Mask Learning for Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing methods generalize poorly due to the distribution distortion induced by unpredictable occlusions. To tackle this problem, we propose a hierarchical segmentation-based mask learning strategy for face recognition, enhancing occlusion-robustness by integrating segmentation representations of occlusion into face recognition in the latent space. |
Ge Yuan; Huicheng Zheng; Jiayu Dong; |
| 508 | Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we aim to boost end-to-end models with object-guided statistical priors. Specifically, We propose to utilize a Verb Semantic Model (VSM) and use semantic aggregation to profit from this object-guided hierarchy. |
Hangjie Yuan; Mang Wang; Dong Ni; Liangpeng Xu; |
| 509 | Task-Level Self-Supervision for Cross-Domain Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Among various solutions, episodic training progres-sively classifies a series of few-shot tasks and thereby is as-sumed to be beneficial for improving the model’s generalization ability. |
Wang Yuan; Zhizhong Zhang; Cong Wang; Haichuan Song; Yuan Xie; Lizhuang Ma; |
| 510 | Improving 360 Monocular Depth Estimation Via Non-local Dense Prediction Transformer and Joint Supervised and Self-Supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose 360 monocular depth estimation methods which improve on the areas that limited previous studies. |
Ilwi Yun; Hyuk-Jae Lee; Chae Eun Rhee; |
| 511 | Homography Decomposition Networks for Planar Object Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The essential reason behind this problem is that the condition number of such a non-linear system changes unstably when the searching range of the homography parameter space becomes larger. To this end, we propose a novel Homography Decomposition Networks~(HDN) approach that drastically reduces and stabilizes the condition number by decomposing the homography transformation into two groups. |
Xinrui Zhan; Yueran Liu; Jianke Zhu; Yang Li; |
| 512 | Patch Diffusion: A General Module for Face Manipulation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Patch Diffusion (PD) module which can be integrated into the existing face manipulation detection networks to boost the performance. |
Baogen Zhang; Sheng Li; Guorui Feng; Zhenxing Qian; Xinpeng Zhang; |
| 513 | Semi-supervised Object Detection with Adaptive Class-Rebalancing Self-Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While self-training achieves state-of-the-art results in semi-supervised object detection (SSOD), it severely suffers from foreground-background and foreground-foreground imbalances in SSOD. In this paper, we propose an Adaptive Class-Rebalancing Self-Training (ACRST) with a novel memory module called CropBank to alleviate these imbalances and generate unbiased pseudo-labels. |
Fangyuan Zhang; Tianxiang Pan; Bin Wang; |
| 514 | Show Your Faith: Cross-Modal Confidence-Aware Network for Image-Text Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Cross-Modal Confidence-Aware Network to infer the matching confidence that indicates the reliability of matched region-word pairs, which is combined with the local semantic similarities to refine the relevance measurement. |
Huatian Zhang; Zhendong Mao; Kun Zhang; Yongdong Zhang; |
| 515 | SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-resolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, this pipeline is redundant and inefficient for the independent processes, and some inner features could have been shared. Therefore, we present an efficient paradigm to perform Simultaneously Image Colorization and Super-resolution (SCS) and propose an end-to-end SCSNet to achieve this goal. |
Jiangning Zhang; Chao Xu; Jian Li; Yue Han; Yabiao Wang; Ying Tai; Yong Liu; |
| 516 | Energy-Based Generative Cooperative Saliency Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, to model the uncertainty of visual saliency, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over the saliency map given an input image, and treating the saliency prediction as a sampling process from the learned distribution. |
Jing Zhang; Jianwen Xie; Zilong Zheng; Nick Barnes; |
| 517 | Attention-Based Transformation from Latent Features to Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose AXform, an attention-based method to transform latent features to point clouds. |
Kaiyi Zhang; Ximing Yang; Yuan Wu; Cheng Jin; |
| 518 | Suppressing Static Visual Cues Via Normalizing Flows for Self-Supervised Video Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Despite the great progress in video understanding made by deep convolutional neural networks, feature representation learned by existing methods may be biased to static visual cues. To address this issue, we propose a novel method to suppress static visual cues (SSVC) based on probabilistic analysis for self-supervised video representation learning. |
Manlin Zhang; Jinpeng Wang; Andy J. Ma; |
| 519 | LGD: Label-Guided Self-Distillation for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose the first self-distillation framework for general object detection, termed LGD (Label-Guided self-Distillation). |
Peizhen Zhang; Zijian Kang; Tong Yang; Xiangyu Zhang; Nanning Zheng; Jian Sun; |
| 520 | Uncertainty Modeling with Second-Order Transformer for Group Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The key challenge of G-ReID is that all the cases of the intra-group member and layout variations are hard to exhaust. To this end, we propose a novel uncertainty modeling, which treats each image as a distribution depending on the current member and layout, then digs out potential group features by random samplings. |
Quan Zhang; Jian-Huang Lai; Zhanxiang Feng; Xiaohua Xie; |
| 521 | Deep Spatial Adaptive Network for Real Image Demosaicing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a deep spatial adaptive network (SANet) for real image demosaicing, which can adaptively learn the nonlinear mapping function for different locations. |
Tao Zhang; Ying Fu; Cheng Li; |
| 522 | MAGIC: Multimodal RelAtional Graph AdversarIal InferenCe for Diverse and Unpaired Text-Based Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the Multimodal relAtional Graph adversarIal InferenCe (MAGIC) framework for diverse and unpaired TextCap. |
Wenqiao Zhang; Haochen Shi; Jiannan Guo; Shengyu Zhang; Qingpeng Cai; Juncheng Li; Sihui Luo; Yueting Zhuang; |
| 523 | Class Guided Channel Weighting Network for Fine-Grained Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Due to the high similarity of different sub-categories and large variations in poses, scales, rotations, and color of the same sub-category in the fine-grained image set, the performance of traditional semantic segmentation methods will decline sharply. To alleviate these dilemmas, a new approach, named Class Guided Channel Weighting Network (CGCWNet), is developed in this paper to enable fine-grained semantic segmentation. |
Xiang Zhang; Wanqing Zhao; Hangzai Luo; Jinye Peng; Jianping Fan; |
| 524 | Context-Based Contrastive Learning for Scene Text Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: On the contrary to the superior accuracy of the seen text, models are prone to misrecognize unseen text even with good image quality. We propose a novel framework, Context-based contrastive learning (ConCLR), to alleviate this issue. |
Xinyun Zhang; Binwu Zhu; Xufeng Yao; Qi Sun; Ruiyu Li; Bei Yu; |
| 525 | Learning Network Architecture for Open-Set Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we make the first attempt to tackle OSR by searching the architecture of a Neural Network (NN) under the open-set assumption. |
Xuelin Zhang; Xuelian Cheng; Donghao Zhang; Paul Bonnington; Zongyuan Ge; |
| 526 | An Adversarial Framework for Generating Unseen Images By Activation Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim to tackle the case where information about the target class is completely removed from the image set. |
Yang Zhang; Wang Zhou; Gaoyuan Zhang; David Cox; Shiyu Chang; |
| 527 | Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, taking into account the degree of similarity of sampled instances as the intermediate state, we propose a novel pretext task – spatio-temporal overlap rate (STOR) prediction. |
Yujia Zhang; Lai-Man Po; Xuyuan Xu; Mengyang Liu; Yexin Wang; Weifeng Ou; Yuzhi Zhao; Wing-Yin Yu; |
| 528 | Pose-Invariant Face Recognition Via Adaptive Angular Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a novel method to learn pose-invariant feature representation without normalizing profile faces to frontal ones or learning disentangled features. |
Zhenduo Zhang; Yongru Chen; Wenming Yang; Guijin Wang; Qingmin Liao; |
| 529 | End-to-End Learning The Partial Permutation Matrix for Robust 3D Point Cloud Registration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Alternatively, the soft matching-based methods have been proposed to learn the matching probability rather than hard assignment. |
Zhiyuan Zhang; Jiadai Sun; Yuchao Dai; Dingfu Zhou; Xibin Song; Mingyi He; |
| 530 | PetsGAN: Rethinking Priors for Single Image Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The main contributions of this paper include: 1) We interpret single image generation from the perspective of the general generative task, that is, to learn a diverse distribution from the Dirac distribution composed of a single image. |
Zicheng Zhang; Yinglu Liu; Congying Han; Hailin Shi; Tiande Guo; Bowen Zhou; |
| 531 | Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way. |
Zizhao Zhang; Han Zhang; Long Zhao; Ting Chen; Sercan Ö. Arik; Tomas Pfister; |
| 532 | OA-FSUI2IT: A Novel Few-Shot Cross Domain Object Detection Framework with Object-Aware Few-Shot Unsupervised Image-to-Image Translation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unsupervised image-to-image (UI2I) translation methods aim to learn a mapping between different visual domains with well-preserved content and consistent structure. |
Lifan Zhao; Yunlong Meng; Lin Xu; |
| 533 | Static-Dynamic Co-teaching for Class-Incremental 3D Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the unexplored yet important class-incremental 3D object detection problem and present the first solution – SDCoT, a novel static-dynamic co-teaching method. |
Na Zhao; Gim Hee Lee; |
| 534 | Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, due to the nature of irregular structure, CNNs-based denosing strategies cannot be trivially applied for meshes. To circumvent this limitation, in the paper, we propose the local surface descriptor (LSD), which is able to transform the local deformable surface around a face into 2D grid representation and thus facilitates the deployment of CNNs to generate denoised face normals. |
Wenbo Zhao; Xianming Liu; Junjun Jiang; Debin Zhao; Ge Li; Xiangyang Ji; |
| 535 | Boosting Generative Zero-Shot Learning By Synthesizing Diverse Features with Attribute Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Therefore, the generated data from attribute could have incomplete semantics. Based on this fact, we propose a novel framework to boost ZSL by synthesizing diverse features. |
Xiaojie Zhao; Yuming Shen; Shidong Wang; Haofeng Zhang; |
| 536 | Self-Supervised Pretraining for RGB-D Salient Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we utilize self-supervised representation learning (SSL) to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. |
Xiaoqi Zhao; Youwei Pang; Lihe Zhang; Huchuan Lu; Xiang Ruan; |
| 537 | Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For some tail classes, the features of their instances are distinct and discriminative, which can also bring satisfactory accuracy; for some head classes, although with sufficient samples, the high semantic similarity with other classes and lack of discriminative features will bring bad accuracy. Based on these observations, we propose Adaptive Logit Adjustment Loss (ALA Loss) to apply an adaptive adjusting term to the logit. |
Yan Zhao; Weicong Chen; Xu Tan; Kai Huang; Jihong Zhu; |
| 538 | CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a novel CAscade Deep REinforcement learning framework, CADRE, to achieve model-free vision-based autonomous urban driving. |
Yinuo Zhao; Kun Wu; Zhiyuan Xu; Zhengping Che; Qi Lu; Jian Tang; Chi Harold Liu; |
| 539 | Learning from The Tangram to Solve Mini Visual Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: By recording human experience in solving tangram puzzles, we present the Tangram dataset and show that a pre-trained neural model on the Tangram helps solve some mini visual tasks based on low-resolution vision. |
Yizhou Zhao; Liang Qiu; Pan Lu; Feng Shi; Tian Han; Song-Chun Zhu; |
| 540 | Handling Slice Permutations Variability in Tensor Recovery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we discuss SPV of several key tensor recovery problems theoretically and experimentally. |
Jingjing Zheng; Xiaoqin Zhang; Wenzhe Wang; Xianta Jiang; |
| 541 | Boosting Contrastive Learning with Relation Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We delve into this problem and find that the lightweight model is prone to collapse in semantic space when simply performing instance-wise contrast. To address this issue, we propose a relation-wise contrastive paradigm with Relation Knowledge Distillation (ReKD). |
Kai Zheng; Yuanjiang Wang; Ye Yuan; |
| 542 | Weakly Supervised Video Moment Localization with Contrastive Negative Sample Mining Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel weakly supervised solution by introducing Contrastive Negative sample Mining (CNM). |
Minghang Zheng; Yanjie Huang; Qingchao Chen; Yang Liu; |
| 543 | Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To cope with that, a dual decoupling training framework is proposed in the present study, i.e. clean and noisy data decoupling, and classification and localization task decoupling. |
Shida Zheng; Chenshu Chen; Xiaowei Cai; Tingqun Ye; Wenming Tan; |
| 544 | SCALoss: Side and Corner Aligned Loss for Bounding Box Regression Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose Side Overlap (SO) loss by maximizing the side overlap of two bounding boxes, which puts more penalty for low overlapping bounding box cases. |
Tu Zheng; Shuai Zhao; Yang Liu; Zili Liu; Deng Cai; |
| 545 | SepFusion: Finding Optimal Fusion Structures for Visual Sound Separation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose SepFusion, a novel framework that can smoothly produce optimal fusion structures for visual-sound separation. |
Dongzhan Zhou; Xinchi Zhou; Di Hu; Hang Zhou; Lei Bai; Ziwei Liu; Wanli Ouyang; |
| 546 | Pan-Sharpening with Customized Transformer and Invertible Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, due to the limitation of the convolution operator, long-range spatial features are often not accurately obtained, thus limiting the overall performance. To this end, we propose a novel and effective method by exploiting a customized transformer architecture and information-lossless invertible neural module for long-range dependencies modeling and effective feature fusion in this paper. |
Man Zhou; Jie Huang; Yanchi Fang; Xueyang Fu; Aiping Liu; |
| 547 | Promoting Single-Modal Optical Flow Network for Diverse Cross-Modal Flow Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To verify our hypothesis, we design a self-supervised framework to promote the single-modal optical flow networks for diverse corss-modal flow estimation. |
Shili Zhou; Weimin Tan; Bo Yan; |
| 548 | Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. |
Wujie Zhou; Shaohua Dong; Caie Xu; Yaguan Qian; |
| 549 | TiGAN: Text-Based Interactive Image Generation and Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework for Text-based Interactive image generation and manipulation (TiGAN) that responds to users’ natural-language feedback. |
Yufan Zhou; Ruiyi Zhang; Jiuxiang Gu; Chris Tensmeyer; Tong Yu; Changyou Chen; Jinhui Xu; Tong Sun; |
| 550 | Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose Cross-Domain Empirical Risk Minimization (xERM) for training an unbiased test-agnostic model to achieve strong performances on both test distributions, which empirically demonstrates that xERM fundamentally improves the classification by learning better feature representation rather than the "head vs. tail" game. |
Beier Zhu; Yulei Niu; Xian-Sheng Hua; Hanwang Zhang; |
| 551 | Deep Recurrent Neural Network with Multi-Scale Bi-directional Propagation for Video Deblurring Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring. |
Chao Zhu; Hang Dong; Jinshan Pan; Boyang Liang; Yuhao Huang; Lean Fu; Fei Wang; |
| 552 | I Can Find You! Boundary-Guided Separated Attention Network for Camouflaged Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By simulating how humans to discover the so-called ‘perfectly’-camouflaged object, we present a novel boundary-guided separated attention network (call BSA-Net). |
Hongwei Zhu; Peng Li; Haoran Xie; Xuefeng Yan; Dong Liang; Dapeng Chen; Mingqiang Wei; Jing Qin; |
| 553 | MoCaNet: Motion Retargeting In-the-Wild Via Canonicalization Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel framework that brings the 3D motion retargeting task from controlled environments to in-the-wild scenarios. |
Wentao Zhu; Zhuoqian Yang; Ziang Di; Wayne Wu; Yizhou Wang; Chen Change Loy; |
| 554 | Robust Depth Completion with Uncertainty-Driven Loss Functions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion. |
Yufan Zhu; Weisheng Dong; Leida Li; Jinjian Wu; Xin Li; Guangming Shi; |
| 555 | Efficient Model-Driven Network for Shadow Removal Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To effectively solve the variational problem, we design an iterative algorithm and unfold it into a deep network, naturally increasing the interpretability of the deep model. |
Yurui Zhu; Zeyu Xiao; Yanchi Fang; Xueyang Fu; Zhiwei Xiong; Zheng-Jun Zha; |
| 556 | Learning Disentangled Classification and Localization Representations for Temporal Action Localization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We evaluate our proposed method on two popular benchmarks for TAL, which outperforms all state-of-the-art methods. |
Zixin Zhu; Le Wang; Wei Tang; Ziyi Liu; Nanning Zheng; Gang Hua; |
| 557 | ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an ACDNet based on the adaptively combined dilated convolution to predict the dense depth map for a monocular panoramic image. |
Chuanqing Zhuang; Zhengda Lu; Yiqun Wang; Jun Xiao; Ying Wang; |
| 558 | Making Adversarial Examples More Transferable and Indistinguishable Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, most of the approaches based on fast gradient sign attack series cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose a method, called Adam Iterative Fast Gradient Tanh Method (AI-FGTM), to generate indistinguishable adversarial examples with high transferability. |
Junhua Zou; Yexin Duan; Boyu Li; Wu Zhang; Yu Pan; Zhisong Pan; |
| 559 | Undercover Boolean Matrix Factorization with MaxSAT Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The k-undercover Boolean matrix factorization problem aims to approximate a m×n Boolean matrix X as the Boolean product of an m×k and a k×n matrices A◦B such that X is a cover of A◦B, i.e., no representation error is allowed on the 0’s entries of the matrix X. To infer an optimal and “block-optimal” k-undercover, we propose two exact methods based on MaxSAT encodings. |
Florent Avellaneda; Roger Villemaire; |
| 560 | Achieving Zero Constraint Violation for Constrained Reinforcement Learning Via Primal-Dual Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To achieve that, we advocate the use of a randomized primal-dual approach to solve the CMDP problems and propose a conservative stochastic primal-dual algorithm (CSPDA) which is shown to exhibit O(1/epsilon^2) sample complexity to achieve epsilon-optimal cumulative reward with zero constraint violations. |
Qinbo Bai; Amrit Singh Bedi; Mridul Agarwal; Alec Koppel; Vaneet Aggarwal; |
| 561 | GEQCA: Generic Qualitative Constraint Acquisition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose GEACQ, which stands for Generic Qualitative Constraint Acquisition, an active CA method that learns qualitative constraints via the concept of qualitative queries. |
Mohamed-Bachir Belaid; Nassim Belmecheri; Arnaud Gotlieb; Nadjib Lazaar; Helge Spieker; |
| 562 | Certified Symmetry and Dominance Breaking for Combinatorial Optimisation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Building on the cutting planes proof system, we develop a certification method for optimisation problems in which symmetry and dominance breaking are easily expressible. |
Bart Bogaerts; Stephan Gocht; Ciaran McCreesh; Jakob Nordström; |
| 563 | The Perils of Learning Before Optimizing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Typically, learning the prediction model used to generate the optimization problem and solving that problem are performed in two separate stages. |
Chris Cameron; Jason Hartford; Taylor Lundy; Kevin Leyton-Brown; |
| 564 | A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In a wide variety of applications including online advertising, contractual hiring, and wireless scheduling, the controller is constrained by a stringent budget constraint on the available resources, which are consumed in a random amount by each action, and a stochastic feasibility constraint that may impose important operational limitations on decision-making. In this work, we consider a general model to address such problems, where each action returns a random reward, cost, and penalty from an unknown joint distribution, and the decision-maker aims to maximize the total reward under a budget constraint B on the total cost and a stochastic constraint on the time-average penalty. |
Semih Cayci; Yilin Zheng; Atilla Eryilmaz; |
| 565 | Resolving Inconsistencies in Simple Temporal Problems: A Parameterized Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of resolving inconsistency of data encoded in the STP. |
Konrad K. Dabrowski; Peter Jonsson; Sebastian Ordyniak; George Osipov; |
| 566 | Efficient Riemannian Meta-Optimization By Implicit Differentiation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an efficient Riemannian meta-optimization method that decouples the complex computation scheme from the meta-gradient. |
Xiaomeng Fan; Yuwei Wu; Zhi Gao; Yunde Jia; Mehrtash Harandi; |
| 567 | Faster Algorithms for Weak Backdoors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We design a new algorithm for WB(3CNF, 0-Val) by reducing it to a local search variant of 3-SAT. |
Serge Gaspers; Andrew Kaploun; |
| 568 | A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we propose a novel divide and conquer algorithm based on transition points to reason over exact optimization problems and predict the coefficients using the optimization loss. |
Ali Ugur Guler; Emir Demirović; Jeffrey Chan; James Bailey; Christopher Leckie; Peter J. Stuckey; |
| 569 | Computing Diverse Shortest Paths Efficiently: A Theoretical and Experimental Study Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Finding diverse solutions in combinatorial problems recently has received considerable attention (Baste et al. 2020; Fomin et al. 2020; Hanaka et al. 2021). In this paper we study the following type of problems: given an integer k, the problem asks for k solutions such that the sum of pairwise (weighted) Hamming distances between these solutions is maximized. |
Tesshu Hanaka; Yasuaki Kobayashi; Kazuhiro Kurita; See Woo Lee; Yota Otachi; |
| 570 | Optimizing Binary Decision Diagrams with MaxSAT for Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In fact, due to their structure (especially with small sizes), these models are inherently understandable by humans. Recently, several exact methods for computing such models are proposed to overcome weaknesses of traditional heuristic methods by providing more compact models or better prediction quality. |
Hao Hu; Marie-José Huguet; Mohamed Siala; |
| 571 | Using MaxSAT for Efficient Explanations of Tree Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by the inherent propositional nature of TEs, this paper proposes to circumvent the need for linear constraints and instead employ an optimization engine for pure propositional logic to efficiently handle the prediction. |
Alexey Ignatiev; Yacine Izza; Peter J. Stuckey; Joao Marques-Silva; |
| 572 | Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose BaMCTS, a Monte Carlo Tree Search framework for finding backdoors to MIPs. |
Elias B. Khalil; Pashootan Vaezipoor; Bistra Dilkina; |
| 573 | Learning to Search in Local Branching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we investigate the relation between the size of the search neighborhood and the behavior of the underlying LB algorithm, and we devise a leaning-based framework for guiding the neighborhood search of the LB heuristic. |
Defeng Liu; Matteo Fischetti; Andrea Lodi; |
| 574 | Analysis of Pure Literal Elimination Rule for Non-uniform Random (MAX) K-SAT Problem with An Arbitrary Degree Distribution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we analyse the performance of the pure literal elimination rule. |
Oleksii Omelchenko; Andrei A. Bulatov; |
| 575 | The SoftCumulative Constraint with Quadratic Penalty Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a checker and a filtering algorithm for the SoftCumulative, which are inspired by the powerful energetic reasoning rule for the Cumulative. |
Yanick Ouellet; Claude-Guy Quimper; |
| 576 | Efficient Vertex-Oriented Polytopic Projection for Web-Scale Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the projections lie on vertices of the polytopes. To do these projections efficiently we derive a vertex-oriented incremental algorithm to project a point onto any arbitrary polytope, as well as give specific algorithms to cater to simplex projection and polytopes where the unit box is cut by planes. |
Rohan Ramanath; S. Sathiya Keerthi; Yao Pan; Konstantin Salomatin; Kinjal Basu; |
| 577 | A Variant of Concurrent Constraint Programming on GPU Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: One reason is that constraint solvers were primarily designed within the mental frame of sequential computation. To solve this issue, we take a step back and contribute to a simple, intrinsically parallel, lock-free and formally correct programming language based on concurrent constraint programming. |
Pierre Talbot; Frédéric G Pinel; Pascal Bouvry; |
| 578 | Real-Time Driver-Request Assignment in Ridesourcing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To deal with the online nature of this problem, existing literature either divides the time horizon into short windows and applies a static offline assignment algorithm within each window or assumes a fully online setting that makes decisions for each request immediately upon its arrival. In this paper, we propose a more realistic model for the driver-request assignment that bridges the above two settings together. |
Hao Wang; Xiaohui Bei; |
| 579 | Encoding Multi-Valued Decision Diagram Constraints As Binary Constraint Trees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a new compact representation called Binary Constraint Tree (BCT). |
Ruiwei Wang; Roland H.C. Yap; |
| 580 | Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we show that SAA remains tractable when the distribution of unknown parameters is only observable through dependent instances and still enjoys asymptotic consistency and finite sample guarantees. |
Yafei Wang; Bo Pan; Wei Tu; Peng Liu; Bei Jiang; Chao Gao; Wei Lu; Shangling Jui; Linglong Kong; |
| 581 | A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a model-free reinforcement learning (RL) algorithm for infinite-horizon average-reward Constrained Markov Decision Processes (CMDPs). |
Honghao Wei; Xin Liu; Lei Ying; |
| 582 | TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, existing hard-label adversarial attack techniques use the genetic algorithm to optimize discrete text data by maintaining a number of adversarial candidates during optimization, which can lead to the problem of generating low-quality adversarial examples in the tight-budget setting. To solve this problem, in this paper, we propose a new method named TextHoaxer by formulating the budgeted hard-label adversarial attack task on text data as a gradient-based optimization problem of perturbation matrix in the continuous word embedding space. |
Muchao Ye; Chenglin Miao; Ting Wang; Fenglong Ma; |
| 583 | Two Compacted Models for Efficient Model-Based Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we proposed two new diagnosis models, namely, the Compacted Model with Multiple Observations (CMMO) and the Dominated-based Compacted Model with Multiple Observations (D-CMMO), to solve the problem in which a considerable amount of time is needed when multiple observations are given and more than one fault is injected. |
Huisi Zhou; Dantong Ouyang; Xiangfu Zhao; Liming Zhang; |
| 584 | Parameterized Approximation Algorithms for K-center Clustering and Variants Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we give a faster algorithm for small dimensions: roughly speaking an O^*(2^{O((1/epsilon)^{O(d)} k^{1-1/d} log k)})-time (1+epsilon)-approximation. |
Sayan Bandyapadhyay; Zachary Friggstad; Ramin Mousavi; |
| 585 | How to Find A Good Explanation for Clustering? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the first question, we introduce a new model of explainable clustering. |
Sayan Bandyapadhyay; Fedor Fomin; Petr A Golovach; William Lochet; Nidhi Purohit; Kirill Simonov; |
| 586 | Regularizing Graph Neural Networks Via Consistency-Diversity Graph Augmentations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose two metrics, Consistency and Diversity, from the aspects of augmentation correctness and generalization. |
Deyu Bo; Binbin Hu; Xiao Wang; Zhiqiang Zhang; Chuan Shi; Jun Zhou; |
| 587 | Two-Stage Octave Residual Network for End-to-End Image Compression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, the correlation between multi-frequency latents produced by OctConv is not utilized in current architectures. In this paper, to address these problems, we propose a novel Two-stage Octave Residual (ToRes) block which strips the sampling operation from OctConv to strengthen the capability of preserving useful information. |
Fangdong Chen; Yumeng Xu; Li Wang; |
| 588 | DANets: Deep Abstract Networks for Tabular Data Classification and Regression Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. |
Jintai Chen; Kuanlun Liao; Yao Wan; Danny Z. Chen; Jian Wu; |
| 589 | Fuzzy Logic Based Logical Query Answering on Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We thus present FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL queries over KGs. |
Xuelu Chen; Ziniu Hu; Yizhou Sun; |
| 590 | TAG: Learning Timed Automata from Logs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider real-time systems, which can be modeled with Timed Automata: our approach is thus a Timed Automata learner. |
Lénaïg Cornanguer; Christine Largouët; Laurence Rozé; Alexandre Termier; |
| 591 | Differentially Describing Groups of Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To perform graph group analysis, we introduce Gragra, which uses maximum entropy modeling to identify a non-redundant set of subgraphs with statistically significant associations to one or more graph groups. |
Corinna Coupette; Sebastian Dalleiger; Jilles Vreeken; |
| 592 | Molecular Contrastive Learning with Chemical Element Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. |
Yin Fang; Qiang Zhang; Haihong Yang; Xiang Zhuang; Shumin Deng; Wen Zhang; Ming Qin; Zhuo Chen; Xiaohui Fan; Huajun Chen; |
| 593 | Heterogeneity-Aware Twitter Bot Detection with Relational Graph Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: State-of-the-art approaches generally leverage the topological structure of the Twittersphere, while they neglect the heterogeneity of relations and influence among users. In this paper, we propose a novel bot detection framework to alleviate this problem, which leverages the topological structure of user-formed heterogeneous graphs and models varying influence intensity between users. |
Shangbin Feng; Zhaoxuan Tan; Rui Li; Minnan Luo; |
| 594 | Subspace Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose subspace differential privacy, to honestly characterize the dependence of the sanitized output on confidential aspects of the data. |
Jie Gao; Ruobin Gong; Fang-Yi Yu; |
| 595 | Orthogonal Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Through a number of experimental observations, we argue that the main factor degrading the performance is the unstable forward normalization and backward gradient resulted from the improper design of the feature transformation, especially for shallow GNNs where the over-smoothing has not happened. Therefore, we propose a novel orthogonal feature transformation, named Ortho-GConv, which could generally augment the existing GNN backbones to stabilize the model training and improve the model’s generalization performance. |
Kai Guo; Kaixiong Zhou; Xia Hu; Yu Li; Yi Chang; Xin Wang; |
| 596 | Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, the retrieval problem of such sequences remains largely unaddressed in literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences. |
Vinayak Gupta; Srikanta Bedathur; Abir De; |
| 597 | GNN-Retro: Retrosynthetic Planning with Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To get a better performance, we propose a new framework, named GNN-Retro, for retrosynthetic planning problem by combining graph neural networks(GNN) and the latest search algorithm. |
Peng Han; Peilin Zhao; Chan Lu; Junzhou Huang; Jiaxiang Wu; Shuo Shang; Bin Yao; Xiangliang Zhang; |
| 598 | Block Modeling-Guided Graph Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modelling into the framework of GCN so that it can realize “block-guided classified aggregation”, and automatically learn the corresponding aggregation rules for neighbors of different classes. |
Dongxiao He; Chundong Liang; Huixin Liu; Mingxiang Wen; Pengfei Jiao; Zhiyong Feng; |
| 599 | CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we think that pre-defined graph or general learning method is difficult due to their irregular structure. |
Hui He; Qi Zhang; Simeng Bai; Kun Yi; Zhendong Niu; |
| 600 | FPAdaMetric: False-Positive-Aware Adaptive Metric Learning for Session-Based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Numerous recommendation algorithms based on collaborative filtering have leveraged post-click user behavior (e.g., skip) to identify false positives. |
Jongwon Jeong; Jeong Choi; Hyunsouk Cho; Sehee Chung; |
| 601 | STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. |
Jiahao Ji; Jingyuan Wang; Zhe Jiang; Jiawei Jiang; Hu Zhang; |
| 602 | Naming The Most Anomalous Cluster in Hilbert Space for Structures with Attribute Information Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our goal is to use this side-information to provide explain- able kernel-based clustering. To this end, we propose a method which is able to find among all possible entity subsets that can be described as a conjunction of the available predicates either a) the optimal cluster within the Reproducing Kernel Hilbert Space, or b) the most anomalous subset within the same space. |
Janis Kalofolias; Jilles Vreeken; |
| 603 | Meta-Learning for Online Update of Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose MeLON, a meta-learning based novel online recommender update strategy that supports two-directional flexibility. |
Minseok Kim; Hwanjun Song; Yooju Shin; Dongmin Park; Kijung Shin; Jae-Gil Lee; |
| 604 | The Triangle-Densest-K-Subgraph Problem: Hardness, Lovász Extension, and Application to Document Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By judiciously exploiting the structure of the problem, we propose a relaxation algorithm for the purpose of obtaining high-quality, sub-optimal solutions. |
Aritra Konar; Nicholas D. Sidiropoulos; |
| 605 | Obtaining Calibrated Probabilities with Personalized Ranking Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. |
Wonbin Kweon; SeongKu Kang; Hwanjo Yu; |
| 606 | DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel method DDG-DA, that can effectively forecast the evolution of data distribution and improve the performance of models. |
Wendi Li; Xiao Yang; Weiqing Liu; Yingce Xia; Jiang Bian; |
| 607 | Unsupervised Anomaly Detection By Robust Density Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose RobustRealNVP, a deep density estimation framework that enhances the robustness of flow-based density estimation methods, enabling their application to unsupervised anomaly detection. |
Boyang Liu; Pang-Ning Tan; Jiayu Zhou; |
| 608 | From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, matching heterogeneous graphs with partial overlap remains a challenging problem in real-world applications. This paper proposes the first practical learning-to-match method to meet this challenge. |
Weijie Liu; Hui Qian; Chao Zhang; Jiahao Xie; Zebang Shen; Nenggan Zheng; |
| 609 | TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We address the task of link forecasting on temporal knowledge graphs and introduce TLogic, an explainable framework that is based on temporal logical rules extracted via temporal random walks. |
Yushan Liu; Yunpu Ma; Marcel Hildebrandt; Mitchell Joblin; Volker Tresp; |
| 610 | Transferring The Contamination Factor Between Anomaly Detection Domains By Shape Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Then, estimating the contamination factor for each dataset separately by labeling data would be extremely time-consuming. Therefore, this paper introduces a method for transferring the known contamination factor from one dataset (the source domain) to a related dataset where it is unknown (the target domain). |
Lorenzo Perini; Vincent Vercruyssen; Jesse Davis; |
| 611 | Unifying Knowledge Base Completion with PU Learning to Mitigate The Observation Bias Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on rule-based approaches, which traditionally address the first challenge by making assumptions that enable identifying negative examples, which in turn makes it possible to compute a rule’s confidence or precision. |
Jonas Schouterden; Jessa Bekker; Jesse Davis; Hendrik Blockeel; |
| 612 | A Self-Supervised Mixed-Curvature Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel Self-Supervised Mixed-Curvature Graph Neural Network (SelfMGNN). |
Li Sun; Zhongbao Zhang; Junda Ye; Hao Peng; Jiawei Zhang; Sen Su; Philip S Yu; |
| 613 | MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). |
Ling Sun; Yuan Rao; Xiangbo Zhang; Yuqian Lan; Shuanghe Yu; |
| 614 | Graph Structure Learning with Variational Information Bottleneck Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a novel Variational Information Bottleneck guided Graph Structure Learning framework, namely VIB-GSL, in the perspective of information theory. |
Qingyun Sun; Jianxin Li; Hao Peng; Jia Wu; Xingcheng Fu; Cheng Ji; Philip S Yu; |
| 615 | Heterogeneous Peer Effects in The Linear Threshold Model Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address individual-level differences, we propose causal inference methods for estimating individual thresholds that can more accurately predict whether and when individuals will be affected by their peers. |
Christopher Tran; Elena Zheleva; |
| 616 | Exploring Relational Semantics for Inductive Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel model called CFAG, which utilizes two granularity levels of relational semantics in a coarse-grained aggregator (CG-AGG) and a fine-grained generative adversarial net (FG-GAN), for inductive KGC. |
Changjian Wang; Xiaofei Zhou; Shirui Pan; Linhua Dong; Zeliang Song; Ying Sha; |
| 617 | HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. |
Chenyu Wang; Zongyu Lin; Xiaochen Yang; Jiao Sun; Mingxuan Yue; Cyrus Shahabi; |
| 618 | Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. |
Chunpai Wang; Daniel B. Neill; Feng Chen; |
| 619 | Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This makes it difficult to distinguish the representation of nodes from different classes. To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs. |
Tao Wang; Di Jin; Rui Wang; Dongxiao He; Yuxiao Huang; |
| 620 | ShuttleNet: Position-Aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we focus on objectively judging what and where to return strokes, which is still unexplored in turn-based sports. |
Wei-Yao Wang; Hong-Han Shuai; Kai-Shiang Chang; Wen-Chih Peng; |
| 621 | Event-Aware Multimodal Mobility Nowcasting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. |
Zhaonan Wang; Renhe Jiang; Hao Xue; Flora D. Salim; Xuan Song; Ryosuke Shibasaki; |
| 622 | Discovering Interpretable Data-to-Sequence Generators Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As the resulting optimization problem is NP-hard, we propose the efficient ConSequence algorithm to discover good event-flow graphs from data. |
Boris Wiegand; Dietrich Klakow; Jilles Vreeken; |
| 623 | DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a deep learning framework for long-term prediction of the distribution of extreme values at different locations. |
Tyler Wilson; Pang-Ning Tan; Lifeng Luo; |
| 624 | SmartIdx: Reducing Communication Cost in Federated Learning By Exploiting The CNNs Structures Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, according to our experimental observation, it spends most of the total communication cost on the index of the selected parameters (i.e., their position informa-tion), which is inefficient for FL training. To solve this problem, we propose a FL compression algorithm for convolution neural networks (CNNs), called SmartIdx, by extending the traditional Top-k largest variation selection strategy intothe convolution-kernel-based selection, to reduce the proportion of the index in the overall communication cost and thusachieve a high compression ratio. |
Donglei Wu; Xiangyu Zou; Shuyu Zhang; Haoyu Jin; Wen Xia; Binxing Fang; |
| 625 | Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). |
Xiao-Ming Wu; Xin Luo; Yu-Wei Zhan; Chen-Lu Ding; Zhen-Duo Chen; Xin-Shun Xu; |
| 626 | CoCoS: Enhancing Semi-supervised Learning on Graphs with Unlabeled Data Via Contrastive Context Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To make full use of available data, we propose a generic framework, Contrastive Context Sharing (CoCoS), to enhance the learning capacity of GNNs for semi-supervised tasks. |
Siyue Xie; Da Sun Handason Tam; Wing Cheong Lau; |
| 627 | Ensemble Semi-supervised Entity Alignment Via Cycle-Teaching Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a diversity-aware alignment selection method to choose reliable entity alignment for each aligner. |
Kexuan Xin; Zequn Sun; Wen Hua; Bing Liu; Wei Hu; Jianfeng Qu; Xiaofang Zhou; |
| 628 | Unsupervised Adversarially Robust Representation Learning on Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an unsupervised defense technique to robustify pre-trained deep graph models, so that the perturbations on the input graph can be successfully identified and blocked before the model is applied to different downstream tasks. |
Jiarong Xu; Yang Yang; Junru Chen; Xin Jiang; Chunping Wang; Jiangang Lu; Yizhou Sun; |
| 629 | Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries. |
Jiarong Xu; Yizhou Sun; Xin Jiang; Yanhao Wang; Chunping Wang; Jiangang Lu; Yang Yang; |
| 630 | PolygonE: Modeling N-ary Relational Data As Gyro-Polygons in Hyperbolic Space Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the issues, we propose a gyro-polygon embedding approach to realize n-ary fact integrity keeping and hierarchy capturing, termed as PolygonE. |
Shiyao Yan; Zequn Zhang; Xian Sun; Guangluan Xu; Shuchao Li; Qing Liu; Nayu Liu; Shensi Wang; |
| 631 | Cross-Task Knowledge Distillation in Multi-Task Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper, instead, proposes a Cross-Task Knowledge Distillation framework that attempts to leverage prediction results of one task as supervised signals to teach another task. |
Chenxiao Yang; Junwei Pan; Xiaofeng Gao; Tingyu Jiang; Dapeng Liu; Guihai Chen; |
| 632 | Self-Supervised Graph Neural Networks Via Diverse and Interactive Message Passing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Essentially, the over-smoothing issue is caused by the over-simplification of the existing message passing, which possesses two intrinsic limits: blind message and uniform passing. In this paper, a novel Diverse and Interactive Message Passing (DIMP) is proposed for self-supervised learning by overcoming these limits. |
Liang Yang; Cheng Chen; Weixun Li; Bingxin Niu; Junhua Gu; Chuan Wang; Dongxiao He; Yuanfang Guo; Xiaochun Cao; |
| 633 | Multi-Scale Distillation from Multiple Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing single-teacher GNN knowledge distillation approaches which are based on a single GNN model, are sub-optimal. To this end, we propose a novel approach to distill multi-scale knowledge, which learns from multiple GNN teacher models with different number of layers to capture the topological semantic at different scales. |
Chunhai Zhang; Jie Liu; Kai Dang; Wenzheng Zhang; |
| 634 | Mind The Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. |
Fuwei Zhang; Zhao Zhang; Xiang Ao; Dehong Gao; Fuzhen Zhuang; Yi Wei; Qing He; |
| 635 | Anisotropic Additive Quantization for Fast Inner Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Learning additive quantization with respect to this loss is important since additive quantization can achieve a lower approximation error than product quantization. To this end, we propose a quantization method called Anisotropic Additive Quantization to combine the score-aware anisotropic loss and additive quantization. |
Jin Zhang; Qi Liu; Defu Lian; Zheng Liu; Le Wu; Enhong Chen; |
| 636 | Robust Heterogeneous Graph Neural Networks Against Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on the two facts, we propose a novel robust HGNN framework RoHe against topology adversarial attacks by equipping an attention purifier, which can prune malicious neighbors based on topology and feature. |
Mengmei Zhang; Xiao Wang; Meiqi Zhu; Chuan Shi; Zhiqiang Zhang; Jun Zhou; |
| 637 | Multi-Dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-Task Learning Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The main challenge now is to characterize and predict the guild health in a quantitative, dynamic, and multi-dimensional manner based on complicated multi-media data streams. To this end, we propose a novel framework, namely Stability-Aware Multi-task Learning Approach(SAMLA) to address these challenges. |
Chuang Zhao; Hongke Zhao; Runze Wu; Qilin Deng; Yu Ding; Jianrong Tao; Changjie Fan; |
| 638 | Multi-View Intent Disentangle Graph Networks for Bundle Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: 2) The local view can reveal the association between items under each intent since the items within the same bundle are highly correlated to each other. To this end, in this paper we propose a novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which is capable of precisely and comprehensively capturing the diversity of user intent and items’ associations at the finer granularity. |
Sen Zhao; Wei Wei; Ding Zou; Xianling Mao; |
| 639 | Multi-Type Urban Crime Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we perform crime prediction exploiting the cross-type and spatio-temporal correlations of urban crimes. |
Xiangyu Zhao; Wenqi Fan; Hui Liu; Jiliang Tang; |
| 640 | Forecasting Asset Dependencies to Reduce Portfolio Risk Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we organize pairwise assets dependencies in an Asset Dependency Matrix (ADM) and formulate the problem of assets dependencies forecast to predict the future ADM given a sequence of past ADMs. |
Haoren Zhu; Shih-Yang Liu; Pengfei Zhao; Yingying Chen; Dik Lun Lee; |
| 641 | Defending Graph Convolutional Networks Against Dynamic Graph Perturbations Via Bayesian Self-Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Many existing works aim to strengthen the robustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. |
Jun Zhuang; Mohammad Al Hasan; |
| 642 | Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we derive a set of benchmarks (BLANCA – Benchmarks for LANguage models on Coding Artifacts) that assess code understanding based on tasks such as predicting the best answer to a question in a forum post, finding related forum posts, or predicting classes related in a hierarchy from class documentation. |
Ibrahim Abdelaziz; Julian Dolby; Jamie McCusker; Kavitha Srinivas; |
| 643 | No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Dichotomous-Polytomous Multi-Task Learning (DP-MTL), a multi-task learning framework that combines KT and OT for more precise student assessment. |
Suyeong An; Junghoon Kim; Minsam Kim; Juneyoung Park; |
| 644 | Diaformer: Automatic Diagnosis Via Symptoms Sequence Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). |
Junying Chen; Dongfang Li; Qingcai Chen; Wenxiu Zhou; Xin Liu; |
| 645 | Zero-Shot Audio Source Separation Through Query-Based Learning from Weakly-Labeled Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a three-component pipeline to train a universal audio source separator from a large, but weakly-labeled dataset: AudioSet. |
Ke Chen; Xingjian Du; Bilei Zhu; Zejun Ma; Taylor Berg-Kirkpatrick; Shlomo Dubnov; |
| 646 | DeepHardMark: Towards Watermarking Neural Network Hardware Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a framework for embedding watermarks into DNN hardware accelerators. |
Joseph Clements; Yingjie Lao; |
| 647 | A Unified Framework for Real Time Motion Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieves a new state-of-the-art accuracy on LaFAN1 (+17% better than previous sota) under multiple evaluation settings. |
Yinglin Duan; Yue Lin; Zhengxia Zou; Yi Yuan; Zhehui Qian; Bohan Zhang; |
| 648 | FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel factor model, FactorVAE, as a probabilistic model with inherent randomness for noise modeling. |
Yitong Duan; Lei Wang; Qizhong Zhang; Jian Li; |
| 649 | AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work presents a novel convolutional neural network (CNN) based architecture designed to learn semantically aligned cross-modal visual and textual representations. |
Ammarah Farooq; Muhammad Awais; Josef Kittler; Syed Safwan Khalid; |
| 650 | SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to encode point clouds into structured color image representation (SCIR) and utilize 2D CNN to fulfill the 3D detection task. |
Qingdong He; Hao Zeng; Yi Zeng; Yijun Liu; |
| 651 | Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. |
Sijie He; Xinyan Li; Laurie Trenary; Benjamin A Cash; Timothy DelSole; Arindam Banerjee; |
| 652 | Solving PDE-Constrained Control Problems Using Operator Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel framework that is generally applicable to solving PDE-constrained optimal control problems by introducing surrogate models for PDE solution operators with special regularizers. |
Rakhoon Hwang; Jae Yong Lee; Jin Young Shin; Hyung Ju Hwang; |
| 653 | Proxy Learning of Visual Concepts of Fine Art Paintings from Styles Through Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. |
Diana Kim; Ahmed Elgammal; Marian Mazzone; |
| 654 | SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with An Autoregressive Embedding Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this study, we propose a novel loss objective combined with COT-GAN based on an autoregressive embedding to reinforce the learning of spatio-temporal dynamics. |
Konstantin Klemmer; Tianlin Xu; Beatrice Acciaio; Daniel B. Neill; |
| 655 | Intra-Inter Subject Self-Supervised Learning for Multivariate Cardiac Signals Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we address the challenges by proposing an Intra-Inter Subject Self-Supervised Learning (ISL) model that is customized for multivariate cardiac signals. |
Xiang Lan; Dianwen Ng; Shenda Hong; Mengling Feng; |
| 656 | GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Whereas, the critical geometric information of molecules remains rarely explored under the current GNN and GCL architectures. To this end, we propose a novel graph contrastive learning method utilizing the geometry of the molecule across 2D and 3D views, which is named GeomGCL. |
Shuangli Li; Jingbo Zhou; Tong Xu; Dejing Dou; Hui Xiong; |
| 657 | OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Lastly, enhancing the cooperation between intersections is needed for large network applications. To address these issues, the Option-Action RL framework for universal Multi-intersection control (OAM) is proposed. |
Enming Liang; Zicheng Su; Chilin Fang; Renxin Zhong; |
| 658 | End-to-End Line Drawing Vectorization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we make an attempt in proposing an end-to-end vectorization method which directly generates vectorized stroke primitives from raster line drawing in one step. |
Hanyuan Liu; Chengze Li; Xueting Liu; Tien-Tsin Wong; |
| 659 | Context-Aware Health Event Prediction Via Transition Functions on Dynamic Disease Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. |
Chang Lu; Tian Han; Yue Ning; |
| 660 | Hyperverlet: A Symplectic Hypersolver for Hamiltonian Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: More specifically, we propose a parameterization of symplectic neural networks and prove that hyperbolic tangent is r-finite expanding the set of allowable activation functions for symplectic neural networks, improving the accuracy. |
Frederik Baymler Mathiesen; Bin Yang; Jilin Hu; |
| 661 | Learning Human Driving Behaviors with Sequential Causal Imitation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Accounting for the latent causal relationships from unobserved variables to outcomes, this paper proposes Sequential Causal Imitation Learning (SeqCIL) for imitating driver behaviors. |
Kangrui Ruan; Xuan Di; |
| 662 | EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for simultaneous dynamic routing and traffic signal control. |
Haoran Su; Yaofeng Desmond Zhong; Biswadip Dey; Amit Chakraborty; |
| 663 | Constrained Prescriptive Trees Via Column Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a scalable method that solves the constrained prescriptive policy generation problem. |
Shivaram Subramanian; Wei Sun; Youssef Drissi; Markus Ettl; |
| 664 | DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Moreover, the dynamics of knowledge information associated with the comments are not involved, either. To this end, we propose a novel Dual-Dynamic Graph Convolutional Networks, termed as DDGCN, which can model the dynamics of messages in propagation as well as the dynamics of the background knowledge from Knowledge graphs in one unified framework. |
Mengzhu Sun; Xi Zhang; Jiaqi Zheng; Guixiang Ma; |
| 665 | Contact-Distil: Boosting Low Homologous Protein Contact Map Prediction By Self-Supervised Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the PCMP accuracy drops dramatically while only poor MSA (e.g., absolute MSA count less than 10) is available. Therefore, in this paper, we propose the Contact-Distil to improve the low homologous PCMP accuracy through knowledge distillation on a self-supervised model. |
Qin Wang; Jiayang Chen; Yuzhe Zhou; Yu Li; Liangzhen Zheng; Sheng Wang; Zhen Li; Shuguang Cui; |
| 666 | EtinyNet: Extremely Tiny Network for TinyML Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, for putting CNNs into storage limited devices, we developed efficient tiny models with only hundreds of KB parameters. |
Kunran Xu; Yishi Li; Huawei Zhang; Rui Lai; Lin Gu; |
| 667 | RepBin: Constraint-Based Graph Representation Learning for Metagenomic Binning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a new formulation using a graph where the nodes are subsequences and edges represent homophily information. |
Hansheng Xue; Vijini Mallawaarachchi; Yujia Zhang; Vaibhav Rajan; Yu Lin; |
| 668 | NSGZero: Efficiently Learning Non-exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel DL-based method, NSGZero, to learn a non-exploitable policy in NSGs. |
Wanqi Xue; Bo An; Chai Kiat Yeo; |
| 669 | RID-Noise: Towards Robust Inverse Design Under Noisy Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To achieve data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing data to train a conditional invertible neural network. |
Jia-Qi Yang; Ke-Bin Fan; Hao Ma; De-Chuan Zhan; |
| 670 | Deepfake Network Architecture Attribution Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. |
Tianyun Yang; Ziyao Huang; Juan Cao; Lei Li; Xirong Li; |
| 671 | ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we propose a single-cell model-based deep graph embedding clustering (scTAG) method, which simultaneously learns cell–cell topology representations and identifies cell clusters based on deep graph convolutional network. |
Zhuohan Yu; Yifu Lu; Yunhe Wang; Fan Tang; Ka-Chun Wong; Xiangtao Li; |
| 672 | DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a new data-driven AI system, namely DeepThermal, to optimize the combustion control strategy for TPGUs. |
Xianyuan Zhan; Haoran Xu; Yue Zhang; Xiangyu Zhu; Honglei Yin; Yu Zheng; |
| 673 | AlphaHoldem: High-Performance Artificial Intelligence for Heads-Up No-Limit Poker Via End-to-End Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present AlphaHoldem, a high-performance and lightweight HUNL AI obtained with an end-to-end self-play reinforcement learning framework. |
Enmin Zhao; Renye Yan; Jinqiu Li; Kai Li; Junliang Xing; |
| 674 | Hierarchical Multi-Supervision Multi-Interaction Graph Attention Network for Multi-Camera Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As such, two major efforts are devoted to facilitating related research and advancing modeling techniques. |
Guoliang Zhao; Yuxun Zhou; Zhanbo Xu; Yadong Zhou; Jiang Wu; |
| 675 | 6DCNN with Roto-Translational Convolution Filters for Volumetric Data Processing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing three-dimensional volumetric data. |
Dmitrii Zhemchuzhnikov; Ilia Igashov; Sergei Grudinin; |
| 676 | Deeply Tensor Compressed Transformers for End-to-End Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, to obtain a compact end-to-end detection framework, we propose to deeply compress the transformers with low-rank tensor decomposition. |
Peining Zhen; Ziyang Gao; Tianshu Hou; Yuan Cheng; Hai-Bao Chen; |
| 677 | Dynamic Manifold Learning for Land Deformation Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present DyLand — Dynamic Manifold Learning with Normalizing Flows for Land deformation prediction — a novel framework for learning dynamic structures of terrain surface and improving the performance of land deformation prediction. |
Fan Zhou; Rongfan Li; Qiang Gao; Goce Trajcevski; Kunpeng Zhang; Ting Zhong; |
| 678 | Fully Adaptive Framework: Neural Computerized Adaptive Testing for Online Education Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a fully adaptive framework named Neural Computerized Adaptive Testing (NCAT), which formally redefines CAT as a reinforcement learning problem and directly learns selection algorithm from real-world data. |
Yan Zhuang; Qi Liu; Zhenya Huang; Zhi Li; Shuanghong Shen; Haiping Ma; |
| 679 | An Algorithmic Introduction to Savings Circles Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we take an algorithmic perspective on the study of roscas. |
Rediet Abebe; Adam Eck; Christian Ikeokwu; Sam Taggart; |
| 680 | Locally Fair Partitioning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We model the societal task of redistricting political districts as a partitioning problem: Given a set of n points in the plane, each belonging to one of two parties, and a parameter k, our goal is to compute a partition P of the plane into regions so that each region contains roughly s = n/k points. |
Pankaj K. Agarwal; Shao-Heng Ko; Kamesh Munagala; Erin Taylor; |
| 681 | Maximizing Nash Social Welfare in 2-Value Instances Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider the problem of maximizing the Nash social welfare when allocating a set G of indivisible goods to a set N of agents. |
Hannaneh Akrami; Bhaskar Ray Chaudhury; Martin Hoefer; Kurt Mehlhorn; Marco Schmalhofer; Golnoosh Shahkarami; Giovanna Varricchio; Quentin Vermande; Ernest van Wijland; |
| 682 | Truth-Tracking Via Approval Voting: Size Matters Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Based on the intuitive idea that more reliable votes contain fewer alternatives, we define several noise models that are approval voting variants of the Mallows model. |
Tahar Allouche; Jérôme Lang; Florian Yger; |
| 683 | Dimensionality and Coordination in Voting: The Distortion of STV Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, motivated by the efficiency of STV, we investigate whether natural learning rules can lead to low-distortion outcomes. Specifically, we introduce simple, deterministic and decentralized exploration/exploitation dynamics, and we show that they converge to a candidate with O(1) distortion. |
Ioannis Anagnostides; Dimitris Fotakis; Panagiotis Patsilinakos; |
| 684 | Fair and Truthful Giveaway Lotteries Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our main contribution is a polynomial mechanism for the problem, which guarantees many of the desired properties, including: leximin optimality, Pareto-optimality, anonymity, group strategy proofness, and adjunctive strategy proofness (which provides that no benefit can be obtained by registering additional – uninterested or bogus – individuals). |
Tal Arbiv; Yonatan Aumann; |
| 685 | Universal and Tight Online Algorithms for Generalized-Mean Welfare Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Parameterized by an exponent term p in (-infty, 1], these means encapsulate a range of welfare functions, including social welfare (p=1), egalitarian welfare (p to -infty), and Nash social welfare (p to 0). We present a simple algorithmic template that takes a threshold as input and, with judicious choices for this threshold, leads to both universal and tailored competitive guarantees. |
Siddharth Barman; Arindam Khan; Arnab Maiti; |
| 686 | Truthful and Fair Mechanisms for Matroid-Rank Valuations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of allocating indivisible goods among strategic agents. |
Siddharth Barman; Paritosh Verma; |
| 687 | Truthful Cake Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study a public goods variant of the problem, where instead of competing with one another for the cake, the agents all share the same subset of the cake which must be chosen subject to a length constraint. |
Xiaohui Bei; Xinhang Lu; Warut Suksompong; |
| 688 | The Secretary Problem with Competing Employers on Random Edge Arrivals Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study a game-theoretic generalization of the secretary problem where a set of employers compete with each other to hire the best candidate. |
Xiaohui Bei; Shengyu Zhang; |
| 689 | Almost Full EFX Exists for Four Agents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Beyond our main result, we introduce a new class of valuations, termed nice cancelable, which includes additive, unit-demand, budget-additive and multiplicative valuations, among others. |
Ben Berger; Avi Cohen; Michal Feldman; Amos Fiat; |
| 690 | Sequential Blocked Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a sequential blocked matching (SBM) model where strategic agents repeatedly report ordinal preferences over a set of services to a central planner. |
Nicholas Bishop; Hau Chan; Debmalya Mandal; Long Tran-Thanh; |
| 691 | Combating Collusion Rings Is Hard But Possible Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce and analyze the problem Cycle-Free Reviewing that aims at finding a review assignment without the following kind of collusion ring: A sequence of reviewers each reviewing a paper authored by the next reviewer in the sequence (with the last reviewer reviewing a paper of the first), thus creating a review cycle where each reviewer gives favorable reviews. |
Niclas Boehmer; Robert Bredereck; André Nichterlein; |
| 692 | Theory of and Experiments on Minimally Invasive Stability Preservation in Changing Two-Sided Matching Markets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Following up on purely theoretical work, we contribute further theoretical insights into adapting stable two-sided matchings to change. |
Niclas Boehmer; Klaus Heeger; Rolf Niedermeier; |
| 693 | A Calculus for Computing Structured Justifications for Election Outcomes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. |
Arthur Boixel; Ulle Endriss; Ronald de Haan; |
| 694 | Single-Agent Dynamics in Additively Separable Hedonic Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While most of the literature focuses on unanimous consent, we also study consent decided by majority vote, and introduce two new stability notions that can be seen as local variants of popularity. We investigate these notions in additively separable hedonic games by pinpointing boundaries to computational complexity depending on the type of consent and restrictions on the utility functions. |
Felix Brandt; Martin Bullinger; Leo Tappe; |
| 695 | On Improving Resource Allocations By Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a sharing concept allowing resources being shared with social network neighbors of the resource owners. To this end, we introduce a formal model that allows a central authority to compute an optimal sharing between neighbors based on an initial allocation. |
Robert Bredereck; Andrzej Kaczmarczyk; Junjie Luo; Rolf Niedermeier; Florian Sachse; |
| 696 | Liquid Democracy with Ranked Delegations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we provide a thorough axiomatic analysis of the space of delegation rules, i.e., functions assigning a feasible delegation path to each delegating agent. |
Markus Brill; Théo Delemazure; Anne-Marie George; Martin Lackner; Ulrike Schmidt-Kraepelin; |
| 697 | Individual Representation in Approval-Based Committee Voting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we formalize the concept of individual representation (IR) and explore to which extent, and under which circumstances, it can be achieved. |
Markus Brill; Jonas Israel; Evi Micha; Jannik Peters; |
| 698 | The Metric Distortion of Multiwinner Voting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We extend the recently introduced framework of metric distortion to multiwinner voting. |
Ioannis Caragiannis; Nisarg Shah; Alexandros A. Voudouris; |
| 699 | A Little Charity Guarantees Fair Connected Graph Partitioning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unfortunately, there exist simple examples in which no partition is reasonably proportional or balanced. To circumvent this, we introduce the idea of charity. |
Ioannis Caragiannis; Evi Micha; Nisarg Shah; |
| 700 | Truthful Aggregation of Budget Proposals with Proportionality Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For three projects, we propose a new, proportional mechanism that is optimal among all moving phantom mechanisms. |
Ioannis Caragiannis; George Christodoulou; Nicos Protopapas; |
| 701 | The Complexity of Learning Approval-Based Multiwinner Voting Rules Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our goal is to learn a target rule (i.e., to learn the corresponding scoring function) using information about the winning committees of a small number of sampled profiles. |
Ioannis Caragiannis; Karl Fehrs; |
| 702 | Efficiency of Ad Auctions with Price Displaying Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most economic reports suggest that almost half of the market value unlocked by artificial intelligence (AI) by the next decade (about 9 trillion USD per year) will be in marketing&sales. |
Matteo Castiglioni; Diodato Ferraioli; Nicola Gatti; Alberto Marchesi; Giulia Romano; |
| 703 | Signaling in Posted Price Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the latter case, we need a custom approximate separation oracle, which we implement with a dynamic programming approach. |
Matteo Castiglioni; Giulia Romano; Alberto Marchesi; Nicola Gatti; |
| 704 | Weighted Fairness Notions for Indivisible Items Revisited Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We revisit the setting of fairly allocating indivisible items when agents have different weights representing their entitlements. |
Mithun Chakraborty; Erel Segal-Halevi; Warut Suksompong; |
| 705 | Pizza Sharing Is PPA-Hard Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the computational complexity of computing solutions for the straight-cut and square-cut pizza sharing problems. |
Argyrios Deligkas; John Fearnley; Themistoklis Melissourgos; |
| 706 | Heterogeneous Facility Location with Limited Resources Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We initiate the study of the heterogeneous facility location problem with limited resources. |
Argyrios Deligkas; Aris Filos-Ratsikas; Alexandros A. Voudouris; |
| 707 | Complexity of Deliberative Coalition Formation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Elkind et al. (AAAI’21) introduced a model for deliberative coalition formation, where a community wishes to identify a strongly supported proposal from a space of alternatives, in order to change the status quo. |
Edith Elkind; Abheek Ghosh; Paul Goldberg; |
| 708 | The Price of Justified Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we investigate the impact of imposing the JR axiom (as well as the more demanding EJR axiom) on social welfare and coverage. |
Edith Elkind; Piotr Faliszewski; Ayumi Igarashi; Pasin Manurangsi; Ulrike Schmidt-Kraepelin; Warut Suksompong; |
| 709 | The Complexity of Subelection Isomorphism Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we propose the Subelection Isomorphism and the Maximum Common Subelection problems and study their computational complexity and approximability. |
Piotr Faliszewski; Krzysztof Sornat; Stanisław Szufa; |
| 710 | Fast Payoff Matrix Sparsification Techniques for Structured Extensive-Form Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To ameliorate the issue, Zhang and Sandholm recently proposed a sparsification technique that factorizes the payoff matrix A into a sparser object A = Â + UVᵀ, where the total combined number of nonzeros of Â, U, and V, is significantly smaller. |
Gabriele Farina; Tuomas Sandholm; |
| 711 | Two-Price Equilibrium Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a new market equilibrium notion, called two-price equilibrium (2PE). |
Michal Feldman; Galia Shabtai; Aner Wolfenfeld; |
| 712 | Algorithmic Bayesian Persuasion with Combinatorial Actions Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Bayesian persuasion is a model for understanding strategic information revelation: an agent with an informational advantage, called a sender, strategically discloses information … |
Kaito Fujii; Shinsaku Sakaue; |
| 713 | Bayesian Persuasion in Sequential Decision-Making Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study a dynamic model of Bayesian persuasion in sequential decision-making settings. |
Jiarui Gan; Rupak Majumdar; Goran Radanovic; Adish Singla; |
| 714 | Hedonic Diversity Games: A Complexity Picture with More Than Two Colors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we design new algorithms accompanied with lower bounds which provide a full parameterized-complexity picture for computing Nash and Individually stable outcomes with respect to the most natural parameterizations of the problem. |
Robert Ganian; Thekla Hamm; Dušan Knop; Šimon Schierreich; Ondřej Suchý; |
| 715 | Fair and Efficient Allocations of Chores Under Bivalued Preferences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of fair and efficient allocation of a set of indivisible chores to agents with additive cost functions. |
Jugal Garg; Aniket Murhekar; John Qin; |
| 716 | Secretary Matching with Vertex Arrivals and No Rejections Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study three related online matching problems with the constraint of matching every vertex, i.e., with no rejections. |
Mohak Goyal; |
| 717 | Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions. |
Lukas Graf; Tobias Harks; Kostas Kollias; Michael Markl; |
| 718 | Multi-Leader Congestion Games with An Adversary Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study a multi-leader single-follower congestion game where multiple users (leaders) choose one resource out of a set of resources and, after observing the realized loads, an adversary (single-follower) attacks the resources with maximum loads causing additional costs for the leaders. |
Tobias Harks; Mona Henle; Max Klimm; Jannik Matuschke; Anja Schedel; |
| 719 | Approval-Based Committee Voting Under Incomplete Information Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider several models of incompleteness where each voter partitions the set of candidates into approved, disapproved, and unknown candidates, possibly with ordinal preference constraints among candidates in the latter category. |
Aviram Imber; Jonas Israel; Markus Brill; Benny Kimelfeld; |
| 720 | Reforming An Envy-Free Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider the problem of reforming an envy-free matching when each agent is assigned a single item. |
Takehiro Ito; Yuni Iwamasa; Naonori Kakimura; Naoyuki Kamiyama; Yusuke Kobayashi; Yuta Nozaki; Yoshio Okamoto; Kenta Ozeki; |
| 721 | The Complexity of Proportionality Degree in Committee Elections Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the complexity of computing committees with a given proportionality degree and of testing if a given committee provides a particular one. |
Łukasz Janeczko; Piotr Faliszewski; |
| 722 | Worst-Case Voting When The Stakes Are High Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the additive distortion of social choice functions in the implicit utilitarian model, and argue that it is a more appropriate metric than multiplicative distortion when an alternative that confers significant social welfare may exist (i.e., when the stakes are high). We define a randomized analog of positional scoring rules, and present a rule which is asymptotically optimal within this class as the number of alternatives increases. |
Anson Kahng; Gregory Kehne; |
| 723 | PageRank for Edges: Axiomatic Characterization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: With this paper, we initiate the discussion on the axiomatic properties of edge centrality measures. |
Natalia Kucharczuk; Tomasz Wąs; Oskar Skibski; |
| 724 | Safe Subgame Resolving for Extensive Form Correlated Equilibrium Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we (i) lay out the foundations to quantify the quality of a refined strategy, in terms of the social welfare and exploitability of correlation plans, (ii) show that EFCEs possess a sufficient amount of independence between subgames to perform resolving efficiently, and (iii) provide two algorithms for resolving, one using linear programming and the other based on regret minimization. |
Chun Kai Ling; Fei Fang; |
| 725 | The Semi-random Likelihood of Doctrinal Paradoxes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we characterize the likelihood of the doctrinal paradox under a general and realistic model called semi-random social choice framework (proposed by Xia in 2020). |
Ao Liu; Lirong Xia; |
| 726 | Is There A Strongest Die in A Set of Dice with The Same Mean Pips? Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Jan-ken, a.k.a. rock-paper-scissors, is a cerebrated example of a non-transitive game with three (pure) strategies, rock, paper and scissors. Interestingly, any Jan-ken … |
Shang Lu; Shuji Kijima; |
| 727 | Choices Are Not Independent: Stackelberg Security Games with Nested Quantal Response Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the nested QR adversary model (based on nested logit model in discrete choice theory) in SSG which addresses shortcoming of the QR model. |
Tien Mai; Arunesh Sinha; |
| 728 | Strictly Proper Contract Functions Can Be Arbitrage-Free Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider mechanisms for truthfully eliciting probabilistic predictions from a group of experts. |
Eric Neyman; Tim Roughgarden; |
| 729 | Characterization of Incentive Compatibility of An Ex-ante Constrained Player Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a variant of the standard Bayesian mechanism, where players evaluate their outcomes and constraints in an ex-ante manner. |
Bonan Ni; Pingzhong Tang; |
| 730 | Online Elicitation of Necessarily Optimal Matchings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of eliciting preferences of agents in the house allocation model. |
Jannik Peters; |
| 731 | Generalized Dynamic Cognitive Hierarchy Models for Strategic Driving Behavior Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While there has been an increasing focus on the use of game theoretic models for autonomous driving, empirical evidence shows that there are still open questions around dealing with the challenges of common knowledge assumptions as well as modeling bounded rationality. To address some of these practical challenges, we develop a framework of generalized dynamic cognitive hierarchy for both modelling naturalistic human driving behavior as well as behavior planning for autonomous vehicles (AV). |
Atrisha Sarkar; Kate Larson; Krzysztof Czarnecki; |
| 732 | Improved Maximin Guarantees for Subadditive and Fractionally Subadditive Fair Allocation Problem Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study the maximin share fairness notion for allocation of indivisible goods in the subadditive and fractionally subadditive settings. |
Masoud Seddighin; Saeed Seddighin; |
| 733 | Proportional Public Decisions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We formulate new criteria of proportionality and analyse two rules, Proportional Approval Voting and the Method of Equal Shares, that are inspired by the corresponding approval-based committee election rules. |
Piotr Skowron; Adrian Górecki; |
| 734 | Online Task Assignment Problems with Reusable Resources Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The setting generalizes that of existing work in which an online task is assigned to one agent under (1). In this paper, we propose an online algorithm that is 1/2-competitive for the above setting, which is tight. |
Hanna Sumita; Shinji Ito; Kei Takemura; Daisuke Hatano; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi; |
| 735 | Iterative Calculus of Voting Under Plurality Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We formalize a voting model for plurality elections that combines Iterative Voting and Calculus of Voting. |
Fabricio Vasselai; |
| 736 | Coordinating Followers to Reach Better Equilibria: End-to-End Gradient Descent for Stackelberg Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Using the unbiased gradient estimate, we implement the gradient-based approach to solve three Stackelberg problems with multiple followers. |
Kai Wang; Lily Xu; Andrew Perrault; Michael K. Reiter; Milind Tambe; |
| 737 | Multi-Unit Auction in Social Networks with Budgets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study multi-unit auctions in social networks, where each buyer has a fixed budget and can spread the sale information to the network neighbors. |
Mingyu Xiao; Yuchao Song; Bakh Khoussainov; |
| 738 | The Strange Role of Information Asymmetry in Auctions—Does More Accurate Value Estimation Benefit A Bidder? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the second-price auction in which bidders have asymmetric information regarding the item’s value. |
Haifeng Xu; Ruggiero Cavallo; |
| 739 | AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work proposes to meta-learn novel CFR algorithms through evolution to ease the burden of manual algorithm design. |
Hang Xu; Kai Li; Haobo Fu; Qiang Fu; Junliang Xing; |
| 740 | Team Correlated Equilibria in Zero-Sum Extensive-Form Games Via Tree Decompositions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we devise a completely new algorithm for solving team games. |
Brian Hu Zhang; Tuomas Sandholm; |
| 741 | Planning with Participation Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We give a fully polynomial-time approximation scheme for this problem. En route, we present polynomial-time algorithms for computing (exact) optimal policies for important special cases of this problem, including when the time horizon is constant, or when the MDP exhibits a "definitive decisions" property. |
Hanrui Zhang; Yu Cheng; Vincent Conitzer; |
| 742 | “I Don’t Think So”: Summarizing Policy Disagreements for Agent Comparison Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method for generating dependent and contrastive summaries that emphasize the differences between agent policies by identifying states in which the agents disagree on the best course of action. |
Yotam Amitai; Ofra Amir; |
| 743 | Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. |
Siddhant Arora; Danish Pruthi; Norman Sadeh; William W. Cohen; Zachary C. Lipton; Graham Neubig; |
| 744 | Role of Human-AI Interaction in Selective Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recent work has shown the potential benefit of selective prediction systems that can learn to defer to a human when the predictions of the AI are unreliable, particularly to improve the reliability of AI systems in high-stakes applications like healthcare or conservation. |
Elizabeth Bondi; Raphael Koster; Hannah Sheahan; Martin Chadwick; Yoram Bachrach; Taylan Cemgil; Ulrich Paquet; Krishnamurthy Dvijotham; |
| 745 | How General-Purpose Is A Language Model? Usefulness and Safety with Human Prompters in The Wild Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel theoretical evaluation framework and a distinctive experimental study assessing language models as general-purpose systems when used directly by human prompters — in the wild. |
Pablo Antonio Moreno Casares; Bao Sheng Loe; John Burden; Sean hEigeartaigh; José Hernández-Orallo; |
| 746 | Adversarial Learning from Crowds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this day, however, there remain under-explored security aspects of LFC systems. In this work, we seek to bridge this gap. |
Pengpeng Chen; Hailong Sun; Yongqiang Yang; Zhijun Chen; |
| 747 | FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce an approximation technique that is effective for finding counterfactual explanations for predictions of the original model and show that our counterfactual examples are significantly closer to the original instances than those produced by other methods specifically designed for tree ensembles. |
Ana Lucic; Harrie Oosterhuis; Hinda Haned; Maarten de Rijke; |
| 748 | Teaching Humans When to Defer to A Classifier Via Exemplars Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we aim to ensure that human decision makers learn a valid mental model of the agent’s strengths and weaknesses. |
Hussein Mozannar; Arvind Satyanarayan; David Sontag; |
| 749 | Deceptive Decision-Making Under Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies via linear programming. |
Yagiz Savas; Christos K. Verginis; Ufuk Topcu; |
| 750 | On Optimizing Interventions in Shared Autonomy Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose two model-free reinforcement learning methods that can account for both hard and soft constraints on the number of interventions. |
Weihao Tan; David Koleczek; Siddhant Pradhan; Nicholas Perello; Vivek Chettiar; Vishal Rohra; Aaslesha Rajaram; Soundararajan Srinivasan; H M Sajjad Hossain; Yash Chandak; |
| 751 | Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. |
Zhenhailong Wang; Heng Ji; |
| 752 | DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a time-travelling visual solution DeepVisualInsight (DVI), aiming to manifest the spatio-temporal causality while training a deep learning image classifier. |
Xianglin Yang; Yun Lin; Ruofan Liu; Zhenfeng He; Chao Wang; Jin Song Dong; Hong Mei; |
| 753 | When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study compound FER in the cross-domain few-shot learning setting, where only a few images of novel classes from the target domain are required as a reference. |
Xinyi Zou; Yan Yan; Jing-Hao Xue; Si Chen; Hanzi Wang; |
| 754 | Discovering State and Action Abstractions for Generalized Task and Motion Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. |
Aidan Curtis; Tom Silver; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Kaelbling; |
| 755 | Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To ensure stability during the learning and control process, we propose a projected policy gradient method that iteratively enforces the stability conditions in the reparametrized space taking advantage of mild additional information on system dynamics. |
Fangda Gu; He Yin; Laurent El Ghaoui; Murat Arcak; Peter Seiler; Ming Jin; |
| 756 | Random Mapping Method for Large-Scale Terrain Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We show that this method can model the environments in much less computation time, memory consumption, and access time, with high accuracy. |
Xu Liu; Decai Li; Yuqing He; |
| 757 | Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose Conservative and Adaptive Penalty (CAP), a model-based safe RL framework that accounts for potential modeling errors by capturing model uncertainty and adaptively exploiting it to balance the reward and the cost objectives. |
Yecheng Jason Ma; Andrew Shen; Osbert Bastani; Jayaraman Dinesh; |
| 758 | CTIN: Robust Contextual Transformer Network for Inertial Navigation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel robust Contextual Transformer-based network for Inertial Navigation (CTIN) to accurately predict velocity and trajectory. |
Bingbing Rao; Ehsan Kazemi; Yifan Ding; Devu M Shila; Frank M Tucker; Liqiang Wang; |
| 759 | Monocular Camera-Based Point-Goal Navigation By Learning Depth Channel and Cross-Modality Pyramid Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For a monocular camera-based navigation system, if we could effectively explore scene geometric cues from RGB images, the geometry information will significantly facilitate the efficiency of the navigation system. Motivated by this, we propose a highly efficient point-goal navigation framework, dubbed Geo-Nav. |
Tianqi Tang; Heming Du; Xin Yu; Yi Yang; |
| 760 | Robust Adversarial Reinforcement Learning with Dissipation Inequation Constraint Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we consider the problem of ensuring system stability during training in the adversarial reinforcement learning architecture. |
Peng Zhai; Jie Luo; Zhiyan Dong; Lihua Zhang; Shunli Wang; Dingkang Yang; |
| 761 | Sim2Real Object-Centric Keypoint Detection and Description Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the object-centric formulation, which, beyond the conventional setting, requires further identifying which object each interest point belongs to. |
Chengliang Zhong; Chao Yang; Fuchun Sun; Jinshan Qi; Xiaodong Mu; Huaping Liu; Wenbing Huang; |
| 762 | Incomplete Argumentation Frameworks: Properties and Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Dung’s Argumentation Framework (AF) has been extended in several directions, including the possibility of representing unquantified uncertainty about the existence of arguments and attacks. |
Gianvincenzo Alfano; Sergio Greco; Francesco Parisi; Irina Trubitsyna; |
| 763 | Trading Complexity for Sparsity in Random Forest Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we examine different types of reasons that explain "why" an input instance is classified as positive or negative by a Boolean random forest. |
Gilles Audemard; Steve Bellart; Louènas Bounia; Frédéric Koriche; Jean-Marie Lagniez; Pierre Marquis; |
| 764 | From Actions to Programs As Abstract Actual Causes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we give a definition of weak potential causes. |
Bita Banihashemi; Shakil M. Khan; Mikhail Soutchanski; |
| 765 | Equivalence in Argumentation Frameworks with A Claim-Centric View – Classical Results with Novel Ingredients Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the present paper, the knowledge representation formalism under consideration are claim-augmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. |
Ringo Baumann; Anna Rapberger; Markus Ulbricht; |
| 766 | Finite Entailment of Local Queries in The Z Family of Description Logics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we study the complexity of finite entailment of local queries (conjunctive queries and positive boolean combinations thereof) in the Z family of DLs, one of the most powerful KR formalisms, lying on the verge of decidability. |
Bartosz Bednarczyk; Emanuel Kieroński; |
| 767 | The Price of Selfishness: Conjunctive Query Entailment for ALCSelf Is 2EXPTIME-Hard Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We contribute to this line of results by showing the surprising fact that even extending ALC by just the Self operator – which proved innocuous in many other contexts – increases the complexity of CQ entailment to 2EXPTIME. As common for this type of problem, our proof establishes a reduction from alternating Turing machines running in exponential space, but several novel ideas and encoding tricks are required to make the approach work in that specific, restricted setting. |
Bartosz Bednarczyk; Sebastian Rudolph; |
| 768 | Expressivity of Planning with Horn Description Logic Ontologies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we propose a novel compilation scheme into standard PDDL with derived predicates, which applies to more expressive DLs and is based on the rewritability of DL queries into Datalog with stratified negation. |
Stefan Borgwardt; Jörg Hoffmann; Alisa Kovtunova; Markus Krötzsch; Bernhard Nebel; Marcel Steinmetz; |
| 769 | ER: Equivariance Regularizer for Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Our motivation is based on the observation that the previous work only focuses on the “size” of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. |
Zongsheng Cao; Qianqian Xu; Zhiyong Yang; Qingming Huang; |
| 770 | Geometry Interaction Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Therefore, embedding KGs in a single space, no matter the Euclidean space, hyperbolic space or hyperspheric space, cannot capture the complex structures of KGs accurately. To overcome this challenge, we propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. |
Zongsheng Cao; Qianqian Xu; Zhiyong Yang; Xiaochun Cao; Qingming Huang; |
| 771 | Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing methods based on graph neural networks learn a deterministic embedding function, which lacks sufficient flexibility to explore better choices when dealing with the imperfect and noisy KGs such as the scarce labeled nodes and noisy graph structure. To this end, we propose a novel multi-relational graph Gaussian Process network (GGPN), which aims to improve the flexibility of deterministic methods by simultaneously learning a family of embedding functions, i.e., a stochastic embedding function. |
Guanzheng Chen; Jinyuan Fang; Zaiqiao Meng; Qiang Zhang; Shangsong Liang; |
| 772 | ASP-Based Declarative Process Mining Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The contributions of the work include an ASP encoding schema for the three problems, their solution, and experiments showing the feasibility of the approach. |
Francesco Chiariello; Fabrizio Maria Maggi; Fabio Patrizi; |
| 773 | On Testing for Discrimination Using Causal Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We expect there to be a regulator whose job it is to certify the bank’s system as fair or unfair. We consider issues that the regulator will have to confront when making such a decision, including the precise definition of fairness, dealing with proxy variables, and dealing with what we call allowed variables, that is, variables such as salary on which the decision is allowed to depend, despite being correlated with sensitive variables. |
Hana Chockler; Joseph Y. Halpern; |
| 774 | Monotone Abstractions in Ontology-Based Data Management Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, a natural question to ask is whether such limitations affect even larger classes of queries. In this paper, we answer this fundamental question for an essential class of queries, namely the class of monotone queries. |
Gianluca Cima; Marco Console; Maurizio Lenzerini; Antonella Poggi; |
| 775 | Lower Bounds on Intermediate Results in Bottom-Up Knowledge Compilation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, in this paper, we look at CNF formulas for which very small circuits exists and look at the efficiency of their bottom-up compilation in one of the most general languages, namely that of structured decomposable negation normal forms (str-DNNF). |
Alexis de Colnet; Stefan Mengel; |
| 776 | Enforcement Heuristics for Argumentation with Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a learning-based approach to the symbolic reasoning problem of dynamic argumentation, where the knowledge about attacks between arguments is incomplete or evolving. |
Dennis Craandijk; Floris Bex; |
| 777 | On The Computation of Necessary and Sufficient Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we refer to the prime implicates of a complete reason as necessary reasons for the decision. |
Adnan Darwiche; Chunxi Ji; |
| 778 | Machine Learning for Utility Prediction in Argument-Based Computational Persuasion Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In order to deal with this situation, games in extended form have been harnessed for argumentation in Bi-party Decision Theory. This opens new problems that we address in this paper: (1) How can we use Machine Learning (ML) methods to predict utility functions for different subpopulations of users? |
Ivan Donadello; Anthony Hunter; Stefano Teso; Mauro Dragoni; |
| 779 | On The Complexity of Inductively Learning Guarded Clauses Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP). |
Andrei Draghici; Georg Gottlob; Matthias Lanzinger; |
| 780 | Tractable Abstract Argumentation Via Backdoor-Treewidth Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the backdoor-treewidth approach for abstract argumentation, combining the best of both worlds with a guaranteed parameter value that does not exceed the minimum of the backdoor- and treewidth-parameter. |
Wolfgang Dvořák; Markus Hecher; Matthias König; André Schidler; Stefan Szeider; Stefan Woltran; |
| 781 | Large-Neighbourhood Search for Optimisation in Answer-Set Solving Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a framework for LNS optimisation in answer-set solving, in which neighbourhoods can be specified either declaratively as part of the ASP encoding, or automatically generated by code. |
Thomas Eiter; Tobias Geibinger; Nelson Higuera Ruiz; Nysret Musliu; Johannes Oetsch; Daria Stepanova; |
| 782 | Answering Queries with Negation Over Existential Rules Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This fails for existential rules, which instead of a single least model have multiple universal models that may not lead to the same results for negative queries. We therefore propose universal core models as a basis for a meaningful (non-monotonic) semantics for queries with negation. |
Stefan Ellmauthaler; Markus Krötzsch; Stephan Mennicke; |
| 783 | Axiomatization of Aggregates in Answer Set Programming Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The paper presents a characterization of logic programs with aggregates based on many-sorted generalization of operator SM that refers neither to grounding nor to fixpoints. |
Jorge Fandinno; Zachary Hansen; Yuliya Lierler; |
| 784 | Linear-Time Verification of Data-Aware Dynamic Systems with Arithmetic Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the expressive yet concise framework of data-aware dynamic systems (DDS), extending it with linear arithmetic, and providing the following contributions. First, we introduce a new, semantic property of “finite summary”, which guarantees the existence of a faithful finite-state abstraction. |
Paolo Felli; Marco Montali; Sarah Winkler; |
| 785 | Rushing and Strolling Among Answer Sets – Navigation Made Easy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a formal and general framework for interactive navigation toward desired subsets of answer sets analogous to faceted browsing. |
Johannes Klaus Fichte; Sarah Alice Gaggl; Dominik Rusovac; |
| 786 | Sufficient Reasons for Classifier Decisions in The Presence of Domain Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a more general theory, also based on prime-implicants, tailored to taking constraints into account. |
Niku Gorji; Sasha Rubin; |
| 787 | Reasoning About Causal Models with Infinitely Many Variables Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide a sound and complete axiomatization of causal reasoning in GSEMs that is an extension of the sound and complete axiomatization provided by Halpern (2000) for SEMs. |
Joseph Y. Halpern; Spencer Peters; |
| 788 | An Axiomatic Approach to Revising Preferences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Requiring the input and output to be aligned yields a second type of operator, which we characterize using preferences on the comparisons in the prior preference Prefence revision is set in a logic-based framework and using the formal machinery of belief change, along the lines of the well-known AGM approach: we propose rationality postulates for each of the two versions of our model and derive representation results, thus situating preference revision within the larger family of belief change operators. |
Adrian Haret; Johannes Peter Wallner; |
| 789 | BERTMap: A BERT-Based Ontology Alignment System Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. |
Yuan He; Jiaoyan Chen; Denvar Antonyrajah; Ian Horrocks; |
| 790 | Conditional Abstract Dialectical Frameworks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Abstract dialectical frameworks (in short, ADFs) are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. |
Jesse Heyninck; Matthias Thimm; Gabriele Kern-Isberner; Tjitze Rienstra; Kenneth Skiba; |
| 791 | MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel way to incorporate expert knowledge into the training of deep neural networks. |
Nick Hoernle; Rafael Michael Karampatsis; Vaishak Belle; Kobi Gal; |
| 792 | Towards Explainable Action Recognition By Salient Qualitative Spatial Object Relation Chains Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a novel neuro-symbolic approach that recognizes actions from videos with human-understandable explanations. |
Hua Hua; Dongxu Li; Ruiqi Li; Peng Zhang; Jochen Renz; Anthony Cohn; |
| 793 | Tractable Explanations for D-DNNF Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper shows that for classifiers represented with some of the best-known propositional languages, different kinds of explanations can be computed in polynomial time. |
Xuanxiang Huang; Yacine Izza; Alexey Ignatiev; Martin Cooper; Nicholas Asher; Joao Marques-Silva; |
| 794 | Understanding Enthymemes in Deductive Argumentation Using Semantic Distance Measures Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To model this process, we propose the use of semantic distance measures (e.g. based on a vector representation of word embeddings or a semantic network representation of words) to determine whether one symbol can be substituted by another. We present a theoretical framework for using substitutions, together with abduction of default knowledge, for understanding enthymemes based on deductive argumentation, and investigate how this could be used in practice. |
Anthony Hunter; |
| 795 | Inferring Lexicographically-Ordered Rewards from Preferences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a method for inferring multi-objective reward-based representations of an agent’s observed preferences. |
Alihan Hüyük; William R. Zame; Mihaela van der Schaar; |
| 796 | Towards Fine-Grained Reasoning for Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking and enabling the modeling of subtle clues. |
Yiqiao Jin; Xiting Wang; Ruichao Yang; Yizhou Sun; Wei Wang; Hao Liao; Xing Xie; |
| 797 | ApproxASP – A Scalable Approximate Answer Set Counter Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a scalable approach to approximate counting for answer set programming. |
Mohimenul Kabir; Flavio O Everardo; Ankit K Shukla; Markus Hecher; Johannes Klaus Fichte; Kuldeep S Meel; |
| 798 | Unit Selection with Causal Diagram Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold. |
Ang Li; Judea Pearl; |
| 799 | Bounds on Causal Effects and Application to High Dimensional Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For such scenarios, we derive bounds on the causal effects by solving two non-linear optimization problems, and demonstrate that the bounds are sufficient. Using this optimization method, we propose a framework for dimensionality reduction that allows one to trade bias for estimation power, and demonstrate its performance using simulation studies. |
Ang Li; Judea Pearl; |
| 800 | How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. |
Ren Li; Yanan Cao; Qiannan Zhu; Guanqun Bi; Fang Fang; Yi Liu; Qian Li; |
| 801 | Multi-View Graph Representation for Programming Language Processing: An Investigation Into Algorithm Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a multi-view graph (MVG) program representation method. |
Ting Long; Yutong Xie; Xianyu Chen; Weinan Zhang; Qinxiang Cao; Yong Yu; |
| 802 | Automated Synthesis of Generalized Invariant Strategies Via Counterexample-Guided Strategy Refinement Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we formalize the problem of generalized strategy synthesis in the situation calculus. |
Kailun Luo; Yongmei Liu; |
| 803 | Using Conditional Independence for Belief Revision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an approach to incorporating qualitative assertions of conditional irrelevance into belief revision, in order to address the limitations of existing work which considers only unconditional irrelevance. |
Matthew James Lynn; James P. Delgrande; Pavlos Peppas; |
| 804 | Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formula Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the concept of lifted interpretations as a tool for formulating closed forms for WFOMC. |
Sagar Malhotra; Luciano Serafini; |
| 805 | TempoQR: Temporal Question Reasoning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. |
Costas Mavromatis; Prasanna Lakkur Subramanyam; Vassilis N. Ioannidis; Adesoji Adeshina; Phillip R Howard; Tetiana Grinberg; Nagib Hakim; George Karypis; |
| 806 | Compilation of Aggregates in ASP Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a compilation-based approach for ASP programs with aggregates. |
Giuseppe Mazzotta; Francesco Ricca; Carmine Dodaro; |
| 807 | Prevailing in The Dark: Information Walls in Strategic Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: The paper studies strategic abilities that rise from restrictions on the information sharing in multi-agent systems. The main technical result is a sound and complete logical … |
Pavel Naumov; Wenxuan Zhang; |
| 808 | Knowledge Compilation Meets Logical Separability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we apply the notion of logical separability in three reasoning problems within the context of propositional logic: satisfiability check (CO), clausal entailment check (CE) and model counting (CT), contributing to three corresponding polytime procedures. |
Junming Qiu; Wenqing Li; Zhanhao Xiao; Quanlong Guan; Liangda Fang; Zhao-Rong Lai; Qian Dong; |
| 809 | Propositional Encodings of Acyclicity and Reachability By Using Vertex Elimination Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce novel methods for encoding acyclicity and s-t-reachability constraints for propositional formulas with underlying directed graphs, based on vertex elimination graphs, which makes them suitable for cases where the underlying graph has a low directed elimination width. |
Masood Feyzbakhsh Rankooh; Jussi Rintanen; |
| 810 | Random Vs. Best-First: Impact of Sampling Strategies on Decision Making in Model-Based Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: One example is the computation of a few most probable fault explanations for a defective system and the use of these to assess which aspect of the system, if measured, would bring the highest information gain. In this work, we scrutinize whether these statistically not well-founded conventions, that both diagnosis researchers and practitioners have adhered to for decades, are indeed reasonable. |
Patrick Rodler; |
| 811 | On Paraconsistent Belief Revision in LP Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, in this context, the standard belief revision postulates trivialize the revision process. In this work we discuss how to adapt these postulates when the underlying logic is Priest’s LP logic, in order to model a rational change, while being a conservative extension of AGM/KM belief revision. |
Nicolas Schwind; Sébastien Konieczny; Ramón Pino Pérez; |
| 812 | Weakly Supervised Neural Symbolic Learning for Cognitive Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose WS-NeSyL, a weakly supervised neural symbolic learning model for cognitive tasks with logical reasoning. |
Jidong Tian; Yitian Li; Wenqing Chen; Liqiang Xiao; Hao He; Yaohui Jin; |
| 813 | First Order Rewritability in Ontology-Mediated Querying in Horn Description Logics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In general, OMQ approaches for such logics rely on non-FO rewriting of the query and/or on non-FO completion of the data, called a ABox. Specifically, we consider the problem of FO rewritability in terms of Beth definability, and show how Craig interpolation can then be used to effectively construct the rewritings, when they exist, from the Clark’s completion of Datalog-like programs encoding a given DL TBox and optionally a query. |
David Toman; Grant Weddell; |
| 814 | MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. |
Dingmin Wang; Pan Hu; Przemysław Andrzej Wałęga; Bernardo Cuenca Grau; |
| 815 | SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a Scene Graph Enhanced Image-Text Learning (SGEITL) framework to incorporate visual scene graph in commonsense reasoning. |
Zhecan Wang; Haoxuan You; Liunian Harold Li; Alireza Zareian; Suji Park; Yiqing Liang; Kai-Wei Chang; Shih-Fu Chang; |
| 816 | Inductive Relation Prediction By BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose an all-in-one solution, called BERTRL (BERT-based Relational Learning), which leverages pre-trained language model and fine-tunes it by taking relation instances and their possible reasoning paths as training samples. |
Hanwen Zha; Zhiyu Chen; Xifeng Yan; |
| 817 | Learning to Walk with Dual Agents for Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This is undesirable for many reasoning tasks in real-world scenarios, where short paths connecting the source and target entities are not available in incomplete KGs, and thus the reasoning performances drop drastically unless the agent is able to seek out more clues from longer paths. To address the above challenge, in this paper, we propose a dual-agent reinforcement learning framework, which trains two agents (Giant and Dwarf) to walk over a KG jointly and search for the answer collaboratively. |
Denghui Zhang; Zixuan Yuan; Hao Liu; Xiaodong lin; Hui Xiong; |
| 818 | Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model. |
Hao Zhang; Shuigeng Zhou; Kun Zhang; Jihong Guan; |
| 819 | Characterizing The Program Expressive Power of Existential Rule Languages Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we establish a number of novel characterizations for the program expressive power of several important existential rule languages, including tuple-generating dependencies (TGDs), linear TGDs, as well as disjunctive TGDs. |
Heng Zhang; Guifei Jiang; |
| 820 | Context-Specific Representation Abstraction for Deep Option Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This issue can result in practical limitations of this method, including sample inefficient learning. To address this problem, we introduce Context-Specific Representation Abstraction for Deep Option Learning (CRADOL), a new framework that considers both temporal abstraction and context-specific representation abstraction to effectively reduce the size of the search over policy space. |
Marwa Abdulhai; Dong-Ki Kim; Matthew Riemer; Miao Liu; Gerald Tesauro; Jonathan P. How; |
| 821 | FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we demonstrate that learning the shape of convolution kernels in non-Euclidean spaces is better than existing deformable kernel methods. |
Ola Ahmad; Freddy Lecue; |
| 822 | Distributed Learning with Strategic Users: A Repeated Game Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a distributed learning setting where strategic users are incentivized by a fusion center, to train a learning model based on local data. |
Abdullah B Akbay; Junshan Zhang; |
| 823 | Private Rank Aggregation in Central and Local Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present differentially private algorithms for rank aggregation in the pure and approximate settings along with distribution-independent utility upper and lower bounds. |
Daniel Alabi; Badih Ghazi; Ravi Kumar; Pasin Manurangsi; |
| 824 | Combating Adversaries with Anti-adversaries Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this effect. |
Motasem Alfarra; Juan C. Perez; Ali Thabet; Adel Bibi; Philip H.S. Torr; Bernard Ghanem; |
| 825 | DeformRS: Certifying Input Deformations with Randomized Smoothing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We reformulate certification in randomized smoothing setting for both general vector field and parameterized deformations and propose DeformRS-VF and DeformRS-Par, respectively. |
Motasem Alfarra; Adel Bibi; Naeemullah Khan; Philip H.S. Torr; Bernard Ghanem; |
| 826 | Latent Time Neural Ordinary Differential Equations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel and unique approach to model uncertainty in NODE by considering a distribution over the end-time T of the ODE solver. |
Srinivas Anumasa; P. K. Srijith; |
| 827 | Beyond GNNs: An Efficient Architecture for Graph Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In these instances, it is known that one needs GNNs of high depth, scaling at a polynomial rate with the number of nodes n, to provably encode the solution space, in turn affecting their statistical efficiency. In this work we propose a new hybrid architecture to overcome this limitation. |
Pranjal Awasthi; Abhimanyu Das; Sreenivas Gollapudi; |
| 828 | Programmatic Modeling and Generation of Real-Time Strategic Soccer Environments for Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To showcase the benefits, we interfaced SCENIC to an existing RTS environment Google Research Football (GRF) simulator and introduced a benchmark consisting of 32 realistic scenarios, encoded in SCENIC, to train RL agents and testing their generalization capabilities. |
Abdus Salam Azad; Edward Kim; Qiancheng Wu; Kimin Lee; Ion Stoica; Pieter Abbeel; Alberto Sangiovanni-Vincentelli; Sanjit A. Seshia; |
| 829 | Admissible Policy Teaching Through Reward Design Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We then proceed by formulating a surrogate problem whose optimal solution approximates the optimal solution to the reward design problem in our setting, but is more amenable to optimization techniques and analysis. For this surrogate problem, we present characterization results that provide bounds on the value of the optimal solution. |
Kiarash Banihashem; Adish Singla; Jiarui Gan; Goran Radanovic; |
| 830 | Entropy-Based Logic Explanations of Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from neural networks using the formalism of First-Order Logic. |
Pietro Barbiero; Gabriele Ciravegna; Francesco Giannini; Pietro Lió; Marco Gori; Stefano Melacci; |
| 831 | Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-series domain due to its unique characteristics compared to images and text data. In this paper, we fill this gap by proposing a novel algorithmic framework referred as RObust Training for Time-Series (RO-TS) to create robust deep models for time-series classification tasks. |
Taha Belkhouja; Yan Yan; Janardhan Rao Doppa; |
| 832 | A Fast Algorithm for PAC Combinatorial Pure Exploration Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a new CPE algorithm in the PAC setting, which is computationally light weight, and so can easily be applied to problems with tens of thousands of arms. |
Noa Ben-David; Sivan Sabato; |
| 833 | Modeling Attrition in Recommender Systems with Departing Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, in real-world systems, dissatisfied users may depart (and never come back). In this work, we propose a novel multi-armed bandit setup that captures such policy-dependent horizons. |
Omer Ben-Porat; Lee Cohen; Liu Leqi; Zachary C. Lipton; Yishay Mansour; |
| 834 | Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we develop, implement, and experimentally validate a novel FL framework termed Federated Dynamic Sparse Training (FedDST) by which complex neural networks can be deployed and trained with substantially improved efficiency in both on-device computation and in-network communication. |
Sameer Bibikar; Haris Vikalo; Zhangyang Wang; Xiaohan Chen; |
| 835 | Robust and Resource-Efficient Data-Free Knowledge Distillation By Generative Pseudo Replay Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose to model the distribution of the previously observed synthetic samples with a generative network. |
Kuluhan Binici; Shivam Aggarwal; Nam Trung Pham; Karianto Leman; Tulika Mitra; |
| 836 | ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. |
Koushik Biswas; Sandeep Kumar; Shilpak Banerjee; Ashish Kumar Pandey; |
| 837 | Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method, named Feedback Gradient Descent (FGD), to our knowledge, the first work showing high efficiency and stability simultaneously. |
Fanchen Bu; Dong Eui Chang; |
| 838 | Breaking The Convergence Barrier: Optimization Via Fixed-Time Convergent Flows Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time stability of dynamical systems. |
Param Budhraja; Mayank Baranwal; Kunal Garg; Ashish Hota; |
| 839 | Shrub Ensembles for Online Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel memory-efficient online classification ensemble called shrub ensembles for resource-constraint systems. |
Sebastian Buschjäger; Sibylle Hess; Katharina J. Morik; |
| 840 | NoiseGrad — Enhancing Explanations By Introducing Stochasticity to Model Weights Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For local explanation, stochasticity is known to help: a simple method, called SmoothGrad, has improved the visual quality of gradient-based attribution by adding noise to the input space and averaging the explanations of the noisy inputs. In this paper, we extend this idea and propose NoiseGrad that enhances both local and global explanation methods. |
Kirill Bykov; Anna Hedström; Shinichi Nakajima; Marina M.-C. Höhne; |
| 841 | Leaping Through Time with Gradient-Based Adaptation for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies. |
Nuttapong Chairatanakul; Hoang NT; Xin Liu; Tsuyoshi Murata; |
| 842 | Active Sampling for Text Classification with Subinstance Level Queries Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In such applications, it is not necessary for the human oracles to review an unlabeled sample end-to-end in order to provide a label; it may be more efficient to identify an optimal subinstance size (percentage of the sample from the start) for each unlabeled sample, and request the human annotator to label the sample by analyzing only the subinstance, instead of the whole data sample. In this paper, we propose a novel framework to address this challenging problem, in an effort to further reduce the labeling burden on the human oracles and utilize the available labeling budget more efficiently. |
Shayok Chakraborty; Ankita Singh; |
| 843 | A Unifying Theory of Thompson Sampling for Continuous Risk-Averse Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper unifies the design and the analysis of risk-averse Thompson sampling algorithms for the multi-armed bandit problem for a class of risk functionals ρ that are continuous and dominant. We prove generalised concentration bounds for these continuous and dominant risk functionals and show that a wide class of popular risk functionals belong to this class. |
Joel Q. L. Chang; Vincent Y. F. Tan; |
| 844 | Locally Private K-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce a new locally private k-means clustering algorithm that achieves near-optimal additive error whilst retaining constant multiplicative approximation factors and round complexity. |
Anamay Chaturvedi; Matthew Jones; Huy Lê Nguyễn; |
| 845 | Safe Online Convex Optimization with Unknown Linear Safety Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The algorithm has access to only the noisy observations of constraints for the chosen actions. We propose an algorithm, called the Safe Online Projected Gradient Descent (SO-PGD) algorithm, to address this problem. |
Sapana Chaudhary; Dileep Kalathil; |
| 846 | Deconvolutional Density Network: Modeling Free-Form Conditional Distributions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we demonstrate the benefits of modeling free-form conditional distributions using a deconvolution-based neural net framework, coping with data deficiency problems in discretization. |
Bing Chen; Mazharul Islam; Jisuo Gao; Lin Wang; |
| 847 | Multiscale Generative Models: Improving Performance of A Generative Model Using Feedback from Other Dependent Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a technique of using feedback from the higher-level GAN to improve performance of lower-level GANs. |
Changyu Chen; Avinandan Bose; Shih-Fen Cheng; Arunesh Sinha; |
| 848 | Simultaneously Learning Stochastic and Adversarial Bandits Under The Position-Based Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a method based on the follow-the-regularized-leader (FTRL) framework with Tsallis entropy and develop a new self-bounding constraint especially designed for PBM. |
Cheng Chen; Canzhe Zhao; Shuai Li; |
| 849 | Clustering Interval-Censored Time-Series for Disease Phenotyping Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping. |
Irene Y. Chen; Rahul G. Krishnan; David Sontag; |
| 850 | Efficient Robust Training Via Backward Smoothing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem. |
Jinghui Chen; Yu Cheng; Zhe Gan; Quanquan Gu; Jingjing Liu; |
| 851 | An Online Learning Approach to Sequential User-Centric Selection Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a new variant of multi-play MAB model, to capture important factors of the sequential user-centric selection problem arising from mobile edge computing, ridesharing applications, etc. |
Junpu Chen; Hong Xie; |
| 852 | Better Parameter-Free Stochastic Optimization with ODE Updates for Coin-Betting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we close the empirical gap with a new parameter-free algorithm based on continuous-time Coin-Betting on truncated models. |
Keyi Chen; John Langford; Francesco Orabona; |
| 853 | Mutual Nearest Neighbor Contrast and Hybrid Prototype Self-Training for Universal Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, over-reliance on learned classifier knowledge is inevitably biased to source data, ignoring the intrinsic structure of target domain. Therefore, in this paper, we propose a novel two-stage UniDA framework called MATHS based on the principle of mutual nearest neighbor contrast and hybrid prototype discrimination. |
Liang Chen; Qianjin Du; Yihang Lou; Jianzhong He; Tao Bai; Minghua Deng; |
| 854 | Evidential Neighborhood Contrastive Learning for Universal Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Previous methods barely exploit the intrinsic manifold structure relationship between two domains for feature alignment, and they rely on the softmax-based scores with class competition nature to detect underlying “unknown” samples. Therefore, in this paper, we propose a novel evidential neighborhood contrastive learning framework called TNT to address these issues. |
Liang Chen; Yihang Lou; Jianzhong He; Tao Bai; Minghua Deng; |
| 855 | Zero Stability Well Predicts Performance of Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we move toward the answer with one more step by connecting zero stability and model performance. |
Liangming Chen; Long Jin; Mingsheng Shang; |
| 856 | Semi-supervised Learning with Multi-Head Co-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Without natural split of features, single-view co-training works at the cost of training extra classifiers, where the algorithm should be delicately designed to prevent individual classifiers from collapsing into each other. To remove these obstacles which deter the adoption of single-view co-training, we present a simple and efficient algorithm Multi-Head Co-Training. |
Mingcai Chen; Yuntao Du; Yi Zhang; Shuwei Qian; Chongjun Wang; |
| 857 | Instance Selection: A Bayesian Decision Theory Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider the problem of lacking theoretical foundation and low execution efficiency of the instance selection methods based on the k-nearest neighbour rule when processing large-scale data. |
Qingqiang Chen; Fuyuan Cao; Ying Xing; Jiye Liang; |
| 858 | Input-Specific Robustness Certification for Randomized Smoothing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Input-Specific Sampling (ISS) acceleration to achieve the cost-effectiveness for robustness certification, in an adaptive way of reducing the sampling size based on the input characteristic. |
Ruoxin Chen; Jie Li; Junchi Yan; Ping Li; Bin Sheng; |
| 859 | Multimodal Adversarially Learned Inference with Factorized Discriminators Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel approach to generative modeling of multimodal data based on generative adversarial networks. |
Wenxue Chen; Jianke Zhu; |
| 860 | Imbalance-Aware Uplift Modeling for Observational Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Such highly imbalanced data is common in various fields such as marketing and medical treatment but it is rarely handled by existing works. In this paper, we theoretically and quantitatively prove that the existing representative methods, transformed outcome (TOM) and doubly robust (DR), suffer from large bias and deviation on highly imbalanced datasets with skewed propensity scores, mainly because they are proportional to the reciprocal of the propensity score. |
Xuanying Chen; Zhining Liu; Li Yu; Liuyi Yao; Wenpeng Zhang; Yi Dong; Lihong Gu; Xiaodong Zeng; Yize Tan; Jinjie Gu; |
| 861 | KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, by combining the statistical learning theory and KAM theory, we provide a theoretical analysis of the behavior of Hamiltonian neural networks when the learning error is not completely zero. |
Yuhan Chen; Takashi Matsubara; Takaharu Yaguchi; |
| 862 | BScNets: Block Simplicial Complex Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: By building upon connection between the convolution operation and the new block Hodge-Laplacian, we propose the first SNN for link prediction. |
Yuzhou Chen; Yulia R. Gel; H. Vincent Poor; |
| 863 | ASM2TV: An Adaptive Semi-supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: On the other hand, supervised methods might be insufficient when labeled data is scarce. To tackle these challenges, we introduce a novel framework ASM2TV for semi-supervised multi-task multi-view learning. |
Zekai Chen; Xiao Zhang; Xiuzhen Cheng; |
| 864 | Identification of Linear Latent Variable Model with Arbitrary Distribution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recently, some methods have been proposed to estimate the structural model by assuming that the noise terms in the measured and latent variables are non-Gaussian. |
Zhengming Chen; Feng Xie; Jie Qiao; Zhifeng Hao; Kun Zhang; Ruichu Cai; |
| 865 | DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies. |
Anda Cheng; Jiaxing Wang; Xi Sheryl Zhang; Qiang Chen; Peisong Wang; Jian Cheng; |
| 866 | Graph Neural Controlled Differential Equations for Traffic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present the method of spatio-temporal graph neural controlled differential equation (STG-NCDE). |
Jeongwhan Choi; Hwangyong Choi; Jeehyun Hwang; Noseong Park; |
| 867 | Differentially Private Regret Minimization in Episodic Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We study regret minimization in finite horizon tabular Markov decision processes (MDPs) under the constraints of differential privacy (DP). |
Sayak Ray Chowdhury; Xingyu Zhou; |
| 868 | Learning By Competition of Self-Interested Reinforcement Learning Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To facilitate structural credit assignment, we propose replacing the reward signal to hidden units with the change in the L2 norm of the unit’s outgoing weight. |
Stephen Chung; |
| 869 | How to Distribute Data Across Tasks for Meta-Learning? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks. |
Alexandru Cioba; Michael Bromberg; Qian Wang; Ritwik Niyogi; Georgios Batzolis; Jezabel Garcia; Da-shan Shiu; Alberto Bernacchia; |
| 870 | Similarity Search for Efficient Active Learning and Search of Rare Concepts Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we improve the computational efficiency of active learning and search methods by restricting the candidate pool for labeling to the nearest neighbors of the currently labeled set instead of scanning over all of the unlabeled data. |
Cody Coleman; Edward Chou; Julian Katz-Samuels; Sean Culatana; Peter Bailis; Alexander C. Berg; Robert Nowak; Roshan Sumbaly; Matei Zaharia; I. Zeki Yalniz; |
| 871 | Learning Influence Adoption in Heterogeneous Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of learning influence adoption in networks. |
Vincent Conitzer; Debmalya Panigrahi; Hanrui Zhang; |
| 872 | Graph-Wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Hence, this work proposes a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX). |
Thilini Cooray; Ngai-Man Cheung; |
| 873 | Reinforcement Learning with Stochastic Reward Machines Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them. |
Jan Corazza; Ivan Gavran; Daniel Neider; |
| 874 | Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. |
Francesco Croce; Maksym Andriushchenko; Naman D. Singh; Nicolas Flammarion; Matthias Hein; |
| 875 | Learning Logic Programs Though Divide, Constrain, and Conquer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. |
Andrew Cropper; |
| 876 | Implicit Gradient Alignment in Distributed and Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we show that data heterogeneity can in fact be exploited to improve generalization performance through implicit regularization. |
Yatin Dandi; Luis Barba; Martin Jaggi; |
| 877 | How Good Are Low-Rank Approximations in Gaussian Process Regression? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on truncating the kernel’s Mercer expansion. |
Constantinos Daskalakis; Petros Dellaportas; Aristeidis Panos; |
| 878 | KOALA: A Kalman Optimization Algorithm with Loss Adaptivity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose to consider the loss as a noisy observation with respect to some reference optimum. |
Aram Davtyan; Sepehr Sameni; Llukman Cerkezi; Givi Meishvili; Adam Bielski; Paolo Favaro; |
| 879 | First-Order Convex Fitting and Its Application to Economics and Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite a basic question of convex analysis, FCF has surprisingly been overlooked in the past literature. With an efficient constructive proof, we provide a clean answer to this question: FCF is possible if and only if the two sequences are permutation stable: p1 * x1 + … + pT * xT is greater than or equal to p1 * x’1 + … + pT * x’T where x’1, …, x’T is any permutation of x1, …, xT. |
Quinlan Dawkins; Minbiao Han; Haifeng Xu; |
| 880 | Gradient Temporal Difference with Momentum: Stability and Convergence Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Gradient temporal difference (Gradient TD) algorithms are a popular class of stochastic approximation (SA) algorithms used for policy evaluation in reinforcement learning. Here, we consider Gradient TD algorithms with an additional heavy ball momentum term and provide choice of step size and momentum parameter that ensures almost sure convergence of these algorithms asymptotically. |
Rohan Deb; Shalabh Bhatnagar; |
| 881 | Distillation of RL Policies with Formal Guarantees Via Variational Abstraction of Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider the challenge of policy simplification and verification in the context of policies learned through reinforcement learning (RL) in continuous environments. |
Florent Delgrange; Ann Nowé; Guillermo A. Pérez; |
| 882 | Reducing Flipping Errors in Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: . In this paper, we empirically study this question and find on several benchmark datasets that the vast majority of the misclassified samples in the last epoch were ever classified correctly before the last epoch, which means that the predictions for these samples were flipped from “correct" to “wrong". |
Xiang Deng; Yun Xiao; Bo Long; Zhongfei Zhang; |
| 883 | Bayesian Optimization Over Permutation Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose and evaluate two algorithms for BO over Permutation Spaces (BOPS). |
Aryan Deshwal; Syrine Belakaria; Janardhan Rao Doppa; Dae Hyun Kim; |
| 884 | Meta Propagation Networks for Graph Few-shot Semi-supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Under the few-shot semi-supervised setting, the performance of most of the existing GNNs is inevitably undermined by the overfitting and oversmoothing issues, largely owing to the shortage of labeled data. In this paper, we propose a decoupled network architecture equipped with a novel meta-learning algorithm to solve this problem. |
Kaize Ding; Jianling Wang; James Caverlee; Huan Liu; |
| 885 | Online Certification of Preference-Based Fairness for Personalized Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a sample-efficient algorithm with theoretical guarantees that it does not deteriorate user experience. |
Virginie Do; Sam Corbett-Davies; Jamal Atif; Nicolas Usunier; |
| 886 | Disentangled Spatiotemporal Graph Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim at pushing forward the modeling and understanding of spatiotemporal graphs via new disentangled deep generative models. |
Yuanqi Du; Xiaojie Guo; Hengning Cao; Yanfang Ye; Liang Zhao; |
| 887 | Learning from The Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. |
Wei Duan; Junyu Xuan; Maoying Qiao; Jie Lu; |
| 888 | Adaptive and Universal Algorithms for Variational Inequalities with Optimal Convergence Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Additionally, prior works require that the optimization domain is bounded. In this work, we remove this restriction and give algorithms for unbounded domains that are adaptive and universal. |
Alina Ene; Huy Lê Nguyễn; |
| 889 | Zero-Shot Out-of-Distribution Detection Based on The Pre-trained Model CLIP Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a novel and yet simple method (called ZOC) to solve the problem. |
Sepideh Esmaeilpour; Bing Liu; Eric Robertson; Lei Shu; |
| 890 | Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we attempt to answer: (1) why training unstructured sparse networks from random initialization performs poorly and; (2) what makes LTs and DST the exceptions? |
Utku Evci; Yani Ioannou; Cem Keskin; Yann Dauphin; |
| 891 | Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a dynamic nonlinear matrix completion (D-NLMC) method, which is able to recover the missing values of streaming data when the low-dimensional nonlinear latent structure of the data changes with time. |
Jicong Fan; |
| 892 | Up to 100x Faster Data-Free Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce an efficacious scheme, termed as FastDFKD, that allows us to accelerate DFKD by a factor of orders of magnitude. |
Gongfan Fang; Kanya Mo; Xinchao Wang; Jie Song; Shitao Bei; Haofei Zhang; Mingli Song; |
| 893 | Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an innovative autoencoder network by learning Aligned Cross-Modal Representations (dubbed ACMR) for GZSC. |
Zhiyu Fang; Xiaobin Zhu; Chun Yang; Zheng Han; Jingyan Qin; Xu-Cheng Yin; |
| 894 | KerGNNs: Interpretable Graph Neural Networks with Graph Kernels Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address the limitations of existing graph kernel and GNN methods, in this paper, we propose a novel GNN framework, termed Kernel Graph Neural Networks (KerGNNs), which integrates graph kernels into the message passing process of GNNs. |
Aosong Feng; Chenyu You; Shiqiang Wang; Leandros Tassiulas; |
| 895 | Scaling Neural Program Synthesis with Distribution-Based Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Within this framework, we introduce two new search algorithms: Heap Search, an enumerative method, and SQRT Sampling, a probabilistic method. |
Nathanaël Fijalkow; Guillaume Lagarde; Théo Matricon; Kevin Ellis; Pierre Ohlmann; Akarsh Nayan Potta; |
| 896 | Modification-Fair Cluster Editing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: When applied to vertex-colored graphs (the colors representing subgroups), standard algorithms for the NP-hard Cluster Editing problem may yield solutions that are biased towards subgroups of data (e.g., demographic groups), measured in the number of modifications incident to the members of the subgroups. We propose a modification fairness constraint which ensures that the number of edits incident to each subgroup is proportional to its size. |
Vincent Froese; Leon Kellerhals; Rolf Niedermeier; |
| 897 | Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Sub-optimal weights may prevent the final model from reaching its full potential. To deal with this challenge, we propose a reinforcement learning (RL) based model combination (RLMC) framework for determining model weights in an ensemble for time series forecasting tasks. |
Yuwei Fu; Di Wu; Benoit Boulet; |
| 898 | JFB: Jacobian-Free Backpropagation for Implicit Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose Jacobian-Free Backpropagation (JFB), a fixed-memory approach that circumvents the need to solve Jacobian-based equations. |
Samy Wu Fung; Howard Heaton; Qiuwei Li; Daniel Mckenzie; Stanley Osher; Wotao Yin; |
| 899 | Smoothing Advantage Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unfortunately, the method tends to be unstable in the case of function approximation. In this paper, we propose a simple variant of AL, named smoothing advantage learning (SAL), to alleviate this problem. |
Yaozhong Gan; Zhe Zhang; Xiaoyang Tan; |
| 900 | Enhancing Counterfactual Classification Performance Via Self-Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A key challenge is that observational data are influenced by historical policies deployed in the system, yielding a biased data distribution. We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes with categorical values for finite unseen actions in the observational data to simulate a randomized trial through pseudolabelling, which we refer to as Counterfactual Self-Training (CST). |
Ruijiang Gao; Max Biggs; Wei Sun; Ligong Han; |
| 901 | Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). |
Ruiqi Gao; Jianwen Xie; Siyuan Huang; Yufan Ren; Song-Chun Zhu; Ying Nian Wu; |
| 902 | Algorithmic Concept-Based Explainable Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unfortunately, a key hindrance of these approaches is their lack of explainability, since GNNs are black-box models that cannot be interpreted directly. In this work, we address this limitation by applying existing work on concept-based explanations to GNN models. |
Dobrik Georgiev; Pietro Barbiero; Dmitry Kazhdan; Petar Veličković; Pietro Lió; |
| 903 | Recovering The Propensity Score from Biased Positive Unlabeled Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose two sets of assumptions under which the propensity score can be uniquely determined: one in which no assumption is made on the functional form of the propensity score (requiring assumptions on the data distribution), and the second which loosens the data assumptions while assuming a functional form for the propensity score. |
Walter Gerych; Thomas Hartvigsen; Luke Buquicchio; Emmanuel Agu; Elke Rundensteiner; |
| 904 | DiPS: Differentiable Policy for Sketching in Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. |
Aritra Ghosh; Saayan Mitra; Andrew Lan; |
| 905 | Learning Large DAGs By Combining Continuous Optimization and Feedback Arc Set Heuristics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose two scalable heuristics for learning DAGs in the linear structural equation case. |
Pierre Gillot; Pekka Parviainen; |
| 906 | Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Understanding modal regression’s theoretical behavior can be fundamental in learning theory. |
Tieliang Gong; Yuxin Dong; Hong Chen; Wei Feng; Bo Dong; Chen Li; |
| 907 | Partial Multi-Label Learning Via Large Margin Nearest Neighbour Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspried by LMNN and embedding technology, we propose a novel PML paradigm called Partial Multi-label Learning via Large Margin Nearest Neighbour Embeddings (PML-LMNNE), which aims to conduct disambiguation by projecting labels and features into a lower-dimension embedding space and reorganize the underlying structure by LMNN in the embedding space simultaneously. |
Xiuwen Gong; Dong Yuan; Wei Bao; |
| 908 | LUNAR: Unifying Local Outlier Detection Methods Via Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, they cannot learn to adapt for a particular set of data due to their lack of trainable parameters. In this paper, we begin by unifying local outlier methods by showing that they are particular cases of the more general message passing framework used in graph neural networks. |
Adam Goodge; Bryan Hooi; See-Kiong Ng; Wee Siong Ng; |
| 909 | Semi-supervised Conditional Density Estimation with Wasserstein Laplacian Regularisation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This task is inherently more complex than regression, and many algorithms suffer from overfitting, particularly when modelled with few labelled data points. For applications where unlabelled data is abundant but labelled data is scarce, we propose Wasserstein Laplacian Regularisation, a semi-supervised learning framework that allows CDE algorithms to leverage these unlabelled data. |
Olivier Graffeuille; Yun Sing Koh; Jörg Wicker; Moritz K Lehmann; |
| 910 | GoTube: Scalable Statistical Verification of Continuous-Depth Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a new statistical verification algorithm that formally quantifies the behavioral robustness of any time-continuous process formulated as a continuous-depth model. |
Sophie A. Gruenbacher; Mathias Lechner; Ramin Hasani; Daniela Rus; Thomas A. Henzinger; Scott A. Smolka; Radu Grosu; |
| 911 | Balanced Self-Paced Learning for AUC Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). |
Bin Gu; Chenkang Zhang; Huan Xiong; Heng Huang; |
| 912 | Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Theoretically, however, there is no existing work on convergence analysis for algorithms with this fictitious discount recipe. This paper takes the first step towards analyzing these algorithms. |
Xin Guo; Anran Hu; Junzi Zhang; |
| 913 | Adaptive Orthogonal Projection for Batch and Online Continual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Catastrophic forgetting is a key obstacle to continual learning. One of the state-of-the-art approaches is orthogonal projection. The idea of this approach is to learn each task … |
Yiduo Guo; Wenpeng Hu; Dongyan Zhao; Bing Liu; |
| 914 | Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work introduces a novel objective function to learn an action translator among training tasks. |
Yijie Guo; Qiucheng Wu; Honglak Lee; |
| 915 | Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a self-supervised pre-training model for learning structure embeddings from protein tertiary structures. |
Yuzhi Guo; Jiaxiang Wu; Hehuan Ma; Junzhou Huang; |
| 916 | Improved Gradient-Based Adversarial Attacks for Quantized Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Even though they exhibit excellent generalization capabilities, their robustness properties are not well-understood. In this work, we systematically study the robustness of quantized networks against gradient based adversarial attacks and demonstrate that these quantized models suffer from gradient vanishing issues and show a fake sense of robustness. |
Kartik Gupta; Thalaiyasingam Ajanthan; |
| 917 | TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Finally, due to relying on one-to-one node mapping from source to the generated graph, existing models leak node identity information and do not allow up-scaling/down-scaling the source graph size. In this paper, we bridge these gaps with a novel generative model called TIGGER. |
Shubham Gupta; Sahil Manchanda; Srikanta Bedathur; Sayan Ranu; |
| 918 | A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootstrapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. |
Anthony GX-Chen; Veronica Chelu; Blake A. Richards; Joelle Pineau; |
| 919 | Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, large models are typically needed for executing complex sequential tasks. To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing. |
Bing Han; Cheng Wang; Kaushik Roy; |
| 920 | End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To instantiate it, we propose modelling the prototypes probabilistically by the normalizing flows, which possess adaptive prototypical complexity to fully capture the underlying properties of each class label and allow for scalable stochastic optimization. |
Jun-Yi Hang; Min-Ling Zhang; Yanghe Feng; Xiaocheng Song; |
| 921 | Cross-Domain Few-Shot Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. |
Kaveh Hassani; |
| 922 | SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. |
Chaoyang He; Emir Ceyani; Keshav Balasubramanian; Murali Annavaram; Salman Avestimehr; |
| 923 | Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning Under Class Distribution Mismatch Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, the other parameters tend to fit irrelevant data, termed harmful parameters. Driven by this insight, we propose Safe Parameter Learning (SPL) to discover safe parameters and make the harmful parameters inactive, such that we can mitigate the adverse effects caused by unseen-class data. |
Rundong He; Zhongyi Han; Yang Yang; Yilong Yin; |
| 924 | Wasserstein Unsupervised Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the policies learned by MI-based methods cannot sufficiently explore the state space, despite they can be successfully identified from each other. Therefore we propose a new framework Wasserstein unsupervised reinforcement learning (WURL) where we directly maximize the distance of state distributions induced by different policies. |
Shuncheng He; Yuhang Jiang; Hongchang Zhang; Jianzhun Shao; Xiangyang Ji; |
| 925 | Multi-Mode Tensor Space Clustering Based on Low-Tensor-Rank Representation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To preserve said structure, in this work we exploit clustering in a high-order tensor space rather than a vector space. |
Yicong He; George K. Atia; |
| 926 | Toward Physically Realizable Quantum Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a new model for QNNs that relies on band-limited Fourier expansions of transfer functions of quantum perceptrons (QPs) to design scalable training procedures. |
Mohsen Heidari; Ananth Grama; Wojciech Szpankowski; |
| 927 | Reinforcement Learning of Causal Variables Using Mediation Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider the problem of acquiring causal representations and concepts in a reinforcement learning setting. |
Tue Herlau; Rasmus Larsen; |
| 928 | Anytime Guarantees Under Heavy-Tailed Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a direct takeaway, we obtain an easily implemented stochastic gradient-based algorithm for which all queried points formally enjoy sub-Gaussian error bounds, and in practice show noteworthy gains on real-world data applications. |
Matthew J. Holland; |
| 929 | Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. |
Chia-Yi Hsu; Pin-Yu Chen; Songtao Lu; Sijia Liu; Chia-Mu Yu; |
| 930 | Towards Automating Model Explanations with Certified Robustness Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, due to its data-driven nature, the interpretability itself is also potentially susceptible to malicious manipulations. Hence, our goal in this paper is to free human from this tedious process, while ensuring that the generated explanations are provably robust to adversarial perturbations. |
Mengdi Huai; Jinduo Liu; Chenglin Miao; Liuyi Yao; Aidong Zhang; |
| 931 | Multi-View Clustering on Topological Manifold Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to exploit the implied data manifold by learning the topological relationship between data points. |
Shudong Huang; Ivor Tsang; Zenglin Xu; Jiancheng Lv; Quan-Hui Liu; |
| 932 | Achieving Counterfactual Fairness for Causal Bandit Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study how to recommend an item at each step to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. |
Wen Huang; Lu Zhang; Xintao Wu; |
| 933 | Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by our observations, we propose an Uncertainty-aware Label Correction framework(ULC) to handle label noise on imbalanced datasets. |
Yingsong Huang; Bing Bai; Shengwei Zhao; Kun Bai; Fei Wang; |
| 934 | Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes. |
Guillermo Infante; Anders Jonsson; Vicenç Gómez; |
| 935 | Causal Discovery in Hawkes Processes By Minimum Description Length Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper approaches the problem of learning Granger-causal network in multi-dimensional Hawkes processes. We formulate this problem as a model selection task in which we follow the minimum description length (MDL) principle. |
Amirkasra Jalaldoust; Kateřina Hlaváčková-Schindler; Claudia Plant; |
| 936 | Group-Aware Threshold Adaptation for Fair Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To mitigate the discriminated model behaviors between different demographic groups, we introduce a novel post-processing method to optimize over multiple fairness constraints through group-aware threshold adaptation. |
Taeuk Jang; Pengyi Shi; Xiaoqian Wang; |
| 937 | Towards Discriminant Analysis Classifiers Using Online Active Learning Via Myoelectric Interfaces Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a discriminant analysis (DA) classifier that uses online active learning to address the need for the frequent training of myoelectric interfaces due to covariate shift. |
Andres G Jaramillo-Yanez; Marco E. Benalcázar; Sebastian Sardina; Fabio Zambetta; |
| 938 | Label Hallucination for Few-Shot Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: At the same time, training a simple linear classifier on top of “frozen” features learned from the large labeled dataset fails to adapt the model to the properties of the novel classes, effectively inducing underfitting. In this paper we propose an alternative approach to both of these two popular strategies. |
Yiren Jian; Lorenzo Torresani; |
| 939 | Learning Expected Emphatic Traces for Deep RL Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we investigate how to combine emphatic weightings with non-sequential, off-line data sampled from a replay buffer. |
Ray Jiang; Shangtong Zhang; Veronica Chelu; Adam White; Hado van Hasselt; |
| 940 | Delving Into Sample Loss Curve to Embrace Noisy and Imbalanced Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: How to handle them simultaneously is a key but under-explored problem. In this paper, we find that these two types of biased samples, though have similar transient loss, have distinguishable trend and characteristics in loss curves, which could provide valuable priors for sample weight assignment. |
Shenwang Jiang; Jianan Li; Ying Wang; Bo Huang; Zhang Zhang; Tingfa Xu; |
| 941 | Fast Graph Neural Tangent Kernel Via Kronecker Sketching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper provides the first algorithm to construct the kernel matrix in o(n^2N^3) running time. |
Shunhua Jiang; Yunze Man; Zhao Song; Zheng Yu; Danyang Zhuo; |
| 942 | Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. |
Mu Jin; Zhihao Ma; Kebing Jin; Hankz Hankui Zhuo; Chen Chen; Chao Yu; |
| 943 | Adaptive Kernel Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: For example, GNNs that focus on low-frequency information may not achieve satisfactory performance when high-frequency information is significant for the graphs, and vice versa. To solve this problem, in this paper, we propose a novel framework – i.e., namely Adaptive Kernel Graph Neural Network (AKGNN) – which learns to adapt to the optimal graph kernel in a unified manner at the first attempt. |
Mingxuan Ju; Shifu Hou; Yujie Fan; Jianan Zhao; Yanfang Ye; Liang Zhao; |
| 944 | Fully Spiking Variational Autoencoder Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this study, we build a variational autoencoder (VAE) with SNN to enable image generation. |
Hiromichi Kamata; Yusuke Mukuta; Tatsuya Harada; |
| 945 | Classifying Emails Into Human Vs Machine Category Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose building deep learning models at the message level. |
Changsung Kang; Hongwei Shang; Jean-Marc Langlois; |
| 946 | Self-Supervised Enhancement of Latent Discovery in GANs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Several methods for discovering interpretable directions in the latent space of pre-trained GANs have been proposed. |
Adarsh Kappiyath; Silpa Vadakkeeveetil Sreelatha; S. Sumitra; |
| 947 | Multiple-Source Domain Adaptation Via Coordinated Domain Encoders and Paired Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present a novel multiple-source unsupervised model for text classification under domain shift. |
Payam Karisani; |
| 948 | Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we give efficient algorithms for pure exploration in MNL-bandit. |
Nikolai Karpov; Qin Zhang; |
| 949 | IDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. |
Ramneet Kaur; Susmit Jha; Anirban Roy; Sangdon Park; Edgar Dobriban; Oleg Sokolsky; Insup Lee; |
| 950 | Partial Wasserstein Covering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the greedy algorithm is still inefficient because it requires solving linear programming for each objective function evaluation. To overcome this inefficiency, we propose quasi-greedy algorithms, which consist of a series of techniques for acceleration such as sensitivity analysis based on strong duality and the so-called C-transform in the optimal transport field. |
Keisuke Kawano; Satoshi Koide; Keisuke Otaki; |
| 951 | Optimal Tensor Transport Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a unified framework, called Optimal Tensor Transport (OTT), which takes the form of a generic formulation that encompasses OT, GW and Co-OT and can handle tensors of any order by learning possibly multiple transport plans. |
Tanguy Kerdoncuff; Rémi Emonet; Michael Perrot; Marc Sebban; |
| 952 | Dist2Cycle: A Simplicial Neural Network for Homology Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a graph convolutional model for learning functions parametrized by the k-homological features of simplicial complexes. |
Alexandros D Keros; Vidit Nanda; Kartic Subr; |
| 953 | Same State, Different Task: Continual Reinforcement Learning Without Interference Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This can occur, in reinforcement learning (RL) when an agent may be rewarded for achieving different goals from the same observation. In this paper we formalize this "interference" as distinct from the problem of forgetting. |
Samuel Kessler; Jack Parker-Holder; Philip Ball; Stefan Zohren; Stephen J. Roberts; |
| 954 | Spatial Frequency Bias in Convolutional Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Understanding the capability of Generative Adversarial Networks (GANs) in learning the full spectrum of spatial frequencies, that is, beyond the low-frequency dominant spectrum of natural images, is critical for assessing the reliability of GAN-generated data in any detail-sensitive application. In this work, we show that the ability of convolutional GANs to learn an image distribution depends on the spatial frequency of the underlying carrier signal, that is, they have a bias against learning high spatial frequencies. |
Mahyar Khayatkhoei; Ahmed Elgammal; |
| 955 | The Effect of Manifold Entanglement and Intrinsic Dimensionality on Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We empirically investigate the effect of class manifold entanglement and the intrinsic and extrinsic dimensionality of the data distribution on the sample complexity of supervised classification with deep ReLU networks. |
Daniel Kienitz; Ekaterina Komendantskaya; Michael Lones; |
| 956 | A Computable Definition of The Spectral Bias Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Neural networks have a bias towards low frequency functions. This spectral bias has been the subject of several previous studies, both empirical and theoretical. |
Jonas Kiessling; Filip Thor; |
| 957 | A Nested Bi-level Optimization Framework for Robust Few Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel robust meta-learning algorithm, NESTEDMAML, which learns to assign weights to training tasks or instances. |
Krishnateja Killamsetty; Changbin Li; Chen Zhao; Feng Chen; Rishabh Iyer; |
| 958 | Fast Monte-Carlo Approximation of The Attention Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce Monte-Carlo Attention (MCA), a randomized approximation method for reducing the computational cost of self-attention mechanisms in Transformer architectures. |
Hyunjun Kim; JeongGil Ko; |
| 959 | Towards A Rigorous Evaluation of Time-Series Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we theoretically and experimentally reveal that the PA protocol has a great possibility of overestimating the detection performance; even a random anomaly score can easily turn into a state-of-the-art TAD method. |
Siwon Kim; Kukjin Choi; Hyun-Soo Choi; Byunghan Lee; Sungroh Yoon; |
| 960 | Introducing Symmetries to Black Box Meta Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore the role of symmetries in meta-generalisation. |
Louis Kirsch; Sebastian Flennerhag; Hado van Hasselt; Abram Friesen; Junhyuk Oh; Yutian Chen; |
| 961 | Directed Graph Auto-Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. |
Georgios Kollias; Vasileios Kalantzis; Tsuyoshi Ide; Aurélie Lozano; Naoki Abe; |
| 962 | HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we transform DUNs into first-order ODE forms, and propose a high-order numerical architecture for ODE-inspired deep unfolding networks. |
Lin Kong; Wei Sun; Fanhua Shang; Yuanyuan Liu; Hongying Liu; |
| 963 | Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a framework to investigate the decision boundary and loss landscape similarities across states and across MDPs. |
Ezgi Korkmaz; |
| 964 | Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by the increased stochasticity in production chains, this paper explores a deep learning approach to deliver efficient and accurate approximations to the JSP. In particular, this paper proposes the design of a deep neural network architecture to exploit the problem structure, its integration with Lagrangian duality to capture the problem constraints, and a post-processing optimization, to guarantee solution feasibility. |
James Kotary; Ferdinando Fioretto; Pascal Van Hentenryck; |
| 965 | Learning Robust Policy Against Disturbance in Transition Dynamics Via State-Conservative Policy Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these algorithms can fail in scenarios where the disturbance from target environments is unknown or is intractable to model in simulators. To tackle this problem, we propose a novel model-free actor-critic algorithm—namely, state-conservative policy optimization (SCPO)—to learn robust policies without modeling the disturbance in advance. |
Yufei Kuang; Miao Lu; Jie Wang; Qi Zhou; Bin Li; Houqiang Li; |
| 966 | Gradient Based Activations for Accurate Bias-Free Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We evaluate the proposed model on standard benchmarks. |
Vinod K. Kurmi; Rishabh Sharma; Yash Vardhan Sharma; Vinay P Namboodiri; |
| 967 | TrustAL: Trustworthy Active Learning Using Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Under this assumption, the most recently trained model is a good surrogate for the current labeled data, from which data acquisition is requested based on uncertainty/diversity. Our contribution is debunking this myth and proposing a new objective for distillation. |
Beong-woo Kwak; Youngwook Kim; Yu Jin Kim; Seung-won Hwang; Jinyoung Yeo; |
| 968 | Tight Neural Network Verification Via Semidefinite Relaxations and Linear Reformulations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel semidefinite programming (SDP) relaxation that enables tight and efficient verification of neural networks. |
Jianglin Lan; Yang Zheng; Alessio Lomuscio; |
| 969 | Learning Adversarial Markov Decision Processes with Delayed Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present novel algorithms based on policy optimization that achieve near-optimal high-probability regret of (K+D)¹ᐟ² under full-information feedback, where K is the number of episodes and D=∑ₖ dᵏ is the total delay. |
Tal Lancewicki; Aviv Rosenberg; Yishay Mansour; |
| 970 | Learning Not to Learn: Nature Versus Nurture In Silico Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: At the same time, many behaviors are highly adaptive and can be tailored to specific environments by means of learning. In this work, we use mathematical analysis and the framework of memory-based meta-learning (or ’learning to learn’) to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior. |
Robert Tjarko Lange; Henning Sprekeler; |
| 971 | Optimization for Classical Machine Learning Problems on The GPU Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Here, we extend the GENO framework to also solve constrained optimization problems on the GPU. |
Sören Laue; Mark Blacher; Joachim Giesen; |
| 972 | Interpretable Clustering Via Multi-Polytope Machines Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, despite its use as a tool for subgroup discovery and description few state-of-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. |
Connor Lawless; Jayant Kalagnanam; Lam M Nguyen; Dzung Phan; Chandra Reddy; |
| 973 | Episodic Policy Gradient Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly. |
Hung Le; Majid Abdolshah; Thommen K. George; Kien Do; Dung Nguyen; Svetha Venkatesh; |
| 974 | Stability Verification in Stochastic Control Systems Via Neural Network Supermartingales Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking supermartingales (RSMs) to certify a.s. asymptotic stability, and (b) we present a method for learning neural network RSMs. |
Mathias Lechner; Đorđe Žikelić; Krishnendu Chatterjee; Thomas A. Henzinger; |
| 975 | Learning Losses for Strategic Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In our work we take a learning theoretic perspective, focusing on the sample complexity needed to learn a good decision rule which is robust to strategic manipulation. We perform this analysis by introducing a novel loss function, the strategic manipulation loss, which takes into account both the accuracy of the final decision rule and its vulnerability to manipulation. |
Tosca Lechner; Ruth Urner; |
| 976 | Differentially Private Normalizing Flows for Synthetic Tabular Data Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a differentially private normalizing flow model for heterogeneous tabular data. |
Jaewoo Lee; Minjung Kim; Yonghyun Jeong; Youngmin Ro; |
| 977 | Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We use the state-of-the-art methods as baselines, and show notable performance gain for all the baselines. |
Jun-Tae Lee; Hyunsin Park; Sungrack Yun; Simyung Chang; |
| 978 | Fast and Efficient MMD-Based Fair PCA Via Optimization Over Stiefel Manifold Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We formulate the problem of fair PCA subject to MMD constraints as a non-convex optimization over the Stiefel manifold and solve it using the Riemannian Exact Penalty Method with Smoothing (REPMS). |
Junghyun Lee; Gwangsu Kim; Mahbod Olfat; Mark Hasegawa-Johnson; Chang D. Yoo; |
| 979 | Augmentation-Free Self-Supervised Learning on Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel augmentation-free self-supervised learning framework for graphs, named AFGRL. |
Namkyeong Lee; Junseok Lee; Chanyoung Park; |
| 980 | Fast and Robust Online Inference with Stochastic Gradient Descent Via Random Scaling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms. |
Sokbae Lee; Yuan Liao; Myung Hwan Seo; Youngki Shin; |
| 981 | Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain. |
Dan Ley; Umang Bhatt; Adrian Weller; |
| 982 | Invariant Information Bottleneck for Domain Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by IRM, in this paper we propose a novel formulation for domain generalization, dubbed invariant information bottleneck (IIB). |
Bo Li; Yifei Shen; Yezhen Wang; Wenzhen Zhu; Colorado Reed; Dongsheng Li; Kurt Keutzer; Han Zhao; |
| 983 | Chunk Dynamic Updating for Group Lasso with ODEs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Due to the complexity of L-2,1 norm, how to achieve accurate chunk incremental and decremental learning efficiently for group Lasso is still an open question. To address this challenging problem, in this paper, we propose a novel accurate dynamic updating algorithm for group Lasso by utilizing the technique of Ordinary Differential Equations (ODEs), which can incorporate or eliminate a chunk of samples from original training set without retraining the model from scratch. |
Diyang Li; Bin Gu; |
| 984 | Policy Learning for Robust Markov Decision Process with A Mismatched Generative Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we consider policy learning for Robust Markov Decision Processes (RMDP), where the agent tries to seek a robust policy with respect to unexpected perturbations on the environments. |
Jialian Li; Tongzheng Ren; Dong Yan; Hang Su; Jun Zhu; |
| 985 | A Fully Single Loop Algorithm for Bilevel Optimization Without Hessian Inverse Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Hessian inverse free Fully Single Loop Algorithm (FSLA) for bilevel optimization problems. |
Junyi Li; Bin Gu; Heng Huang; |
| 986 | A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we target the problem of causal structure learning from observational mixed-type data. |
Yan Li; Rui Xia; Chunchen Liu; Liang Sun; |
| 987 | Sharp Analysis of Random Fourier Features in Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our study covers the standard feature sampling method for which we reduce the number of features required, as well as a problem-dependent sampling method which further reduces the number of features while still keeping the optimal generalization property. |
Zhu Li; |
| 988 | Zeroth-Order Optimization for Composite Problems with Functional Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel zeroth-order inexact augmented Lagrangian method (ZO-iALM) to solve black-box optimization problems, which involve a composite (i.e., smooth+nonsmooth) objective and functional constraints. |
Zichong Li; Pin-Yu Chen; Sijia Liu; Songtao Lu; Yangyang Xu; |
| 989 | Robust Graph-Based Multi-View Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Though demonstrating promising clustering performance in various applications, we observe that their formulations are usually non-convex, leading to a local optimum. In this paper, we propose a novel MVC algorithm termed robust graph-based multi-view clustering (RG-MVC) to address this issue. |
Weixuan Liang; Xinwang Liu; Sihang Zhou; Jiyuan Liu; Siwei Wang; En Zhu; |
| 990 | Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, a novel graph-based convolution for imitating the meteorological flows is proposed to capture the local spatial patterns. |
Haitao Lin; Zhangyang Gao; Yongjie Xu; Lirong Wu; Ling Li; Stan Z. Li; |
| 991 | On The Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For the use in future studies, we propose simple and effective settings without using practically unknown information. |
Li-Chung Lin; Cheng-Hung Liu; Chih-Ming Chen; Kai-Chin Hsu; I-Feng Wu; Ming-Feng Tsai; Chih-Jen Lin; |
| 992 | Deep Unsupervised Hashing with Latent Semantic Components Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To make up the defect, we propose a novel Deep Semantic Components Hashing (DSCH), which involves a common sense that an image normally contains a bunch of semantic components with homology and co-occurrence relationships. |
Qinghong Lin; Xiaojun Chen; Qin Zhang; Shaotian Cai; Wenzhe Zhao; Hongfa Wang; |
| 993 | SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing works overlook the different significance of different classes. We introduce Set-classifier with class-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiple labels to each example. |
Zhen Lin; Lucas Glass; M. Brandon Westover; Cao Xiao; Jimeng Sun; |
| 994 | RareGAN: Generating Samples for Rare Classes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose RareGAN, a novel synthesis of three key ideas: (1) extending conditional GANs to use labelled and unlabelled data for better generalization; (2) an active learning approach that requests the most useful labels; and (3) a weighted loss function to favor learning the rare class. |
Zinan Lin; Hao Liang; Giulia Fanti; Vyas Sekar; |
| 995 | Conjugated Discrete Distributions for Distributional Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work we continue to build upon recent advances in reinforcement learning for finite Markov processes. |
Björn Lindenberg; Jonas Nordqvist; Karl-Olof Lindahl; |
| 996 | Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an approach which learns a hyper-policy, whose input is time, that outputs the parameters of the policy to be queried at that time. |
Pierre Liotet; Francesco Vidaich; Alberto Maria Metelli; Marcello Restelli; |
| 997 | Learning Parameterized Task Structure for Generalization to Unseen Entities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Moreover, an agent may also need to solve unseen tasks, which can involve unseen entities. To this end, we formulate parameterized subtask graph inference (PSGI), a method for modeling subtask dependencies using first-order logic with factored entities. |
Anthony Liu; Sungryull Sohn; Mahdi Qazwini; Honglak Lee; |
| 998 | Stationary Diffusion State Neural Estimation for Multiview Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. |
Chenghua Liu; Zhuolin Liao; Yixuan Ma; Kun Zhan; |
| 999 | Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e.g., graph, network). |
Huafeng Liu; Tong Zhou; Jiaqi Wang; |
| 1000 | Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The key insight of this work is that we introduce a latent-conditioned policy to provide goals and intrinsic rewards for learning the goal-conditioned policy. |
Jinxin Liu; Donglin Wang; Qiangxing Tian; Zhengyu Chen; |
| 1001 | Transformer with Memory Replay Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Transformer with Memory Replay, which integrates memory replay with transformer, making transformer more sample efficient. |
Rui Liu; Barzan Mozafari; |
| 1002 | Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). |
Suyuan Liu; Siwei Wang; Pei Zhang; Kai Xu; Xinwang Liu; Changwang Zhang; Feng Gao; |
| 1003 | Trusted Multi-View Deep Learning with Opinion Aggregation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Aiming at the problem, we revisit the multi-view learning from the perspective of opinion aggregation and thereby devise a trusted multi-view deep learning method. |
Wei Liu; Xiaodong Yue; Yufei Chen; Thierry Denoeux; |
| 1004 | Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we start from a particular edge-to-vertex transform and exploit the isomorphism property in the edge-to-vertex dual graphs. |
Xin Liu; Yangqiu Song; |
| 1005 | Deep Graph Clustering Via Dual Correlation Reduction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. |
Yue Liu; Wenxuan Tu; Sihang Zhou; Xinwang Liu; Linxuan Song; Xihong Yang; En Zhu; |
| 1006 | Optimistic Initialization for Exploration in Continuous Control Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a framework for more effectively incorporating optimistic initialization into reinforcement learning for continuous control. |
Sam Lobel; Omer Gottesman; Cameron Allen; Akhil Bagaria; George Konidaris; |
| 1007 | Fast and Data Efficient Reinforcement Learning from Pixels Via Non-parametric Value Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient. |
Alexander Long; Alan Blair; Herke van Hoof; |
| 1008 | Frozen Pretrained Transformers As Universal Computation Engines Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning — in particular, without finetuning of the self-attention and feedforward layers of the residual blocks. We consider such a model, which we call a Frozen Pretrained Transformer (FPT), and study finetuning it on a variety of sequence classification tasks spanning numerical computation, vision, and protein fold prediction. |
Kevin Lu; Aditya Grover; Pieter Abbeel; Igor Mordatch; |
| 1009 | Adapt to Environment Sudden Changes By Learning A Context Sensitive Policy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes an environment sensitive contextual policy learning (ESCP) approach, in order to improve both the sensitivity and the robustness of context encoding. |
Fan-Ming Luo; Shengyi Jiang; Yang Yu; ZongZhang Zhang; Yi-Feng Zhang; |
| 1010 | Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a DeepGCN based View-Specific MVML method (D-VSM) which can bypass seeking for the shared subspace representation, and instead directly encoding the feature representation of each individual view through the deep GCN to couple with the information derived from the other views. |
Gengyu Lyu; Xiang Deng; Yanan Wu; Songhe Feng; |
| 1011 | Efficient Continuous Control with Double Actors and Regularized Critics Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we explore the potential of double actors, which has been neglected for a long time, for better value estimation in the continuous setting. |
Jiafei Lyu; Xiaoteng Ma; Jiangpeng Yan; Xiu Li; |
| 1012 | Recursive Reasoning Graph for Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper adopts a recursive reasoning model in a centralized-training-decentralized-execution framework to help learning agents better cooperate with or compete against others. |
Xiaobai Ma; David Isele; Jayesh K. Gupta; Kikuo Fujimura; Mykel J. Kochenderfer; |
| 1013 | Sharp Restricted Isometry Property Bounds for Low-Rank Matrix Recovery Problems with Corrupted Measurements Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study a general low-rank matrix recovery problem with linear measurements corrupted by some noise. |
Ziye Ma; Yingjie Bi; Javad Lavaei; Somayeh Sojoudi; |
| 1014 | Cross-Lingual Adversarial Domain Adaptation for Novice Programming Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we focus on two essential SMP tasks: program classification and early prediction of student success and propose a Cross-Lingual Adversarial Domain Adaptation (CrossLing) framework that can leverage a large programming dataset to learn features that can improve SMP’s build using a much smaller dataset in a different programming language. |
Ye Mao; Farzaneh Khoshnevisan; Thomas Price; Tiffany Barnes; Min Chi; |
| 1015 | Hard to Forget: Poisoning Attacks on Certified Machine Unlearning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Rapid progress has been made towards privacy guarantees on the indistinguishability of unlearned and retrained models, but current formalisms do not place practical bounds on computation. In this paper we demonstrate how an attacker can exploit this oversight, highlighting a novel attack surface introduced by machine unlearning. |
Neil G. Marchant; Benjamin I. P. Rubinstein; Scott Alfeld; |
| 1016 | Exploring Safer Behaviors for Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, we propose a Safety-Oriented Search that complements Deep RL algorithms to bias the policy toward safety within an evolutionary cost optimization. |
Enrico Marchesini; Davide Corsi; Alessandro Farinelli; |
| 1017 | FGOT: Graph Distances Based on Filters and Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work we introduce the filter graph distance. |
Hermina Petric Maretic; Mireille El Gheche; Giovanni Chierchia; Pascal Frossard; |
| 1018 | When AI Difficulty Is Easy: The Explanatory Power of Predicting IRT Difficulty Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Many issues such as adversarial examples, out of distribution performance, Clever Hans phenomena, and the wider areas of AI evaluation and explainable AI, have to do with the following question: Did the system fail because it is a hard instance or because something else? In this paper we address this question with a generic method for estimating IRT-based instance difficulty for a wide range of AI domains covering several areas, from supervised feature-based classification to automated reasoning. |
Fernando Martínez-Plumed; David Castellano; Carlos Monserrat-Aranda; José Hernández-Orallo; |
| 1019 | Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified By Other Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The transformation progressively fades-out as long as training proceeds, until it completely vanishes. In this work we revisit and extend this idea, introducing a radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial Machine Learning. |
Simone Marullo; Matteo Tiezzi; Marco Gori; Stefano Melacci; |
| 1020 | An Experimental Design Approach for Regret Minimization in Logistic Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we consider the problem of regret minimization for logistic bandits. |
Blake Mason; Kwang-Sung Jun; Lalit Jain; |
| 1021 | Coordinate Descent on The Orthogonal Group for Recurrent Neural Network Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In addition, we numerically show that the Riemannian gradient has an approximately sparse structure. Leveraging this observation, we propose a variant of our proposed algorithm that relies on the Gauss-Southwell coordinate selection rule. |
Estelle Massart; Vinayak Abrol; |
| 1022 | Curiosity-Driven Exploration Via Latent Bayesian Surprise Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this contribution, we propose to apply Bayesian surprise in a latent space representing the agent’s current understanding of the dynamics of the system, drastically reducing the computational costs. |
Pietro Mazzaglia; Ozan Catal; Tim Verbelen; Bart Dhoedt; |
| 1023 | What Can We Learn Even from The Weakest? Learning Sketches for Programmatic Strategies Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we show that behavioral cloning can be used to learn effective sketches of programmatic strategies. |
Leandro C. Medeiros; David S. Aleixo; Levi H. S. Lelis; |
| 1024 | Top-Down Deep Clustering with Multi-Generator GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose HC-MGAN, a new technique based on GANs with multiple generators (MGANs), which have not been explored for clustering. |
Daniel P. M. de Mello; Renato M. Assunção; Fabricio Murai; |
| 1025 | Temporal Knowledge Graph Completion Using Box Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose BoxTE, a box embedding model for TKGC, building on the static knowledge graph embedding model BoxE. |
Johannes Messner; Ralph Abboud; Ismail Ilkan Ceylan; |
| 1026 | An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It is therefore desirable to compare clustering algorithms not only on their optimally tuned performance, but also some notion of how realistic it would be to obtain this performance in practice. We propose an evaluation of clustering methods capturing this ease-of-tuning by modeling the expected best clustering score under a given computation budget. |
Siddhartha Mishra; Nicholas Monath; Michael Boratko; Ariel Kobren; Andrew McCallum; |
| 1027 | Simple Unsupervised Graph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. |
Yujie Mo; Liang Peng; Jie Xu; Xiaoshuang Shi; Xiaofeng Zhu; |
| 1028 | The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study honest hyperparameter selection for differentially private machine learning, in which the process of hyperparameter tuning is accounted for in the overall privacy budget. |
Shubhankar Mohapatra; Sajin Sasy; Xi He; Gautam Kamath; Om Thakkar; |
| 1029 | Learning Bayesian Networks in The Presence of Structural Side Information Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, often in many applications, side information about the underlying structure can potentially reduce the learning complexity. In this paper, we develop a recursive constraint-based algorithm that efficiently incorporates such knowledge (i.e., side information) into the learning process. |
Ehsan Mokhtarian; Sina Akbari; Fateme Jamshidi; Jalal Etesami; Negar Kiyavash; |
| 1030 | Preemptive Image Robustification for Protecting Users Against Man-in-the-Middle Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we focus on a real-world threat model where a Man-in-the-Middle adversary maliciously intercepts and perturbs images web users upload online. |
Seungyong Moon; Gaon An; Hyun Oh Song; |
| 1031 | Provable Guarantees for Understanding Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we develop an analytical framework that characterizes and unifies the theoretical understanding for OOD detection. |
Peyman Morteza; Yixuan Li; |
| 1032 | Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. |
Tong Mu; Georgios Theocharous; David Arbour; Emma Brunskill; |
| 1033 | Unsupervised Reinforcement Learning in Multiple Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we foster an exploration strategy that is sensitive to the most adverse cases within the class. |
Mirco Mutti; Mattia Mancassola; Marcello Restelli; |
| 1034 | Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an approach called Federated Learning with Relevant Data (FLRD), that facilitates clients to derive updates using relevant data. |
Lokesh Nagalapatti; Ruhi Sharma Mittal; Ramasuri Narayanam; |
| 1035 | A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. |
Krishna Prasad Neupane; Ervine Zheng; Yu Kong; Qi Yu; |
| 1036 | Out of Distribution Data Detection Using Dropout Bayesian Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce an alternative approach to measuring embedding uncertainty, and demonstrate how incorporating embedding uncertainty improves OOD data identification across three tasks: image classification, language classification, and malware detection. |
Andre T. Nguyen; Fred Lu; Gary Lopez Munoz; Edward Raff; Charles Nicholas; James Holt; |
| 1037 | Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: When the model class is misspecified or has a limited representational capacity, model parameters with high likelihood might not necessarily result in high performance of the agent on a downstream control task. To alleviate this problem, we propose an end-to-end approach for model learning which directly optimizes the expected returns using implicit differentiation. |
Evgenii Nikishin; Romina Abachi; Rishabh Agarwal; Pierre-Luc Bacon; |
| 1038 | Improving Evidential Deep Learning Via Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, it is possible that the target is inaccurately predicted due to the gradient shrinkage problem of the original loss function of the ENet, the negative log marginal likelihood (NLL) loss. In this paper, the objective is to improve the prediction accuracy of the ENet while maintaining its efficient uncertainty estimation by resolving the gradient shrinkage problem. |
Dongpin Oh; Bonggun Shin; |
| 1039 | Clustering Approach to Solve Hierarchical Classification Problem Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite the obvious interest of this approach, our theoretical analysis shows a high complexity for finding an exact solution. We propose in this paper an original approach based on the association of clustering and classification approaches to overcome this limitation. |
Aomar Osmani; Massinissa Hamidi; Pegah Alizadeh; |
| 1040 | Random Tensor Theory for Tensor Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new framework for tensor decomposition based on trace invariants, which are particular cases of tensor networks. |
Mohamed Ouerfelli; Mohamed Tamaazousti; Vincent Rivasseau; |
| 1041 | Bag Graph: Multiple Instance Learning Using Bayesian Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we consider modelling the interactions between bags using a graph and employ Graph Neural Networks (GNNs) to facilitate end-to-end learning. |
Soumyasundar Pal; Antonios Valkanas; Florence Regol; Mark Coates; |
| 1042 | Competing Mutual Information Constraints with Stochastic Competition-Based Activations for Learning Diversified Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work aims to address the long-established problem of learning diversified representations. |
Konstantinos P. Panousis; Anastasios Antoniadis; Sotirios Chatzis; |
| 1043 | Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. |
Giseung Park; Sungho Choi; Youngchul Sung; |
| 1044 | Deformable Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address the two common problems of graph convolution, in this paper, we propose Deformable Graph Convolutional Networks (Deformable GCNs) that adaptively perform convolution in multiple latent spaces and capture short/long-range dependencies between nodes. |
Jinyoung Park; Sungdong Yoo; Jihwan Park; Hyunwoo J. Kim; |
| 1045 | Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present, a novel, yet simple Mixup-variant that captures the best of both worlds. |
Joonhyung Park; June Yong Yang; Jinwoo Shin; Sung Ju Hwang; Eunho Yang; |
| 1046 | Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult. To tackle this challenge, we present the first Mixup-like graph augmentation method called Graph Transplant, which mixes irregular graphs in data space. |
Joonhyung Park; Hajin Shim; Eunho Yang; |
| 1047 | CC-CERT: A Probabilistic Approach to Certify General Robustness of Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds that can be used in general attack settings. |
Mikhail Pautov; Nurislam Tursynbek; Marina Munkhoeva; Nikita Muravev; Aleksandr Petiushko; Ivan Oseledets; |
| 1048 | Covered Information Disentanglement: Model Transparency Via Unbiased Permutation Importance Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, this method and other related approaches will undervalue the importance of a feature in the presence of covariates since these cover part of its provided information. To address this issue, we propose Covered Information Disentanglement CID, a framework that considers all feature information overlap to correct the values provided by permutation importance. |
João P. B. Pereira; Erik S. G. Stroes; Aeilko H. Zwinderman; Evgeni Levin; |
| 1049 | On The Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Namely, we show that for certain data sources, the most accurate classifier that satisfies EO is a trivial classifier. |
Carlos Pinzón; Catuscia Palamidessi; Pablo Piantanida; Frank Valencia; |
| 1050 | Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show that SNNs can be trained for practical sequential tasks by proposing modifications to a network of LIF neurons that enable internal states to learn long sequences and make their inherent recurrence resilient to the vanishing gradient problem. |
Wachirawit Ponghiran; Kaushik Roy; |
| 1051 | How Private Is Your RL Policy? An Inverse RL Based Analysis Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a new Privacy-Aware Inverse RL analysis framework (PRIL) that involves performing reward reconstruction as an adversarial attack on private policies that the agents may deploy. |
Kritika Prakash; Fiza Husain; Praveen Paruchuri; Sujit Gujar; |
| 1052 | Detecting Misclassification Errors in Neural Networks with A Gaussian Process Model Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents a new framework that produces a quantitative metric for detecting misclassification errors. |
Xin Qiu; Risto Miikkulainen; |
| 1053 | DeepType 2: Superhuman Entity Linking, All You Need Is Type Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This is made possible by associating a context-specific score to each of the entity’s abstract representation’s sub-components (types), and summing these scores to form a candidate entity logit. In this paper, we explain how this factorization focuses the learning on the salient types of the candidate entities. |
Jonathan Raiman; |
| 1054 | Federated Nearest Neighbor Classification with A Colony of Fruit-Flies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The mathematical formalization of a neurological mechanism in the fruit-fly olfactory circuit as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently proposed and "reprogrammed" for various learning tasks such as similarity search, outlier detection and text embeddings. We propose a novel reprogramming of this hash and bloom filter to emulate the canonical nearest neighbor classifier (NNC) in the challenging Federated Learning (FL) setup where training and test data are spread across parties and no data can leave their respective parties. |
Parikshit Ram; Kaushik Sinha; |
| 1055 | I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, the high-dimensionality of the underlying distribution, and the inability to sample from it, pose significant challenges. To this end, we develop an Importance Sampling based distance metric — I-SEA — which enjoys the properties of a metric while consistently achieving superior performance for machine learning tasks such as classification and representation learning. |
Sirisha Rambhatla; Zhengping Che; Yan Liu; |
| 1056 | Saving Stochastic Bandits from Poisoning Attacks Via Limited Data Verification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This amount of contamination is also necessary, as we prove that there exists an O(log T) regret bandit algorithm, specifically the classical UCB, that requires Omega(log T) amount of contamination to suffer regret Omega(T). To combat such poisoning attacks, our second main contribution is to propose verification based mechanisms, which use limited verification to access a limited number of uncontaminated rewards. |
Anshuka Rangi; Long Tran-Thanh; Haifeng Xu; Massimo Franceschetti; |
| 1057 | DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an adaptive, resource-aware, on-device learning mechanism, DISTREAL, which is able to fully and efficiently utilize the available resources on devices in a distributed manner, increasing the convergence speed. |
Martin Rapp; Ramin Khalili; Kilian Pfeiffer; Jörg Henkel; |
| 1058 | Sublinear Time Approximation of Text Similarity Matrices Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, much less is understood about indefinite (non-PSD) similarity matrices, which often arise in NLP. Motivated by the observation that many of these matrices are still somewhat close to PSD, we introduce a generalization of the popular Nystrom method to the indefinite setting. |
Archan Ray; Nicholas Monath; Andrew McCallum; Cameron Musco; |
| 1059 | Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker’s action and evolves dynamically in time according to a geometric decay process. |
Mitas Ray; Lillian J. Ratliff; Dmitriy Drusvyatskiy; Maryam Fazel; |
| 1060 | On Causally Disentangled Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We motivate the need for new metrics and datasets to study causal disentanglement and propose two evaluation metrics and a dataset. |
Abbavaram Gowtham Reddy; Benin Godfrey L; Vineeth N Balasubramanian; |
| 1061 | Conditional Loss and Deep Euler Scheme for Time Series Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce three new generative models for time series that are based on Euler discretization of Stochastic Differential Equations (SDEs) and Wasserstein metrics. |
Carl Remlinger; Joseph Mikael; Romuald Elie; |
| 1062 | Offline Reinforcement Learning As Anti-exploration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Instantiated with a bonus based on the prediction error of a variational autoencoder, we show that our simple agent is competitive with the state of the art on a set of continuous control locomotion and manipulation tasks. |
Shideh Rezaeifar; Robert Dadashi; Nino Vieillard; Léonard Hussenot; Olivier Bachem; Olivier Pietquin; Matthieu Geist; |
| 1063 | Interpretable Neural Subgraph Matching for Graph Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In response, we propose ISONET, a novel interpretable neural edge alignment formulation, which is better able to learn the edge-consistent mapping necessary for subgraph matching. |
Indradyumna Roy; Venkata Sai Baba Reddy Velugoti; Soumen Chakrabarti; Abir De; |
| 1064 | FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. |
Yichen Ruan; Carlee Joe-Wong; |
| 1065 | Knowledge Distillation Via Constrained Variational Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a framework for distilling the knowledge of a powerful discriminative model such as a neural network into commonly used graphical models known to be more interpretable (e.g., topic models, autoregressive Hidden Markov Models). |
Ardavan Saeedi; Yuria Utsumi; Li Sun; Kayhan Batmanghelich; Li-wei Lehman; |
| 1066 | Hypergraph Modeling Via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel K-Means, and Heat Kernel Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. |
Shota Saito; |
| 1067 | Reverse Differentiation Via Predictive Coding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the recent literature shows also that there is no biologically plausible method yet that can exactly replicate the weight update of BP on complex models. To fill this gap, in this paper, we generalize (PC and) Z-IL by directly defining it on computational graphs, and show that it can perform exact reverse differentiation. |
Tommaso Salvatori; Yuhang Song; Zhenghua Xu; Thomas Lukasiewicz; Rafal Bogacz; |
| 1068 | VACA: Designing Variational Graph Autoencoders for Causal Queries Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. |
Pablo Sánchez-Martin; Miriam Rateike; Isabel Valera; |
| 1069 | Verification of Neural-Network Control Systems By Integrating Taylor Models and Zonotopes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present an algorithm to chain approaches based on Taylor models and zonotopes, yielding a precise reachability algorithm for NNCS. |
Christian Schilling; Marcelo Forets; Sebastián Guadalupe; |
| 1070 | Scaling Up Influence Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. |
Andrea Schioppa; Polina Zablotskaia; David Vilar; Artem Sokolov; |
| 1071 | Chaining Value Functions for Off-Policy Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we discuss a novel family of off-policy prediction algorithms which are convergent by construction. |
Simon Schmitt; John Shawe-Taylor; Hado van Hasselt; |
| 1072 | Graph Filtration Kernels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The majority of popular graph kernels is based on the concept of Haussler’s R-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtrations: Using meaningful orders on the set of edges, which allow to construct a sequence of nested graphs, we can consider a graph at multiple granularities. |
Till Schulz; Pascal Welke; Stefan Wrobel; |
| 1073 | Neural Networks Classify Through The Class-Wise Means of Their Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, based on an asymptotic analysis of the Softmax layer, we show that when training neural networks for classification tasks, the weight vectors corre sponding to each class of the Softmax layer tend to converge to the class-wise means computed at the representation layer (for specific choices of the representation activation). |
Mohamed El Amine Seddik; Mohamed Tamaazousti; |
| 1074 | Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose learning rules with the recently proposed logical neural networks (LNN). |
Prithviraj Sen; Breno W. S. R. de Carvalho; Ryan Riegel; Alexander Gray; |
| 1075 | Max-Margin Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: As SVM optimization can be computationally demanding, especially in an end-to-end setting, we present simplifications that alleviate the computational burden. |
Anshul Shah; Suvrit Sra; Rama Chellappa; Anoop Cherian; |
| 1076 | Learning to Transfer with Von Neumann Conditional Divergence Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce the recently proposed von Neumann conditional divergence to improve the transferability across multiple domains. |
Ammar Shaker; Shujian Yu; Daniel Oñoro-Rubio; |
| 1077 | Online Apprenticeship Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce an online variant of AL (Online Apprenticeship Learning; OAL), where the agent is expected to perform comparably to the expert while interacting with the environment. |
Lior Shani; Tom Zahavy; Shie Mannor; |
| 1078 | HoD-Net: High-Order Differentiable Deep Neural Networks and Applications Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a deep architecture named HoD-Net to enable high-order differentiability for deep learning. |
Siyuan Shen; Tianjia Shao; Kun Zhou; Chenfanfu Jiang; Feng Luo; Yin Yang; |
| 1079 | Conditional Generative Model Based Predicate-Aware Query Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose ELECTRA, a predicate-aware AQP system that can answer analytics-style queries with a large number of predicates with much smaller approximation errors. |
Nikhil Sheoran; Subrata Mitra; Vibhor Porwal; Siddharth Ghetia; Jatin Varshney; Tung Mai; Anup Rao; Vikas Maddukuri; |
| 1080 | Learning Bounded Context-Free-Grammar Via LSTM and The Transformer: Difference and The Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: But the reason is barely understood. We study such practical differences between LSTM and Transformer and propose an explanation based on their latent space decomposition patterns. |
Hui Shi; Sicun Gao; Yuandong Tian; Xinyun Chen; Jishen Zhao; |
| 1081 | Shape Prior Guided Attack: Sparser Perturbations on 3D Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method named SPGA (Shape Prior Guided Attack) to generate adversarial point cloud examples. |
Zhenbo Shi; Zhi Chen; Zhenbo Xu; Wei Yang; Zhidong Yu; Liusheng Huang; |
| 1082 | TRF: Learning Kernels with Tuned Random Features Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we propose selecting the density function from a reproducing kernel Hilbert space to allow us to search the space of all translation-invariant kernels. |
Alistair Shilton; Sunil Gupta; Santu Rana; Arun Kumar Venkatesh; Svetha Venkatesh; |
| 1083 | Estimation of Local Average Treatment Effect By Data Combination Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider a novel problem setting in which LATE, as a function of covariates, is nonparametrically identified from the combination of separately observed datasets. |
Kazuhiko Shinoda; Takahiro Hoshino; |
| 1084 | Constraint-Driven Explanations for Black-Box ML Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While this approach affords the user the ability to tailor the explanation to their needs, striking a balance between flexibility, theoretical rigor and computational cost has remained an open challenge. We propose a novel constraint-driven explanation generation approach which simultaneously addresses these issues in a modular fashion. |
Aditya A. Shrotri; Nina Narodytska; Alexey Ignatiev; Kuldeep S Meel; Joao Marques-Silva; Moshe Y. Vardi; |
| 1085 | Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work proposes a theoretically motivated framework to correct label noise in the presence of multiple labels inferred from unsupervised models. |
Amila Silva; Ling Luo; Shanika Karunasekera; Christopher Leckie; |
| 1086 | QUILT: Effective Multi-Class Classification on Quantum Computers Using An Ensemble of Diverse Quantum Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits … |
Daniel Silver; Tirthak Patel; Devesh Tiwari; |
| 1087 | EqGNN: Equalized Node Opportunity in Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present a GNN framework that allows optimizing representations for the notion of Equalized Odds fairness criteria. |
Uriel Singer; Kira Radinsky; |
| 1088 | ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a different approach, named Approximate Coded Inference (ApproxIFER), that does not require training any parity models, hence it is agnostic to the model hosted by the cloud and can be readily applied to different data domains and model architectures. |
Mahdi Soleymani; Ramy E. Ali; Hessam Mahdavifar; A. Salman Avestimehr; |
| 1089 | Feature Importance Explanations for Temporal Black-Box Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose TIME, a method to explain models that are inherently temporal in nature. |
Akshay Sood; Mark Craven; |
| 1090 | Reward-Weighted Regression Converges to A Global Optimum Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we provide for the first time a proof that RWR converges to a global optimum when no function approximation is used, in a general compact setting. |
Miroslav Štrupl; Francesco Faccio; Dylan R. Ashley; Rupesh Kumar Srivastava; Jürgen Schmidhuber; |
| 1091 | Gradient-Based Novelty Detection Boosted By Self-Supervised Binary Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel, self-supervised approach that does not rely on any pre-defined OOD data: (1) The new method evaluates the Mahalanobis distance of the gradients between the in-distribution and OOD data. |
Jingbo Sun; Li Yang; Jiaxin Zhang; Frank Liu; Mahantesh Halappanavar; Deliang Fan; Yu Cao; |
| 1092 | Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. |
Mingfei Sun; Sam Devlin; Katja Hofmann; Shimon Whiteson; |
| 1093 | Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing OOD detection methods require many labeled In-Distribution (ID) data, causing a heavy labeling cost. In this paper, we focus on the more realistic scenario, where limited labeled data and abundant unlabeled data are available, and these unlabeled data are mixed with ID and OOD samples. |
Yi-Xuan Sun; Wei Wang; |
| 1094 | Generalized Equivariance and Preferential Labeling for GNN Node Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we analyze the limitation of existing approaches to node classification problems. |
Zeyu Sun; Wenjie Zhang; Lili Mou; Qihao Zhu; Yingfei Xiong; Lu Zhang; |
| 1095 | Explainable and Local Correction of Classification Models Using Decision Trees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we pose two challenges to model correction. |
Hirofumi Suzuki; Hiroaki Iwashita; Takuya Takagi; Keisuke Goto; Yuta Fujishige; Satoshi Hara; |
| 1096 | Consistency Regularization for Adversarial Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary `consistency’ regularization loss during AT. |
Jihoon Tack; Sihyun Yu; Jongheon Jeong; Minseon Kim; Sung Ju Hwang; Jinwoo Shin; |
| 1097 | Regularization Guarantees Generalization in Bayesian Reinforcement Learning Through Algorithmic Stability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study generalization in Bayesian RL under the probably approximately correct (PAC) framework, using the method of algorithmic stability. |
Aviv Tamar; Daniel Soudry; Ev Zisselman; |
| 1098 | FedProto: Federated Prototype Learning Across Heterogeneous Clients Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. |
Yue Tan; Guodong Long; LU LIU; Tianyi Zhou; Qinghua Lu; Jing Jiang; Chengqi Zhang; |
| 1099 | What About Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: From theoretical and empirical lens, we show that generalized value estimates offered by PeVFA may have lower initial approximation error to true values of successive policies, which is expected to improve consecutive value approximation during GPI. Based on above clues, we introduce a new form of GPI with PeVFA which leverages the value generalization along policy improvement path. |
Hongyao Tang; Zhaopeng Meng; Jianye Hao; Chen Chen; Daniel Graves; Dong Li; Changmin Yu; Hangyu Mao; Wulong Liu; Yaodong Yang; Wenyuan Tao; Li Wang; |
| 1100 | Optimal Sampling Gaps for Adaptive Submodular Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we examine the performance loss caused by probability sampling in the context of adaptive submodular maximization. |
Shaojie Tang; Jing Yuan; |
| 1101 | With False Friends Like These, Who Can Notice Mistakes? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we unveil the threat of hypocritical examples—inputs that are originally misclassified yet perturbed by a false friend to force correct predictions. |
Lue Tao; Lei Feng; Jinfeng Yi; Songcan Chen; |
| 1102 | Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we move forward to refine novel-class features by finetuning a trained deep network. |
Ran Tao; Han Zhang; Yutong Zheng; Marios Savvides; |
| 1103 | SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a State-aware Multi-aspect Interests representation Network (SMINet) for cold-start users recommendation at OTPs, which consists of a multi-aspect interests extractor, a co-attention layer, and a state-aware gating layer. |
Wanjie Tao; Yu Li; Liangyue Li; Zulong Chen; Hong Wen; Peilin Chen; Tingting Liang; Quan Lu; |
| 1104 | SplitFed: When Federated Learning Meets Split Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness. |
Chandra Thapa; Pathum Chamikara Mahawaga Arachchige; Seyit Camtepe; Lichao Sun; |
| 1105 | Listwise Learning to Rank Based on Approximate Rank Indicators Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study here a way to approximate information retrieval metrics through a softmax-based approximation of the rank indicator function. |
Thibaut Thonet; Yagmur Gizem Cinar; Eric Gaussier; Minghan Li; Jean-Michel Renders; |
| 1106 | PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a differentially private mechanism called PrivateMail for performing supervised manifold learning. |
Praneeth Vepakomma; Julia Balla; Ramesh Raskar; |
| 1107 | Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel stochastic-control-based approach that generates sequential recourses, that is, recourses that allow x to move stochastically and sequentially across intermediate states to a final state x’. |
Sahil Verma; Keegan Hines; John P. Dickerson; |
| 1108 | Robust Optimal Classification Trees Against Adversarial Examples Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we propose ROCT, a collection of methods to train decision trees that are optimally robust against user-specified attack models. |
Daniël Vos; Sicco Verwer; |
| 1109 | Spline-PINN: Approaching PDEs Without Data Using Fast, Physics-Informed Hermite-Spline CNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose to approach the solution of PDEs based on a novel technique that combines the advantages of two recently emerging machine learning based approaches. |
Nils Wandel; Michael Weinmann; Michael Neidlin; Reinhard Klein; |
| 1110 | Context Uncertainty in Contextual Bandits with Applications to Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, they usually focus solely on item relevance and fail to effectively explore diverse items for users, therefore harming the system performance in the long run. To address this problem, we propose a new type of recurrent neural networks, dubbed recurrent exploration networks (REN), to jointly perform representation learning and effective exploration in the latent space. |
Hao Wang; Yifei Ma; Hao Ding; Yuyang Wang; |
| 1111 | Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We then use it to conduct a novel analysis to obtain a worst-case convergence upper bound for two-level H-SGD with non-IID data, non-convex objective function, and stochastic gradient. By extending this result to the case with random grouping, we observe that this convergence upper bound of H-SGD is between the upper bounds of two single-level local SGD settings, with the number of local iterations equal to the local and global update periods in H-SGD, respectively. |
Jiayi Wang; Shiqiang Wang; Rong-Rong Chen; Mingyue Ji; |
| 1112 | Learngene: From Open-World to Your Learning Task Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Since biological systems can overcome the above difficulties very well, individuals inherit an innate gene from collective creatures that have evolved over hundreds of millions of years and then learn new skills through few examples. Inspired by this, we propose a practical collective-individual paradigm where an evolution (expandable) network is trained on sequential tasks and then recognize unknown classes in real-world. |
Qiu-Feng Wang; Xin Geng; Shu-Xia Lin; Shi-Yu Xia; Lei Qi; Ning Xu; |
| 1113 | Boosting Active Learning Via Improving Test Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Nevertheless, it remains unclear how selected data impacts the test performance of the task model used in AL. In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in a better test performance. |
Tianyang Wang; Xingjian Li; Pengkun Yang; Guosheng Hu; Xiangrui Zeng; Siyu Huang; Cheng-Zhong Xu; Min Xu; |
| 1114 | Efficient Algorithms for General Isotone Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop an efficient, provable convergent algorithm for solving isotone optimization problems. |
Xiwen Wang; Jiaxi Ying; José Vinícius de M. Cardoso; Daniel P. Palomar; |
| 1115 | Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For reducing the contradictory information, we propose a new algorithm, which first learns an interventional Markov equivalence class (I-MEC) before merging multiple graphs. |
Yunxia Wang; Fuyuan Cao; Kui Yu; Jiye Liang; |
| 1116 | Continual Learning Through Retrieval and Imagination Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although simply replaying all previous data or continuously adding the model parameters could alleviate the issue, it is impractical in real-world applications due to the limited available resources. Inspired by the mechanism of the human brain to deepen its past impression, we propose a novel framework, Deep Retrieval and Imagination (DRI), which consists of two components: 1) an embedding network that constructs a unified embedding space without adding model parameters on the arrival of new tasks; and 2) a generative model to produce additional (imaginary) data based on the limited memory. |
Zhen Wang; Liu Liu; Yiqun Duan; Dacheng Tao; |
| 1117 | Max-Min Grouped Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a multi-armed bandit problem termed max-min grouped bandits, in which the arms are arranged in possibly-overlapping groups, and the goal is to find a group whose worst arm has the highest mean reward. |
Zhenlin Wang; Jonathan Scarlett; |
| 1118 | Sample-Efficient Reinforcement Learning Via Conservative Model-Based Actor-Critic Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, learning an accurate model is challenging, especially in complex and noisy environments. To tackle this problem, we propose the conservative model-based actor-critic (CMBAC), a novel approach that achieves high sample efficiency without the strong reliance on accurate learned models. |
Zhihai Wang; Jie Wang; Qi Zhou; Bin Li; Houqiang Li; |
| 1119 | Controlling Underestimation Bias in Reinforcement Learning Via Quasi-median Operation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the Quasi-Median Operation, a novel way to mitigate the underestimation bias by selecting the quasi-median from multiple state-action values. |
Wei Wei; Yujia Zhang; Jiye Liang; Lin Li; Yyuze Li; |
| 1120 | Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We consider the problem of symbolic mathematical integration, as it requires generalizing systematically beyond the training set. |
Sean Welleck; Peter West; Jize Cao; Yejin Choi; |
| 1121 | Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We introduce a fast, low-cost method for creating diverse ensembles of neural networks without needing to train multiple models from scratch. |
Tim Whitaker; Darrell Whitley; |
| 1122 | PluGeN: Multi-Label Conditional Generation from Pre-trained Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Moreover, it is difficult to disentangle selected attributes so that to perform edits of only one attribute while leaving the others unchanged. To overcome these limitations we propose PluGeN (Plugin Generative Network), a simple yet effective generative technique that can be used as a plugin to pre-trained generative models. |
Maciej Wołczyk; Magdalena Proszewska; Łukasz Maziarka; Maciej Zieba; Patryk Wielopolski; Rafał Kurczab; Marek Smieja; |
| 1123 | Structure Learning-Based Task Decomposition for Reinforcement Learning in Non-stationary Environments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we consider an RL-based agent and address the issue of learning via continual interaction with a time-varying dynamic system modeled as a non-stationary Markov decision process (MDP). |
Honguk Woo; Gwangpyo Yoo; Minjong Yoo; |
| 1124 | An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present the learning-to-rank distillation-based COP framework, where a high-performance ranking policy obtained by RL for a COP can be distilled into a non-iterative, simple model, thereby achieving a low-latency COP solver. |
Honguk Woo; Hyunsung Lee; Sangwoo Cho; |
| 1125 | PUMA: Performance Unchanged Model Augmentation for Training Data Removal Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the performance degradation problem, this paper presents a novel approach called Performance Unchanged Model Augmentation (PUMA). |
Ga Wu; Masoud Hashemi; Christopher Srinivasa; |
| 1126 | Generalizing Reinforcement Learning Through Fusing Self-Supervised Learning Into Intrinsic Motivation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to improve the generalization of RL algorithms through fusing Self-supervised learning into Intrinsic Motivation (SIM). |
Keyu Wu; Min Wu; Zhenghua Chen; Yuecong Xu; Xiaoli Li; |
| 1127 | AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a computationally-friendly adaptive learning rate schedule, “AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descent methods. |
Xiaoxia Wu; Yuege Xie; Simon Shaolei Du; Rachel Ward; |
| 1128 | Towards Off-Policy Learning for Ranking Policies with Logged Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new off-policy value ranking (VR) algorithm that can simultaneously maximize user long-term rewards and optimize the ranking metric offline for improved sample efficiency in a unified Expectation-Maximization (EM) framework. |
Teng Xiao; Suhang Wang; |
| 1129 | Active Learning for Domain Adaptation: An Energy-Based Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. |
Binhui Xie; Longhui Yuan; Shuang Li; Chi Harold Liu; Xinjing Cheng; Guoren Wang; |
| 1130 | GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a universal paradigm called GearNet to exploit bilateral relationships between the two domains. |
Renchunzi Xie; Hongxin Wei; Lei Feng; Bo An; |
| 1131 | Reinforcement Learning Augmented Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Since finding the optimal policy is typically intractable, we propose a computationally appealing index policy entitled Occupancy-Measured-Reward Index Policy for the finite-horizon R(MA)^2B. |
Guojun Xiong; Jian Li; Rahul Singh; |
| 1132 | Coordinating Momenta for Cross-Silo Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a new method to improve the training performance in cross-silo FL via maintaining double momentum buffers. |
An Xu; Heng Huang; |
| 1133 | Learning-Augmented Algorithms for Online Steiner Tree Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. |
Chenyang Xu; Benjamin Moseley; |
| 1134 | Constraints Penalized Q-learning for Safe Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a theoretical analysis and demonstrate empirically that our approach can learn robustly across a variety of benchmark control tasks, outperforming several baselines. |
Haoran Xu; Xianyuan Zhan; Xiangyu Zhu; |
| 1135 | Deep Incomplete Multi-View Clustering Via Mining Cluster Complementarity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, (2) the quality of features after fusion might be interfered by the low-quality views, especially the inaccurate imputed views. To avoid these issues, this work presents an imputation-free and fusion-free deep IMVC framework. |
Jie Xu; Chao Li; Yazhou Ren; Liang Peng; Yujie Mo; Xiaoshuang Shi; Xiaofeng Zhu; |
| 1136 | Linearity-Aware Subspace Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To better represent the linear relation of samples, we present a subspace clustering model, Linearity-Aware Subspace Clustering (LASC), which can consciously learn the similarity matrix by employing a linearity-aware metric. |
Yesong Xu; Shuo Chen; Jun Li; Jianjun Qian; |
| 1137 | Go Wider Instead of Deeper Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a parameter-efficient framework, going wider instead of deeper. |
Fuzhao Xue; Ziji Shi; Futao Wei; Yuxuan Lou; Yong Liu; Yang You; |
| 1138 | Seizing Critical Learning Periods in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i.e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy. |
Gang Yan; Hao Wang; Jian Li; |
| 1139 | Learning to Identify Top Elo Ratings: A Dueling Bandits Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, to minimize the number of comparisons and to improve the sample efficiency of the Elo evaluation (for top players), we propose an efficient online match scheduling algorithm. |
Xue Yan; Yali Du; Binxin Ru; Jun Wang; Haifeng Zhang; Xu Chen; |
| 1140 | Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel approach for modeling basketball games using deep reinforcement learning. |
Chen Yanai; Adir Solomon; Gilad Katz; Bracha Shapira; Lior Rokach; |
| 1141 | Training A Resilient Q-network Against Observational Interference Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we consider a deep q-network (DQN) framework training with an auxiliary task of observational interferences such as artificial noises. |
Chao-Han Huck Yang; I-Te Danny Hung; Yi Ouyang; Pin-Yu Chen; |
| 1142 | Policy Optimization with Stochastic Mirror Descent Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes VRMPO algorithm: a sample efficient policy gradient method with stochastic mirror descent. |
Long Yang; Yu Zhang; Gang Zheng; Qian Zheng; Pengfei Li; Jianhang Huang; Gang Pan; |
| 1143 | Graph Pointer Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present Graph Pointer Neural Networks (GPNN) to tackle the challenges mentioned above. |
Tianmeng Yang; Yujing Wang; Zhihan Yue; Yaming Yang; Yunhai Tong; Jing Bai; |
| 1144 | LOGICDEF: An Interpretable Defense Framework Against Adversarial Examples Via Inductive Scene Graph Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we argue that an effective defense should produce an explanation as to why the system is attacked, and by using a representation that is easily readable by a human user, e.g. a logic formalism. |
Yuan Yang; James C Kerce; Faramarz Fekri; |
| 1145 | Exploiting Invariance in Training Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks. |
Chengxi Ye; Xiong Zhou; Tristan McKinney; Yanfeng Liu; Qinggang Zhou; Fedor Zhdanov; |
| 1146 | Lifelong Generative Modelling Using Dynamic Expansion Graph Model Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we study the forgetting behaviour of VAEs using a joint GR and ENA methodology, by deriving an upper bound on the negative marginal log-likelihood. |
Fei Ye; Adrian G. Bors; |
| 1147 | Stage Conscious Attention Network (SCAN): A Demonstration-Conditioned Policy for Few-Shot Imitation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: No previous work can achieve these abilities at the same time. In this work, we conduct FSIL problem under the union of above settings and introduce a novel stage conscious attention network (SCAN) to retrieve knowledge from few demonstrations simultaneously. |
Jia-Fong Yeh; Chi-Ming Chung; Hung-Ting Su; Yi-Ting Chen; Winston H. Hsu; |
| 1148 | BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, as an NP-hard problem, selecting optimal tensor ranks under the desired budget is very challenging and the state-of-the-art studies suffer from unsatisfied compression performance and timing-consuming search procedures. To systematically address this fundamental problem, in this paper we propose BATUDE, a Budget-Aware TUcker DEcomposition-based compression approach that can efficiently calculate optimal tensor ranks via one-shot training. |
Miao Yin; Huy Phan; Xiao Zang; Siyu Liao; Bo Yuan; |
| 1149 | Distributed Randomized Sketching Kernel Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the statistical and computational requirements for distributed kernel ridge regression with randomized sketching (DKRR-RS) and successfully achieve the optimal learning rates with only a fraction of computations. |
Rong Yin; Yong Liu; Dan Meng; |
| 1150 | AutoGCL: Automated Graph Contrastive Learning Via Learnable View Generators Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Most of the existing contrastive learning methods employ pre-defined view generation methods, e.g., node drop or edge perturbation, which usually cannot adapt to input data or preserve the original semantic structures well. To address this issue, we propose a novel framework named Automated Graph Contrastive Learning (AutoGCL) in this paper. |
Yihang Yin; Qingzhong Wang; Siyu Huang; Haoyi Xiong; Xiang Zhang; |
| 1151 | BM-NAS: Bilevel Multimodal Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes Bilevel Multimodal Neural Architecture Search (BM-NAS) framework, which makes the architecture of multimodal fusion models fully searchable via a bilevel searching scheme. |
Yihang Yin; Siyu Huang; Xiang Zhang; |
| 1152 | Early-Bird GCNs: Graph-Network Co-optimization Towards More Efficient GCN Training and Inference Via Drawing Early-Bird Lottery Tickets Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To tackle the aforementioned challenges, we explore the possibility of drawing lottery tickets when sparsifying GCN graphs, i.e., subgraphs that largely shrink the adjacency matrix yet are capable of achieving accuracy comparable to or even better than their full graphs. Specifically, we for the first time discover the existence of graph early-bird (GEB) tickets that emerge at the very early stage when sparsifying GCN graphs, and propose a simple yet effective detector to automatically identify the emergence of such GEB tickets. |
Haoran You; Zhihan Lu; Zijian Zhou; Yonggan Fu; Yingyan Lin; |
| 1153 | Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Backpropagation is not directly applicable, nor are the reparameterization tricks used in networks with continuous stochastic variables. To address this challenge, we present Hindsight Network Credit Assignment (HNCA), a novel gradient estimation algorithm for networks of discrete stochastic units. |
Kenny Young; |
| 1154 | SAIL: Self-Augmented Graph Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL – a novel self-augmented graph contrastive learning framework, with two complementary self-distilling regularization modules, i.e., intra- and inter-graph knowledge distillation. |
Lu Yu; Shichao Pei; Lizhong Ding; Jun Zhou; Longfei Li; Chuxu Zhang; Xiangliang Zhang; |
| 1155 | Natural Black-Box Adversarial Examples Against Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, current methods of crafting adversarial examples only utilize simple pixel space metrics which neglect semantics in the whole images, and thus generate unnatural adversarial examples. To address these problems, we propose an advRL-GAN framework to directly generate semantically natural adversarial examples in the black-box setting, bypassing the transferability requirement of adversarial examples. |
Mengran Yu; Shiliang Sun; |
| 1156 | Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we theoretically reveal the relationship between training data quality and algorithm performance, and analyze the optimal regularization scheme for Lipschitz regularized invariant risk minimization. |
Runpeng Yu; Hong Zhu; Kaican Li; Lanqing Hong; Rui Zhang; Nanyang Ye; Shao-Lun Huang; Xiuqiang He; |
| 1157 | Low-Pass Graph Convolutional Network for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: (2) LCFN follows the general network structure of GCNs, which is suboptimal. To address these issues, we utilize the bipartite graph to define the graph space directly and explore the best network structure based on experiments. |
Wenhui Yu; Zixin Zhang; Zheng Qin; |
| 1158 | MIA-Former: Efficient and Robust Vision Transformers Via Multi-Grained Input-Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In parallel, different images are of varied complexity and their different regions can contain various levels of visual information, e.g., a sky background is not as informative as a foreground object in object classification tasks, indicating that treating those regions equally in terms of model complexity is unnecessary while such opportunities for trimming down ViTs’ complexity have not been fully exploited. To this end, we propose a Multi-grained Input-Adaptive Vision Transformer framework dubbed MIA-Former that can input-adaptively adjust the structure of ViTs at three coarse-to-fine-grained granularities (i.e., model depth and the number of model heads/tokens). |
Zhongzhi Yu; Yonggan Fu; Sicheng Li; Chaojian Li; Yingyan Lin; |
| 1159 | Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It is intriguing to design models that have the similar ability. In this paper, we consider a novel problem of learning compositional scene representations from multiple unspecified viewpoints without using any supervision, and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. |
Jinyang Yuan; Bin Li; Xiangyang Xue; |
| 1160 | TS2Vec: Towards Universal Representation of Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. |
Zhihan Yue; Yujing Wang; Juanyong Duan; Tianmeng Yang; Congrui Huang; Yunhai Tong; Bixiong Xu; |
| 1161 | Fractional Adaptive Linear Units Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work introduces Fractional Adaptive Linear Units (FALUs), a flexible generalization of adaptive activation functions. |
Julio Zamora; Anthony D. Rhodes; Lama Nachman; |
| 1162 | SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Addressing the computational complexity, stringent assumptions and representation collapse challenges in existing work of bisimulation metric, we devise Simple State Representation (SimSR) operator. |
Hongyu Zang; Xin Li; Mingzhong Wang; |
| 1163 | Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing methods have a large sample complexity, slowing down the empirical convergence speed. To address this issue, in this paper, we proposed a novel decentralized stochastic gradient descent method for the nonconvex finite-sum optimization problem, which enjoys a better sample and communication complexity than existing methods. |
Wenkang Zhan; Gang Wu; Hongchang Gao; |
| 1164 | MetaNODE: Prototype Optimization As A Neural ODE for Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we attempt to diminish the prototype bias by regarding it as a prototype optimization problem. |
Baoquan Zhang; Xutao Li; Shanshan Feng; Yunming Ye; Rui Ye; |
| 1165 | State Deviation Correction for Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the state deviation correction (SDC) method to constrain the policy’s induced state distribution by penalizing the out-of-distribution states which might appear during the test period. |
Hongchang Zhang; Jianzhun Shao; Yuhang Jiang; Shuncheng He; Guanwen Zhang; Xiangyang Ji; |
| 1166 | Multi-Agent Reinforcement Learning with General Utilities Via Decentralized Shadow Reward Actor-Critic Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We derive the Decentralized Shadow Reward Actor-Critic (DSAC) in which agents alternate between policy evaluation (critic), weighted averaging with neighbors (information mixing), and local gradient updates for their policy parameters (actor). |
Junyu Zhang; Amrit Singh Bedi; Mengdi Wang; Alec Koppel; |
| 1167 | Co-promotion Predictions of Financing Market and Sales Market: A Cooperative-Competitive Attention Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, for bridgly learning the knowledge interactions between financing market and sales market, we propose a cross-market approach, namely CATN: Cooperative-competitive Attention Transfer Network, which could effectively transfer knowledge of financing capability from the crowdfunding market and sales prospect from the E-commerce market. |
Lei Zhang; Wang Xiang; Chuang Zhao; Hongke Zhao; Rui Li; Runze Wu; |
| 1168 | Categorical Neighbour Correlation Coefficient (CnCor) for Detecting Relationships Between Categorical Variables Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Following the basic idea of the neighbour correlation coefficient (nCor), in this study, we propose a new measure named the categorical nCor (CnCor) to examine the association between categorical variables through using indicator functions to reform the distance metric and product-moment correlation coefficient. |
Lifeng Zhang; Shimo Yang; Hongxun Jiang; |
| 1169 | Interpretable Domain Adaptation for Hidden Subdomain Alignment in The Context of Pre-trained Source Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, this paper introduces a new subdomain combination method that leverages a variable number of subdomains. |
Luxin Zhang; Pascal Germain; Yacine Kessaci; Christophe Biernacki; |
| 1170 | Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we establish explicit convergence rates of policy gradient methods, extending the convergence regime to weakly smooth policy classes with L2 integrable gradient. |
Matthew S. Zhang; Murat A Erdogdu; Animesh Garg; |
| 1171 | Gaussian Process Bandits with Aggregated Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback. |
Mengyan Zhang; Russell Tsuchida; Cheng Soon Ong; |
| 1172 | Rethinking Influence Functions of Neural Networks in The Over-Parameterized Regime Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the classic implicit Hessian-vector product (IHVP) method for calculating IF is fragile, and theoretical analysis of IF in the context of neural networks is still lacking. To this end, we utilize the neural tangent kernel (NTK) theory to calculate IF for the neural network trained with regularized mean-square loss, and prove that the approximation error can be arbitrarily small when the width is sufficiently large for two-layer ReLU networks. |
Rui Zhang; Shihua Zhang; |
| 1173 | A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the recent success of Multi Agent Reinforcement Learning (MARL) in solving complex control problems, we present FedMarl, a federated learning framework that relies on trained MARL agents to perform efficient run-time client selection. |
Sai Qian Zhang; Jieyu Lin; Qi Zhang; |
| 1174 | Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose versaTile multi-modAl learning for multI-labeL emOtion Recognition (TAILOR), aiming to refine multi-modal representations and enhance discriminative capacity of each label. |
Yi Zhang; Mingyuan Chen; Jundong Shen; Chongjun Wang; |
| 1175 | Fusion Multiple Kernel K-means Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the separate base partition generation leads to inadequate negotiation with the clustering procedure and a great loss of beneficial information in corresponding kernel matrices, which negatively affects the clustering performance. To address this issue, we propose a novel algorithm, termed as Fusion Multiple Kernel k-means (FMKKM), which unifies base partition learning and late fusion clustering into one single objective function, and adopts early fusion technique to capture more sufficient information in kernel matrices. |
Yi Zhang; Xinwang Liu; Jiyuan Liu; Sisi Dai; Changwang Zhang; Kai Xu; En Zhu; |
| 1176 | Batch Active Learning with Graph Neural Networks Via Multi-Agent Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time. |
Yuheng Zhang; Hanghang Tong; Yinglong Xia; Yan Zhu; Yuejie Chi; Lei Ying; |
| 1177 | ProtGNN: Towards Self-Explaining Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs. |
Zaixi Zhang; Qi Liu; Hao Wang; Chengqiang Lu; Cheekong Lee; |
| 1178 | Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i.e., the solution obtained by the model has a large gap from the optimal solution. To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptive-hardness, i.e., how difficult a TSP instance can be for a solver. |
Zeyang Zhang; Ziwei Zhang; Xin Wang; Wenwu Zhu; |
| 1179 | Robust Action Gap Increasing with Clipped Advantage Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the method becomes problematic when the optimal action induced by the approximated value function does not agree with the true optimal action. In this paper, we present a novel method, named clipped Advantage Learning (clipped AL), to address this issue. |
Zhe Zhang; Yaozhong Gan; Xiaoyang Tan; |
| 1180 | Online Influence Maximization with Node-Level Feedback Using Standard Offline Oracles Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: So we focus on how to use the standard offline influence maximization oracle which finds the best seed set given the edge parameters as input. In this paper, we resolve these challenges for the famous independent cascade (IC) diffusion model. |
Zhijie Zhang; Wei Chen; Xiaoming Sun; Jialin Zhang; |
| 1181 | CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper, for the first time, proposes a clean-label approach, CLPA, for the poisoning availability attack. |
Bingyin Zhao; Yingjie Lao; |
| 1182 | FedInv: Byzantine-Robust Federated Learning By Inversing Local Model Updates Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Things worsen when many clients are compromised or data among clients are highly non-independent and identically distributed (non-IID). In this work, to address these issues, we propose FedInv, a novel Byzantine-robust FL framework by inversing local model updates. |
Bo Zhao; Peng Sun; Tao Wang; Keyu Jiang; |
| 1183 | Well-Classified Examples Are Underestimated in Classification with Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, we theoretically show that this common practice hinders representation learning, energy optimization, and margin growth. To counteract this deficiency, we propose to reward well-classified examples with additive bonuses to revive their contribution to the learning process. |
Guangxiang Zhao; Wenkai Yang; Xuancheng Ren; Lei Li; Yunfang Wu; Xu Sun; |
| 1184 | Error-Based Knockoffs Inference for Controlled Feature Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the procedure of model-X knockoffs depends heavily on the coefficient-based feature importance and only concerns the control of false discovery rate (FDR). To further improve its adaptivity and flexibility, in this paper, we propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. |
Xuebin Zhao; Hong Chen; Yingjie Wang; Weifu Li; Tieliang Gong; Yulong Wang; Feng Zheng; |
| 1185 | Online Missing Value Imputation and Change Point Detection with The Gaussian Copula Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we develop a new online imputation algorithm for mixed data using the Gaussian copula. |
Yuxuan Zhao; Eric Landgrebe; Eliot Shekhtman; Madeleine Udell; |
| 1186 | LaSSL: Label-Guided Self-Training for Semi-supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we emphasize the cruciality of the label information and propose a Label-guided Self-training approach to Semi-supervised Learning (LaSSL), which improves pseudo-label generations from two mutually boosted strategies. |
Zhen Zhao; Luping Zhou; Lei Wang; Yinghuan Shi; Yang Gao; |
| 1187 | Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We adopt this viewpoint and model the actor and critic interaction as a two-player general-sum game with a leader-follower structure known as a Stackelberg game. Given this abstraction, we propose a meta-framework for Stackelberg actor-critic algorithms where the leader player follows the total derivative of its objective instead of the usual individual gradient. |
Liyuan Zheng; Tanner Fiez; Zane Alumbaugh; Benjamin Chasnov; Lillian J. Ratliff; |
| 1188 | Adaptive Pairwise Weights for Temporal Credit Assignment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: One of the earliest and still most widely used heuristics is to assign this credit based on a scalar coefficient, lambda (treated as a hyperparameter), raised to the power of the time interval between the state-action and the reward. In this empirical paper, we explore heuristics based on more general pairwise weightings that are functions of the state in which the action was taken, the state at the time of the reward, as well as the time interval between the two. |
Zeyu Zheng; Risto Vuorio; Richard Lewis; Satinder Singh; |
| 1189 | Programmatic Reward Design By Example Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the idea of programmatic reward design, i.e. using programs to specify the reward functions in RL environments. |
Weichao Zhou; Wenchao Li; |
| 1190 | Neural Piecewise-Constant Delay Differential Equations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this article, we introduce a new sort of continuous-depth neural network, called the Neural Piecewise-Constant Delay Differential Equations (PCDDEs). |
Qunxi Zhu; Yifei Shen; Dongsheng Li; Wei Lin; |
| 1191 | Structural Landmarking and Interaction Modelling: A “SLIM” Network for Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, we speculate that preserving the interacting relation between parts, instead of pooling them together, could benefit system level prediction. To verify this, we propose SLIM, a graph neural network model for Structural Landmarking and Interaction Modelling. |
Yaokang Zhu; Kai Zhang; Jun Wang; Haibin Ling; Jie Zhang; Hongyuan Zha; |
| 1192 | Invariant Action Effect Model for Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose Invariant Action Effect Model (IAEM) to capture the invariance in action effects, where the effect of an action is represented as the residual of representations from neighboring states. |
Zheng-Mao Zhu; Shengyi Jiang; Yu-Ren Liu; Yang Yu; Kun Zhang; |
| 1193 | Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-optimal Demonstrations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address a practical scenario, in this work, we propose Self-Adaptive Imitation Learning (SAIL), which, provided with a few demonstrations from a sub-optimal teacher, can perform well in RL tasks with extremely delayed rewards, where the only reward feedback is trajectory-wise ranking. |
Zhuangdi Zhu; Kaixiang Lin; Bo Dai; Jiayu Zhou; |
| 1194 | Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Still, in many real-world settings, there exist simplified underlying dynamics that can be leveraged for more scalable solutions. In this work, we exploit such locality structures effectively whilst maintaining global cooperation. |
Roy Zohar; Shie Mannor; Guy Tennenholtz; |
| 1195 | Hedonic Games with Fixed-Size Coalitions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we initiate the study of hedonic games with fixed-size coalitions, where the set of possible coalition structures is restricted as follows: there are k coalitions, each coalition has a fixed size, and the sum of the sizes of all coalitions equals n. |
Vittorio Bilò; Gianpiero Monaco; Luca Moscardelli; |
| 1196 | Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose and analyze a decentralized Multi-Armed Bandit (MAB) problem with coupled rewards as an abstraction of more general multi-agent collaboration. |
Erdem Biyik; Anusha Lalitha; Rajarshi Saha; Andrea Goldsmith; Dorsa Sadigh; |
| 1197 | Fixation Maximization in The Positional Moran Process Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we introduce the positional Moran process, a natural generalization in which the mutant fitness advantage is only realized on specific nodes called active nodes, and study the problem of fixation maximization: given a budget k, choose a set of k active nodes that maximize the fixation probability of the invading mutant. |
Joachim Brendborg; Panagiotis Karras; Andreas Pavlogiannis; Asger Ullersted Rasmussen; Josef Tkadlec; |
| 1198 | Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the costs of many paths are often much smaller than w times their minimum path costs, meaning that the sum of path costs is much smaller than w C*. In this paper, we therefore propose Flexible EECBS (FEECBS), which uses a flex(ible) distribution of the path costs (that relaxes the requirement to find bounded-suboptimal paths on the low level) in order to reduce the number of collisions that need to be resolved on the high level while still guaranteeing to solve MAPF bounded suboptimally. |
Shao-Hung Chan; Jiaoyang Li; Graeme Gange; Daniel Harabor; Peter J. Stuckey; Sven Koenig; |
| 1199 | Participatory Budgeting with Donations and Diversity Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Participatory budgeting (PB) is a democratic process where citizens jointly decide on how to allocate public funds to indivisible projects. In this work, we focus on PB processes where citizens may provide additional money to projects they want to see funded. |
Jiehua Chen; Martin Lackner; Jan Maly; |
| 1200 | Pretrained Cost Model for Distributed Constraint Optimization Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address the generality issue, we propose a novel directed acyclic graph representation schema for DCOPs and leverage the Graph Attention Networks (GATs) to embed graph representations. |
Yanchen Deng; Shufeng Kong; Bo An; |
| 1201 | Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a concentration network called ConcNet. |
Qingxu Fu; Tenghai Qiu; Jianqiang Yi; Zhiqiang Pu; Shiguang Wu; |
| 1202 | Cooperative Multi-Agent Fairness and Equivariant Policies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address fairness in multi-agent contexts, we introduce team fairness, a group-based fairness measure for multi-agent learning. |
Niko A. Grupen; Bart Selman; Daniel D. Lee; |
| 1203 | Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. |
Mingyu Guo; Jialiang Li; Aneta Neumann; Frank Neumann; Hung Nguyen; |
| 1204 | Anytime Multi-Agent Path Finding Via Machine Learning-Guided Large Neighborhood Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It generates the subsets of agents for replanning by randomized destroy heuristics, but not all of them increase the solution quality substantially. We propose to use machine learning to learn how to select a subset of agents from a collection of subsets, such that replanning increases the solution quality more. |
Taoan Huang; Jiaoyang Li; Sven Koenig; Bistra Dilkina; |
| 1205 | MDPGT: Momentum-Based Decentralized Policy Gradient Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations. |
Zhanhong Jiang; Xian Yeow Lee; Sin Yong Tan; Kai Liang Tan; Aditya Balu; Young M Lee; Chinmay Hegde; Soumik Sarkar; |
| 1206 | Shard Systems: Scalable, Robust and Persistent Multi-Agent Path Finding with Performance Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While many MAPF solvers that provide some of these properties exist, none provides them all. To fill this need, we propose a new MAPF framework, the shard system. |
Christopher Leet; Jiaoyang Li; Sven Koenig; |
| 1207 | A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we show that state-based critics can introduce bias in the policy gradient estimates, potentially undermining the asymptotic guarantees of the algorithm. |
Xueguang Lyu; Andrea Baisero; Yuchen Xiao; Christopher Amato; |
| 1208 | When Can The Defender Effectively Deceive Attackers in Security Games? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we consider three different attacker strategies of response (to the defender’s deception) with increasing sophistication, and design efficient polynomial-time algorithms to compute the equilibrium for each. |
Thanh Nguyen; Haifeng Xu; |
| 1209 | Generalization in Mean Field Games By Learning Master Policies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In reference to the Master equation in MFGs, we coin the term “Master policies” to describe them and we prove that a single Master policy provides a Nash equilibrium, whatever the initial distribution. We propose a method to learn such Master policies. |
Sarah Perrin; Mathieu Laurière; Julien Pérolat; Romuald Élie; Matthieu Geist; Olivier Pietquin; |
| 1210 | Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems: Complexity, Special Case Algorithms and Heuristics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For solving the problem on larger networks, we propose a general heuristic framework along with greedy selection methods. |
Zirou Qiu; Chen Chen; Madhav Marathe; S.S. Ravi; Daniel J. Rosenkrantz; Richard Stearns; Anil Vullikanti; |
| 1211 | How Many Representatives Do We Need? The Optimal Size of A Congress Voting on Binary Issues Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work investigates the optimal number of agents needed to decide on a binary issue under majority rule. |
Manon Revel; Tao Lin; Daniel Halpern; |
| 1212 | Decentralized Mean Field Games Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, almost all previous methods in this area make a strong assumption of a centralized system where all the agents in the environment learn the same policy and are effectively indistinguishable from each other. In this paper, we relax this assumption about indistinguishable agents and propose a new mean field system known as Decentralized Mean Field Games, where each agent can be quite different from others. |
Sriram Ganapathi Subramanian; Matthew E. Taylor; Mark Crowley; Pascal Poupart; |
| 1213 | Incentivizing Collaboration in Machine Learning Via Synthetic Data Rewards Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e.g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions. Distributing synthetic data as rewards (instead of trained models or money) offers task- and model-agnostic benefits for downstream learning tasks and is less likely to violate data privacy regulation. |
Sebastian Shenghong Tay; Xinyi Xu; Chuan Sheng Foo; Bryan Kian Hsiang Low; |
| 1214 | Learning The Optimal Recommendation from Explorative Users Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new problem setting to study the sequential interactions between a recommender system and a user. |
Fan Yao; Chuanhao Li; Denis Nekipelov; Hongning Wang; Haifeng Xu; |
| 1215 | Multi-Agent Incentive Communication Via Decentralized Teammate Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, it enlarges agents’ local policy spaces and increases learning complexity, leading to poor coordination in complex settings. To handle this limitation, this paper proposes a novel framework named Multi-Agent Incentive Communication (MAIC) that allows each agent to learn to generate incentive messages and bias other agents’ value functions directly, resulting in effective explicit coordination. |
Lei Yuan; Jianhao Wang; Fuxiang Zhang; Chenghe Wang; ZongZhang Zhang; Yang Yu; Chongjie Zhang; |
| 1216 | MLink: Linking Black-Box Models for Collaborative Multi-Model Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study underlying relationships among black-box ML models and propose a novel learning task: model linking. |
Mu Yuan; Lan Zhang; Xiang-Yang Li; |
| 1217 | Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We extend the classic regret minimization framework for approximating equilibria in normal-form games by greedily weighing iterates based on regrets observed at runtime. |
Hugh Zhang; Adam Lerer; Noam Brown; |
| 1218 | Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We refer to this as introduced unfairness, and investigate the conditions under which it may arise. To this end, we propose introduced total variation as a measure of introduced unfairness, and establish graphical conditions under which it may be incentivised to occur. |
Carolyn Ashurst; Ryan Carey; Silvia Chiappa; Tom Everitt; |
| 1219 | Incorporating Item Frequency for Differentially Private Set Union Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose incorporating the item frequency, which is typically available in set union problems, to boost the utility of private mechanisms. |
Ricardo Silva Carvalho; Ke Wang; Lovedeep Singh Gondara; |
| 1220 | Cosine Model Watermarking Against Ensemble Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on tackling the challenging task of defending against ensemble distillation. |
Laurent Charette; Lingyang Chu; Yizhou Chen; Jian Pei; Lanjun Wang; Yong Zhang; |
| 1221 | Towards Debiasing DNN Models from Spurious Feature Influence Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We observe that algorithmic discrimination can be explained by the high reliance of the models on fairness sensitive features. Motivated by this observation, we propose to achieve fairness by suppressing the DNN models from capturing the spurious correlation between those fairness sensitive features with the underlying task. |
Mengnan Du; Ruixiang Tang; Weijie Fu; Xia Hu; |
| 1222 | Path-Specific Objectives for Safer Agent Incentives Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a general framework for training safe agents whose naive incentives are unsafe. |
Sebastian Farquhar; Ryan Carey; Tom Everitt; |
| 1223 | Algorithmic Fairness Verification with Graphical Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an efficient fairness verifier, called FVGM, that encodes the correlations among features as a Bayesian network. |
Bishwamittra Ghosh; Debabrota Basu; Kuldeep S Meel; |
| 1224 | Achieving Long-Term Fairness in Sequential Decision Making Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a framework for achieving long-term fair sequential decision making. |
Yaowei Hu; Lu Zhang; |
| 1225 | Fairness Without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate the fairness concerns of training a machine learning model using data with missing values. |
Haewon Jeong; Hao Wang; Flavio P. Calmon; |
| 1226 | Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we show that neural SDEs with adaptive attribution-driven noise lead to even more robust attributions and smaller sensitivity metrics than traditional neural SDEs with Brownian motion as noise. |
Sumit Kumar Jha; Rickard Ewetz; Alvaro Velasquez; Arvind Ramanathan; Susmit Jha; |
| 1227 | Certified Robustness of Nearest Neighbors Against Data Poisoning and Backdoor Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we show that the intrinsic majority vote mechanisms in kNN and rNN already provide certified robustness guarantees against data poisoning attacks and backdoor attacks. |
Jinyuan Jia; Yupei Liu; Xiaoyu Cao; Neil Zhenqiang Gong; |
| 1228 | On The Fairness of Causal Algorithmic Recourse Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose two new fair-ness criteria at the group and individual level, which—unlike prior work on equalising the average group-wise distance from the decision boundary—explicitly account for causal relationships between features, thereby capturing downstream effects of recourse actions performed in the physical world. |
Julius von Kügelgen; Amir-Hossein Karimi; Umang Bhatt; Isabel Valera; Adrian Weller; Bernhard Schölkopf; |
| 1229 | DeepAuth: A DNN Authentication Framework By Model-Unique and Fragile Signature Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a novel DNN authentication framework DeepAuth that embeds a unique and fragile signature to each protected DNN model. |
Yingjie Lao; Weijie Zhao; Peng Yang; Ping Li; |
| 1230 | Fast Sparse Decision Tree Optimization Via Reference Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. |
Hayden McTavish; Chudi Zhong; Reto Achermann; Ilias Karimalis; Jacques Chen; Cynthia Rudin; Margo Seltzer; |
| 1231 | Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We aim to explain a black-box classifier with the form: "data X is classified as class Y because X has A, B and does not have C" in which A, B, and C are high-level concepts. |
Thien Q Tran; Kazuto Fukuchi; Youhei Akimoto; Jun Sakuma; |
| 1232 | Do Feature Attribution Methods Correctly Attribute Features? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: These methods compute the attribution of each input feature to represent its importance, but there is no consensus on the definition of "attribution", leading to many competing methods with little systematic evaluation, complicated in particular by the lack of ground truth attribution. To address this, we propose a dataset modification procedure to induce such ground truth. |
Yilun Zhou; Serena Booth; Marco Tulio Ribeiro; Julie Shah; |
| 1233 | Formal Semantics and Formally Verified Validation for Temporal Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a simple and concise semantics for temporal planning. |
Mohammad Abdulaziz; Lukas Koller; |
| 1234 | Goal Recognition As Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we develop a framework that combines model-free reinforcement learning and goal recognition to alleviate the need for careful, manual domain design, and the need for costly online executions. |
Leonardo Amado; Reuth Mirsky; Felipe Meneguzzi; |
| 1235 | Online Search with Best-Price and Query-Based Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we study learning-augmented algorithms, in which there is a potentially erroneous prediction concerning the input. |
Spyros Angelopoulos; Shahin Kamali; Dehou Zhang; |
| 1236 | Extended Goal Recognition Design with First-Order Computation Tree Logic Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The paper presents a new GRD framework called extended goal recognition design (EGRD) for goal recognition that involves multiple goals. |
Tsz-Chiu Au; |
| 1237 | Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel planning method that does not rely on any explicit representation of the noise distributions. |
Thom S. Badings; Alessandro Abate; Nils Jansen; David Parker; Hasan A. Poonawala; Marielle Stoelinga; |
| 1238 | Synthesis from Satisficing and Temporal Goals Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This work extends the existing satisficing approach, presenting the first sound algorithm for synthesis from LTL and DS rewards with fractional discount factors. |
Suguman Bansal; Lydia Kavraki; Moshe Y. Vardi; Andrew Wells; |
| 1239 | Making Translations to Classical Planning Competitive with Other HTN Planners Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present new translation techniques both for the special case of totally-ordered HTNs as well as for the general partially-ordered case. |
Gregor Behnke; Florian Pollitt; Daniel Höller; Pascal Bercher; Ron Alford; |
| 1240 | PlanVerb: Domain-Independent Verbalization and Summary of Task Plans Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose PlanVerb, a domain and planner-independent method for the verbalization of task plans. |
Gerard Canal; Senka Krivić; Paul Luff; Andrew Coles; |
| 1241 | Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: That said, the agent should know what the competitor will likely do and then generate its plan accordingly. In this paper we propose a novel approach for estimating strategies of the adversary (or the competitor), sampling its actions that might hinder agent’s goals by interfering with the agent’s actions. |
Lukáš Chrpa; Pavel Rytíř; Rostislav Horčík; Stefan Edelkamp; |
| 1242 | The FF Heuristic for Lifted Classical Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recent work introduced the idea of using Datalog programs to compute the additive heuristic over lifted tasks. |
Augusto B. Corrêa; Florian Pommerening; Malte Helmert; Guillem Francès; |
| 1243 | Inconsistent Planning: When in Doubt, Toss A Coin! Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As we prove in this paper, the cost of irrationality is highly susceptible to the agent’s choices when faced with a few possible actions of equal estimated costs. |
Yuriy Dementiev; Fedor Fomin; Artur Ignatiev; |
| 1244 | Robustification of Online Graph Exploration Methods Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm and significantly outperforms any known online algorithm if the prediction is of high accuracy while maintaining good guarantees when the prediction is of poor quality. |
Franziska Eberle; Alexander Lindermayr; Nicole Megow; Lukas Nölke; Jens Schlöter; |
| 1245 | Explainable Planner Selection for Classical Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks. |
Patrick Ferber; Jendrik Seipp; |
| 1246 | Operator-Potential Heuristics for Symbolic Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Indeed, progress using heuristic functions in symbolic search has been limited and even very informed heuristics have been shown to be detrimental. Here we show how this connection can be made stronger for LP-based potential heuristics. |
Daniel Fišer; Álvaro Torralba; Jörg Hoffmann; |
| 1247 | Reconfiguring Shortest Paths in Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For (b), (c) and (d), we present efficient algorithms to solve the respective problems. |
Kshitij Gajjar; Agastya Vibhuti Jha; Manish Kumar; Abhiruk Lahiri; |
| 1248 | Homomorphisms of Lifted Planning Tasks: The Case for Delete-Free Relaxation Heuristics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel method for computing lifted (admissible) delete-free relaxed heuristics via grounding of the smaller task and computing the (admissible) delete-free relaxed heuristics there. |
Rostislav Horčík; Daniel Fišer; Álvaro Torralba; |
| 1249 | Speeding Up The RUL¯ Dynamic-Controllability-Checking Algorithm for Simple Temporal Networks with Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: A Simple Temporal Network with Uncertainty (STNU) includes real-valued variables, called time-points; binary difference constraints on those time-points; and contingent links that … |
Luke Hunsberger; Roberto Posenato; |
| 1250 | Learning to Solve Routing Problems Via Distributionally Robust Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group DRO) to tackle this issue, where we jointly optimize the weights for different groups of distributions and the parameters for the deep model in an interleaved manner during training. |
Yuan Jiang; Yaoxin Wu; Zhiguang Cao; Jie Zhang; |
| 1251 | Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As input, we are given a set of previously executed trajectories, and the main challenge is to learn an action model that has a similar goal achievement probability to the policies used to create these trajectories. To this end, we introduce a variant of PPDDL in which there is uncertainty about the transition probabilities, specified by an interval for each factor that contains the respective true transition probabilities. |
Brendan Juba; Roni Stern; |
| 1252 | Bounding Quality in Diverse Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to extend the portfolio of planners for various computational problems in diverse planning. |
Michael Katz; Shirin Sohrabi; Octavian Udrea; |
| 1253 | A* Search and Bound-Sensitive Heuristics for Oversubscription Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We also introduce novel bound-sensitive heuristics, which are able to reason about the primary cost of a solution while taking into account secondary cost functions and bounds, to provide superior guidance compared to heuristics that do not take these bounds into account. We propose two such bound-sensitive variants of existing classical planning heuristics, and show experimentally that the resulting search is significantly more informed than with comparable heuristics that do not consider bounds. |
Michael Katz; Emil Keyder; |
| 1254 | NICE: Robust Scheduling Through Reinforcement Learning-Guided Integer Programming Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present NICE (Neural network IP Coefficient Extraction), a novel technique that combines reinforcement learning and integer programming to tackle the problem of robust scheduling. |
Luke Kenworthy; Siddharth Nayak; Christopher Chin; Hamsa Balakrishnan; |
| 1255 | Planning to Avoid Side Effects Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we investigate how to avoid side effects in a symbolic planning setting. |
Toryn Q. Klassen; Sheila A. McIlraith; Christian Muise; Jarvis Xu; |
| 1256 | Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we revisit the overall model-based DRP objective and instead take a minorization-maximization perspective to iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective. |
Siow Meng Low; Akshat Kumar; Scott Sanner; |
| 1257 | Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It is challenging to design an efficient search mechanism in the large search space in form of arbitrary LTLf formulae while alleviating the wrong search bias resulting from noisy data. In this paper, we tackle this problem by bridging LTLf inference to GNN inference. |
Weilin Luo; Pingjia Liang; Jianfeng Du; Hai Wan; Bo Peng; Delong Zhang; |
| 1258 | Risk-Aware Stochastic Shortest Path Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. |
Tobias Meggendorfer; |
| 1259 | Differential Assessment of Black-Box AI Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a novel approach to differentially assess black-box AI agents that have drifted from their previously known models. |
Rashmeet Kaur Nayyar; Pulkit Verma; Siddharth Srivastava; |
| 1260 | Solving Disjunctive Temporal Networks with Uncertainty Under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a tree search approach to determine whether or not a DTNU is R-TDC. |
Kevin Osanlou; Jeremy Frank; Andrei Bursuc; Tristan Cazenave; Eric Jacopin; Christophe Guettier; J. Benton; |
| 1261 | Deciding Unsolvability in Temporal Planning Under Action Non-Self-Overlapping Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present sound and complete decision procedures that address the dual problem of proving that no plan exists, which has important applications in oversubscription, model validation and optimization. |
Stefan Panjkovic; Andrea Micheli; Alessandro Cimatti; |
| 1262 | A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing methods typically do not take risk into account when optimizing in stochastic domains, which can be incorporated efficiently in MDPs by optimizing a nonlinear utility function of the return distribution. We bridge this gap by introducing Risk-Aware Planning using PyTorch (RAPTOR), a novel unified framework for risk-sensitive planning through end-to-end optimization of commonly-studied risk-sensitive utility functions such as entropic utility, mean-variance optimization and CVaR. |
Noah Patton; Jihwan Jeong; Mike Gimelfarb; Scott Sanner; |
| 1263 | Formula Synthesis in Propositional Dynamic Logic with Shuffle Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the formula-synthesis problem for Propositional Dynamic Logic with Shuffle (PDL || ). |
Sophie Pinchinat; Sasha Rubin; François Schwarzentruber; |
| 1264 | Efficient Encoding of Cost Optimal Delete-Free Planning As SAT Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a novel method for encoding cost optimal delete-free STRIPS Planning as SAT. |
Masood Feyzbakhsh Rankooh; Jussi Rintanen; |
| 1265 | Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates Via State Abstraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To improve the scalability of our approach to a greater number of servers and task classes, we present an approximation based on state abstraction. |
Marc Rigter; Danial Dervovic; Parisa Hassanzadeh; Jason Long; Parisa Zehtabi; Daniele Magazzeni; |
| 1266 | Enhancing Column Generation By A Machine-Learning-Based Pricing Heuristic for Graph Coloring Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The new columns are generated as needed by repeatedly solving a pricing problem, which is often NP-hard and is a bottleneck of the CG approach. To tackle this, we propose a Machine-Learning-based Pricing Heuristic (MLPH) that can generate many high-quality columns efficiently. |
Yunzhuang Shen; Yuan Sun; Xiaodong Li; Andrew Eberhard; Andreas Ernst; |
| 1267 | Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: It is aided in performing these tasks by a Graph neural network that evaluates the value function and action probabilities for each state. Along with this, we propose a new method of adding mutex-lock like variables in our state representation which helps factor in the parallelization of the scheduled operations, thereby pruning the depth of the output circuit. |
Animesh Sinha; Utkarsh Azad; Harjinder Singh; |
| 1268 | Classical Planning with Avoid Conditions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: – the avoid condition – that must be false throughout the plan. We design techniques tackling such avoid conditions effectively. |
Marcel Steinmetz; Jörg Hoffmann; Alisa Kovtunova; Stefan Borgwardt; |
| 1269 | Stochastic Goal Recognition Design Problems with Suboptimal Agents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents the Suboptimal Stochastic GRD model, where we consider boundedly rational agents that, due to limited resources, might follow a suboptimal policy. |
Christabel Wayllace; William Yeoh; |
| 1270 | Equity Promotion in Online Resource Allocation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio. We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. |
Pan Xu; Yifan Xu; |
| 1271 | Efficient Device Scheduling with Multi-Job Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel multi-job FL framework to enable the parallel training process of multiple jobs. |
Chendi Zhou; Ji Liu; Juncheng Jia; Jingbo Zhou; Yang Zhou; Huaiyu Dai; Dejing Dou; |
| 1272 | MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel multi-agent reinforcement learning based framework to solve the cooperative PDP (MAPDP). |
Zefang Zong; Meng Zheng; Yong Li; Depeng Jin; |
| 1273 | Entropy Estimation Via Normalizing Flow Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we propose a transformbased method for high dimensional entropy estimation, which consists of the following two main ingredients. |
Ziqiao Ao; Jinglai Li; |
| 1274 | Fast and More Powerful Selective Inference for Sparse High-Order Interaction Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduced an efficient pruning strategy and demonstrated the computational efficiency and statistical power of the proposed method using both synthetic and real data. |
Diptesh Das; Vo Nguyen Le Duy; Hiroyuki Hanada; Koji Tsuda; Ichiro Takeuchi; |
| 1275 | Generalized Stochastic Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we generalize the recently studied stochastic matching problem to more accurately model a significant medical process, kidney exchange, and several other applications. |
Alireza Farhadi; Jacob Gilbert; MohammadTaghi Hajiaghayi; |
| 1276 | Robust Tests in Online Decision-Making Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, misspecification is frequent in practice due to incorrect functional form or missing covariates. In this work, we propose a modified actor-critic algorithm which is robust to critic misspecification and derive a novel testing procedure for the actor parameters in this case. |
Gi-Soo Kim; Jane P Kim; Hyun-Joon Yang; |
| 1277 | Local Differential Privacy for Belief Functions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose two new definitions of local differential privacy for belief functions. |
Qiyu Li; Chunlai Zhou; Biao Qin; Zhiqiang Xu; |
| 1278 | A Complete Criterion for Value of Information in Soluble Influence Diagrams Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Along the way, we establish two techniques for proving properties of multi-decision influence diagrams: ID homomorphisms are structure-preserving transformations of influence diagrams, while a Tree of Systems is a collection of paths that captures how information and control can flow in an influence diagram. |
Chris van Merwijk; Ryan Carey; Tom Everitt; |
| 1279 | Training-Free Uncertainty Estimation for Dense Regression: Sensitivity As A Surrogate Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose three simple and scalable methods to analyze the variance of outputs from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. |
Lu Mi; Hao Wang; Yonglong Tian; Hao He; Nir N Shavit; |
| 1280 | On The Impact of Spurious Correlation for Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a new formalization and model the data shifts by taking into account both the invariant and environmental (spurious) features. |
Yifei Ming; Hang Yin; Yixuan Li; |
| 1281 | Inference and Learning with Model Uncertainty in Probabilistic Logic Programs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce BetaProbLog, a PLP language that can model epistemic uncertainty. |
Victor Verreet; Vincent Derkinderen; Pedro Zuidberg Dos Martires; Luc De Raedt; |
| 1282 | Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Solutions to this problem have applications to the scalable generation of combinatorial structures, as well as sampling in several statistical-relational models such as Markov logic networks and probabilistic logic programs. In this paper, we identify certain classes of sentences that are domain-liftable under sampling, in the sense that they admit a sampling algorithm that runs in time polynomial in n. |
Yuanhong Wang; Timothy van Bremen; Yuyi Wang; Ondřej Kuželka; |
| 1283 | Identifiability of Linear AMP Chain Graph Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: AMP models are described by DAGs on chain components which themselves are undirected graphs. |
Yuhao Wang; Arnab Bhattacharyya; |
| 1284 | DeepStochLog: Neural Stochastic Logic Programming Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose DeepStochLog, an alternative neural-symbolic framework based on stochastic definite clause grammars, a kind of stochastic logic program. |
Thomas Winters; Giuseppe Marra; Robin Manhaeve; Luc De Raedt; |
| 1285 | Towards Robust Off-Policy Learning for Runtime Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We theoretically justify the robustness of our methods to runtime uncertainty, and demonstrate their effectiveness using both the simulation and the real-world online experiments. |
Da Xu; Yuting Ye; Chuanwei Ruan; Bo Yang; |
| 1286 | Improving Bayesian Neural Networks By Adversarial Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we argue that the randomness of sampling in Bayesian neural networks causes errors in the updating of model parameters during training and some sampled models with poor performance in testing. |
Jiaru Zhang; Yang Hua; Tao Song; Hao Wang; Zhengui Xue; Ruhui Ma; Haibing Guan; |
| 1287 | Efficient Optimal Transport Algorithm By Accelerated Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel algorithm to further improve the efficiency and accuracy based on Nesterov’s smoothing technique. |
Dongsheng An; Na Lei; Xiaoyin Xu; Xianfeng Gu; |
| 1288 | Local and Global Linear Convergence of General Low-Rank Matrix Recovery Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the convergence rate of gradient-based local search methods for solving low-rank matrix recovery problems with general objectives in both symmetric and asymmetric cases, under the assumption of the restricted isometry property. |
Yingjie Bi; Haixiang Zhang; Javad Lavaei; |
| 1289 | A*+BFHS: A Hybrid Heuristic Search Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a new algorithm called A*+BFHS for solving problems with unit-cost operators where A* and IDA* fail due to memory limitations and/or the existence of many distinct paths between the same pair of nodes. |
Zhaoxing Bu; Richard E. Korf; |
| 1290 | NukCP: An Improved Local Search Algorithm for Maximum K-Club Problem Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For solving instances with different scales, this paper develops an efficient local search algorithm named NukCP for the MkCP which mainly includes two novel ideas. |
Jiejiang Chen; Yiyuan Wang; Shaowei Cai; Minghao Yin; Yupeng Zhou; Jieyu Wu; |
| 1291 | Fourier Representations for Black-Box Optimization Over Categorical Variables Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Standalone search algorithms, such as simulated annealing (SA) and Monte Carlo tree search (MCTS), are typically used for such optimization problems. In order to improve the performance and sample efficiency of such algorithms, we propose to use existing methods in conjunction with a surrogate model for the black-box evaluations over purely categorical variables. |
Hamid Dadkhahi; Jesus Rios; Karthikeyan Shanmugam; Payel Das; |
| 1292 | New Results in Bounded-Suboptimal Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we investigate several new algorithms for bounded-suboptimal search, including novel variants of EES and DPS, the two most prominent previous proposals, and methods inspired by recent work in bounded-cost search that leverages uncertainty estimates of the heuristic. |
Maximilian Fickert; Tianyi Gu; Wheeler Ruml; |
| 1293 | An Exact Algorithm with New Upper Bounds for The Maximum K-Defective Clique Problem in Massive Sparse Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel branch-and-bound algorithm to solve the MDCP based on several new techniques. |
Jian Gao; Zhenghang Xu; Ruizhi Li; Minghao Yin; |
| 1294 | Learning from Mistakes – A Framework for Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Learning from one’s mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In this paper, we investigate if this human learning strategy can be applied in machine learning. |
Bhanu Garg; Li Zhang; Pradyumna Sridhara; Ramtin Hosseini; Eric Xing; Pengtao Xie; |
| 1295 | The Complexity of Temporal Vertex Cover in Small-Degree Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we initiate a systematic study of the complexity of TVC and Delta-TVC on sparse graphs. |
Thekla Hamm; Nina Klobas; George B. Mertzios; Paul G. Spirakis; |
| 1296 | Provable Sensor Sets for Epidemic Detection Over Networks with Minimum Delay Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present the algorithm RoundSensor, which gives a rigorous worst case O(log(n))-factor for the detection time, while violating the budget by a factor of O(log^2(n)). |
Jack Heavey; Jiaming Cui; Chen Chen; B. Aditya Prakash; Anil Vullikanti; |
| 1297 | Towards Automated Discovery of God-Like Folk Algorithms for Rubik’s Cube Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a multi-objective meta-search procedure that constructs candidate algorithms for state-space search puzzles like Rubik’s cube. |
Garrett E. Katz; Naveed Tahir; |
| 1298 | MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-crafted heuristics, largely ignoring common patterns within a given instance distribution of the problem of interest. Here, we propose MIP-GNN, a general framework for enhancing such solvers with data-driven insights. |
Elias B. Khalil; Christopher Morris; Andrea Lodi; |
| 1299 | Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present the BLDS algorithm for optimized algorithm selection (ML operations) in a fixed ML pipeline structure. |
Akihiro Kishimoto; Djallel Bouneffouf; Radu Marinescu; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito Palmes; Adi Botea; |
| 1300 | PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Examples of such problems include: i)targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is under performing, and ii)guided summarization, where data (e.g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent. Motivated by such applications, we present PRISM, a rich class of PaRameterIzed Submodular information Measures. |
Suraj Kothawade; Vishal Kaushal; Ganesh Ramakrishnan; Jeff Bilmes; Rishabh Iyer; |
| 1301 | Split Moves for Monte-Carlo Tree Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents’ strength. |
Jakub Kowalski; Maksymilian Mika; Wojciech Pawlik; Jakub Sutowicz; Marek Szykuła; Mark H. M. Winands; |
| 1302 | MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding Via Large Neighborhood Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel algorithm MAPF-LNS2 based on large neighborhood search for solving MAPF efficiently. |
Jiaoyang Li; Zhe Chen; Daniel Harabor; Peter J. Stuckey; Sven Koenig; |
| 1303 | Local and Global Convergence of General Burer-Monteiro Tensor Optimizations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider tensor optimization with a convex and well-conditioned objective function and reformulate it into a nonconvex optimization using the Burer-Monteiro type parameterization. |
Shuang Li; Qiuwei Li; |
| 1304 | Bi-CMR: Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, all these methods assume that label annotations reliably reflect the relevance between their corresponding instances, which is not true in real applications. In this paper, we propose a novel framework called Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval (Bi-CMR), which exploits a bidirectional learning to relieve the negative impact of this assumption. |
Tieying Li; Xiaochun Yang; Bin Wang; Chong Xi; Hanzhong Zheng; Xiangmin Zhou; |
| 1305 | Improving Local Search Algorithms Via Probabilistic Configuration Checking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the idea that neighborhoods with different levels should have different contributions to solving COPs, we propose the probabilistic configuration (PC), which introduces probabilities for neighborhoods at different levels to consider the impact of neighborhoods of different levels on the CC strategy. |
Weilin Luo; Rongzhen Ye; Hai Wan; Shaowei Cai; Biqing Fang; Delong Zhang; |
| 1306 | PEA*+IDA*: An Improved Hybrid Memory-Restricted Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a hybrid memory-restricted algorithm that combines Partial Expansion A* (PEA*) and IDA*. |
Frederico Messa; André Grahl Pereira; |
| 1307 | Search Strategies for Topological Network Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a recursive search over the space of decomposition trees, in which partial solutions are obtained by exploring k-way partitionings of expandable nodes. We present two complementary pruning techniques that bound the value of intermediate solutions from above and below, applying monotonic operations to the contents of unresolved leaves. |
Michael D. Moffitt; |
| 1308 | Hibernated Backdoor: A Mutual Information Empowered Backdoor Attack to Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a practical algorithm to achieve MI maximization to effectively plant the hibernated backdoor. |
Rui Ning; Jiang Li; Chunsheng Xin; Hongyi Wu; Chonggang Wang; |
| 1309 | Planning with Explanations for Finding Desired Meeting Points on Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We experimentally demonstrate that our search-based framework is promising to solve instances with generating explanations in a sequential decision-making process. |
Keisuke Otaki; |
| 1310 | A Fast Local Search Algorithm for The Latin Square Completion Problem Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, to further improve the performance, a fast local search algorithm is developed based on three main ideas. |
Shiwei Pan; Yiyuan Wang; Minghao Yin; |
| 1311 | Sparsification of Decomposable Submodular Functions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In many data intensive applications, however, the number of underlying submodular functions in the original function is so large that we need prohibitively large amount of time to process it and/or it does not even fit in the main memory. To overcome this issue, we introduce the notion of sparsification for decomposable submodular functions whose objective is to obtain an accurate approximation of the original function that is a (weighted) sum of only a few submodular functions. |
Akbar Rafiey; Yuichi Yoshida; |
| 1312 | Subset Approximation of Pareto Regions with Bi-objective A* Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This means that early during search the solution set covers is not diverse, being concentrated in a small region of the solution set. To address this issue, we present a new approach to subset approximation of the solution set, that can be used as the basis for an anytime bi-objective search algorithm. |
Nicolás Rivera; Jorge A. Baier; Carlos Hernández; |
| 1313 | On Probabilistic Generalization of Backdoors in Boolean Satisfiability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the paper we propose a new probabilistic algorithm to solve the latter problem, and show that the asymptotic estimation of the worst-case complexity of the proposed algorithm is significantly smaller than that of the algorithm by Williams et al. |
Alexander Semenov; Artem Pavlenko; Daniil Chivilikhin; Stepan Kochemazov; |
| 1314 | A Novel Approach to Solving Goal-Achieving Problems for Board Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We use RZS and FTL to solve L&D problems on Go, namely solving 68 among 106 problems from a professional L&D book while a previous state-of-the-art program TSUMEGO-EXPLORER solves 11 only. |
Chung-Chin Shih; Ti-Rong Wu; Ting Han Wei; I-Chen Wu; |
| 1315 | Machine Learning for Online Algorithm Selection Under Censored Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. |
Alexander Tornede; Viktor Bengs; Eyke Hüllermeier; |
| 1316 | Procrastinated Tree Search: Black-Box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle. |
Junxiong Wang; Debabrota Basu; Immanuel Trummer; |
| 1317 | DPCD: Discrete Principal Coordinate Descent for Binary Variable Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an efficient algorithm, named Discrete Principal Coordinate Descent (DPCD), to find effective approximate solutions for general binary optimization problems. |
Huan Xiong; |
| 1318 | Optimize What You Evaluate With: Search Result Diversification Based on Metric Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel framework through direct metric optimization for SRD (referred to as MO4SRD) based on the score-and-sort strategy. |
Hai-Tao Yu; |
| 1319 | A First Mathematical Runtime Analysis of The Non-dominated Sorting Genetic Algorithm II (NSGA-II) Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we show that mathematical runtime analyses are feasible also for the NSGA-II. |
Weijie Zheng; Yufei Liu; Benjamin Doerr; |