Accept paper list

anomaly detection [anomaly, outlier, out-of-distribution, one-class, Malware detection, …]

• LREN: Low-Rank Embedded Network for Sample-Free Hyperspectral Anomaly Detection

  Kai Jiang, Weiying Xie, Jie Lei, Tao Jiang, Yunsong Li

• GAN Ensemble for Anomaly Detection

  Xiaohui Chen, Xu Han, Liping Liu

• Anomaly Attribution with Likelihood Compensation

  Tsuyoshi Ide, Amit Dhurandhar, Jiri Navratil, Moninder Singh, Naoki Abe

• Regularizing Attention Networks for Anomaly Detection in Visual Question Answering

  Doyup Lee, Yeongjae Cheon, Wook-Shin Han

• Appearance-Motion Memory Consistency Network for Video Anomaly Detection

  Ruichu Cai, Hao Zhang, Wen Liu, Shenghua Gao, Zhifeng Hao

• 【看一下】 Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection

  Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, Pheng-Ann Heng

• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

  Ailin Deng, Bryan Hooi

• 【重点阅读】 Time Series Anomaly Detection with Multiresolution Ensemble Decoding

  Lifeng Shen, Zhongzhong Yu, Qianli Ma, James Tin-Yau Kwok

• 【看一下】 Outlier Impact Characterization for Time Series Data

  Jianbo Li, Lecheng Zheng, Yada Zhu, Jingrui He

• Graph Neural Network to Dilute Outliers for Refactoring Monolith Application

  Utkarsh Desai, Sambaran Bandyopadhyay, Srikanth Tamilselvam

• Accelerated Combinatorial Search for Outlier Detection with Provable Bound on Sub-
  Optimality

  Guihong Wan, Haim Schweitzer

• 【看一下】 Neighborhood Consensus Networks for Unsupervised Multi-View Outlier Detection

  Li Cheng, Yijie Wang, Xinwang Liu

• DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and
  Semantic Augmentation

  Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, Gary Chan, Zhenguo Li

• Few-Shot One-Class Classification via Meta-Learning

  Ahmed Frikha, Denis Krompass, Hans-Georg Koepken, Volker Tresp

• Classifying Sequences of Extreme Length with Constant Memory Applied to Malware
  Detection

  Edward Raff, William Fleshman, Richard J Zak, Hyrum Anderson, Bobby Filar, Mark McLean

• Disentangled Representation Learning in Heterogeneous Information Network for Large-
  Scale Android Malware Detection in the COVID-19 Era and Beyond

  Shifu Hou, Yujie Fan, Mingxuan Ju, Yanfang Ye, Wenqiang Wan, Kui Wang, Yinming Mei, Qi Xiong,
  Fudong Shao

heterogeneous

• Embedding Heterogeneous Networks into Hyperbolic Space without Meta-­‐Path

  Lili Wang, Chongyang Gao, Chenghan Huang, Ruibo Liu, Weicheng Ma, Soroush Vosoughi

• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-­‐Horizon Probabilistic Forecasting

  Longyuan Li, Jihai Zhang, Junchi Yan, Yaohui Jin, Yunhao Zhang, Yanjie Duan, Guangjian Tian

• Multi-­‐Modal Multi-­‐Label Emotion Recognition with Heterogeneous Hierarchical Message Passing

  Dong Zhang, Xincheng Ju, Wei Zhang, Junhui Li, Shoushan Li, Zhu Qiaoming, Zhou Guodong

• Heterogeneous Graph Structure Learning for Graph Neural Networks

  Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, Yanfang Ye

• Disentangled Representation Learning in Heterogeneous Information Network for Large-­‐
  Scale Android Malware Detection in the COVID-­‐19 Era and Beyond

  Shifu Hou, Yujie Fan, Mingxuan Ju, Yanfang Ye, Wenqiang Wan, Kui Wang, Yinming Mei, Qi Xiong, Fudong Shao

• MERL: Multimodal Event Representation Learning in Heterogeneous Embedding Spaces

  Linhai Zhang, Deyu Zhou, Yulan He, Zeng Yang

• Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

  Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, Xiao Zhang

• 【重要】 Infusing Multi-­‐Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation

  Yunlong Liang, Fandong Meng, Ying Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

• HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction

  Kuo-­‐Yu Huang, Hen-­‐Hsen Huang, Hsin-­‐Hsi Chen

• Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures

  Sebastian Risi, Kenneth O Stanley

• Real-­‐Time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data

  Boyo Chen, Buo-­‐Fu Chen, Yun-­‐Nung Chen

Time series

• Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting

  Amirreza Farnoosh, Bahar Azari, Sarah Ostadabbas

• 【重点阅读】 Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse
  Multivariate Time Series

  Yinjun Wu, Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao
  Zhang, Haifeng Chen, Susan B Davidson

• Second Order Techniques for Learning Time-Series with Structural Breaks

  Takayuki Osogami

• Correlative Channel-Aware Fusion for Multi-View Time Series Classification

  Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li, Yun Fu

• 【看一下】 Learnable Dynamic Temporal Pooling for Time Series Classification

  Dongha Lee, Seonghyeon Lee, Hwanjo Yu

• Time Series Domain Adaptation via Sparse Associative Structure Alignment

  Ruichu Cai, Jiawei Chen, Zijian Li, Wei Chen, Keli Zhang, Junjian Ye, Zhuozhang Li, Xiaoyan Yang,
  Zhenjie Zhang

• 【看一下】 Learning Representations for Incomplete Time Series Clustering

  Qianli Ma, Chuxin Chen, Sen Li, Garrison Cottrell

• Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series
  Forecasting

  Nam Nguyen, Brian Quanz

• ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification

  Guozhong Li, Byron Choi, Jianliang Xu, Sourav S Bhowmick, Kwok-Pan Chun, Grace Lai-Hung Wong

• Joint-Label Learning by Dual Augmentation for Time Series Classification

  Qianli Ma, Zhenjing Zheng, Jiawei Zheng, Sen Li, Wanqing Zhuang, Garrison Cottrell

• 【Best paper award】 Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

  Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang

• Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting

  Boris N. Oreshkin, Dmitri Carpov, Chapados Nicolas, Yoshua Bengio

about deep learning

• Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster

  Zhiqin John Xu, Hanxu Zhou

• Understanding Decoupled and Early Weight Decay

  Johan Björck, Kilian Weinberger, Carla P Gomes

sequence

• Copy That! Editing Sequences by Copying Spans

  Sheena L Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt

• Semi-Supervised Knowledge Amalgamation for Sequence Classification

  Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner

• Neural Sequence-to-Grid Module for Learning Symbolic Rules

  Segwang Kim, Hyoungwook Nam, Joonyoung Kim, Kyomin Jung

• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic
  Forecasting

  Longyuan Li, Jihai Zhang, Junchi Yan, Yaohui Jin, Yunhao Zhang, Yanjie Duan, Guangjian Tian

• Semi-Supervised Sequence Classification through Change Point Detection

  Nauman Ahad, Mark Davenport

• Bridging Towers of Multi-Task Learning with a Gating Mechanism for Aspect-Based
  Sentiment Analysis and Sequential Metaphor Identification

  Rui Mao, Xiao Li

• Deterministic Mini-Batch Sequencing for Training Deep Neural Networks

  Subhankar Banerjee, Shayok Chakraborty

• 【看一下】 SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning

  Ting Yao, Yiheng Zhang, Zhaofan Qiu, Yingwei Pan, Tao Mei

• Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

  Bhushan Kotnis, Carolin Lawrence, Mathias Niepert

• Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences

  Andis Draguns, Emīls Ozoliņš, Agris Šostaks, Matīss Apinis, Karlis Freivalds

• Entity Guided Question Generation with Contextual Structure and Sequence Information
  Capturing

  Qingbao Huang, Mingyi Fu, Linzhang Mo, Yi Cai, Jingyun Xu, Pijian Li, Qing Li, Ho-fung Leung

• Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-
  Generation Networks

  Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, Yan Zhang

• 【看一下】 Continuous-Time Attention for Sequential Learning

  Yi-Hsiang Chen, Jen-Tzung Chien

• Interpretable Sequence Classification via Discrete Optimization

  Maayan Shvo, Andrew C Li, Rodrigo A Toro Icarte, Sheila A. McIlraith

interpretable [Understanding, explanation, Attribution …]

• Building Interpretable Interaction Trees for Deep NLP Models

  Die Zhang, HuiLin Zhou, Xiaoyi Bao, Da Huo, Ruizhao Chen, Hao Zhang, Xu Cheng, Mengyue Wu,
  Quanshi Zhang

• Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component
  Analysis and Graph Neural Network

  Seunghyun Lee, Byung Cheol Song

• Interpreting Neural Networks as Quantitative Argumentation Frameworks

  Nico Potyka

• Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

  Yuhang Yao, Carlee Joe-Wong

• Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing
  Comparative Gradients and Hostile Activations

  Woo Jeoung Nam, Jaesik Choi, Seong-Whan Lee

• Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis

  Rohan K Yadav, Lei Jiao, Ole-Christoffer Granmo, Morten Goodwin

• Learning Accurate and Interpretable Decision Rule Sets from Neural Networks

  Litao Qiao, Weijia Wang, Bill Lin

• Learning Interpretable Models for Couple Networks under Domain Constraints

  Hongyuan You, Sikun Lin, Ambuj Singh

• Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning
  with Interpretability

  Tao Han, Wei-Wei Tu, Yu-Feng Li

• i-Algebra: Towards Interactive Interpretability of Deep Neural Networks

  Xinyang Zhang, Pang Ren, Shouling Ji, Fenglong Ma, Ting Wang

• 【看一下】 Explainable Models with Consistent Interpretations

  Vipin Pillai, Hamed Pirsiavash

• Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods

  Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso

• HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

  Yuanyuan Chen, Boyang Li, Han Yu, Pengcheng Wu, Chunyan Miao

• Interpreting Multivariate Shapley Interactions in DNNs

  Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang

• 【看一下】 Self-Attention Attribution: Interpreting Information Interactions Inside Transformer

  Yaru Hao, Li Dong, Furu Wei, Ke Xu

• Interpretable Sequence Classification via Discrete Optimization

  Maayan Shvo, Andrew C Li, Rodrigo A Toro Icarte, Sheila A. McIlraith

• 【看一下】 The Heads Hypothesis: A Unifying Statistical Approach towards Understanding Multi-Headed
  Attention in BERT

  Madhura Pande, Aakriti Budhraja, Preksha Nema, Pratyush Kumar, Mitesh M. Khapra

• Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

  Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura

• Strong Explanations in Abstract Argumentation

  Markus Ulbricht, Johannes Peter Wallner

• On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

  Eoin Kenny, Mark Keane

• The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and
  Decomposable Boolean Circuits

  Marcelo Arenas, Pablo Barceló, Leopoldo Bertossi, Mikaël Monet

• On the Tractability of SHAP Explanations

  Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu

• Responsibility Attribution in Parameterized Markovian Models

  Christel Baier, Florian Funke, Rupak Majumdar

• A Unified Taylor Framework for Revisiting Attribution Methods

  Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu

• 【看一下】 Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and
  Block-Wise Feature Aggregation

  Sam Sattarzadeh, Mahesh Sudhakar, Anthony Lem, Shervin Mehryar, Konstantinos N Plataniotis,
  Jongseong Jang, Hyunwoo Kim, Yeonjeong Jeong, SangMin Lee, Kyunghoon Bae

• 【看一下】 Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided
  Factorization

  Shir Gur, Ameen Ali, Lior Wolf

• Enhanced Regularizers for Attributional Robustness

  Anindya Sarkar, Anirban Sarkar, Vineeth N Balasubramanian

• 【看一下】 Explaining a Black-Box by Using a Deep Variational information Bottleneck Approach

  Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing

• Explaining Neural Matrix Factorization with Gradient Rollback

  Carolin Lawrence, Timo Sztyler, Mathias Niepert

Autoencoder

• Content Learning with Structure-Aware Writing: A Graph-Infused Dual Conditional
  Variational Autoencoder for Automatic Storytelling

  Meng Hsuan Yu, Juntao Li , Zhangming Chan, Dongyan Zhao, Rui Yan

• 【看一下】 HOT-VAE: Learning High-Order Label Correlation for Multi-LabelClassification via Attention-
  Based Variational Autoencoders

  Wenting Zhao, Shufeng Kong, Junwen Bai, Daniel Fink, Carla P Gomes

• Fractal Autoencoders for Feature Selection

  Xinxing Wu, Qiang Cheng

• Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series
  Forecasting

  Nam Nguyen, Brian Quanz

• Open-Set Recognition with Gaussian Mixture Variational Autoencoders

  Alexander Cao, Yuan Luo, Diego Klabjan

• Unsupervised Learning of Discourse Structures Using a Tree Autoencoder

  Patrick Huber, Giuseppe Carenini

missing value & irregularly sampled time series [Incomplete, imputation, …]

• Generative Semi-Supervised Learning for Multivariate Time Series Imputation

  Xiaoye Miao, Yangyang Wu, Jun Wang, Yunjun Gao, Xudong Mao, Jianwei Yin

• Tripartite Collaborative Filtering with Observability and Selection for Debiasing Rating
  Estimation on Missing-Not-at-Random Data

  Qi Zhang, Longbing Cao, Chongyang Shi, Liang Hu

• Unified Tensor Framework for Incomplete Multi-View Clustering and Missing-View Inferring

  Jie Wen, Zheng Zhang, Zhao Zhang, Lei Zhu, Lunke Fei, Bob Zhang, Yong Xu

• Quantification of Resource Production Incompleteness

  Yakoub Salhi

• 【看一下】 Learning Representations for Incomplete Time Series Clustering

  Qianli Ma, Chuxin Chen, Sen Li, Garrison Cottrell

• The Parameterized Complexity of Clustering Incomplete Data

  Eduard Eiben, Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider

• Restricted Domains of Dichotomous Preferences with Possibly Incomplete Information

  Zoi Terzopoulou, Alexander Karpov, Svetlana Obraztsova

• Estimating the Number of Induced Subgraphs from Incomplete Data and Neighborhood
  Queries

  Dimitris Fotakis, Thanasis Pittas, Stratis Skoulakis

Recurrent Neural Network

这部分都可以看一下

• Shuffling Recurrent Neural Networks

  Michael Rotman, Lior Wolf

• Memory-Gated Recurrent Networks

  Yaquan Zhang, Qi Wu, Nanbo Peng, Min Dai, Jing Zhang, Hu Wang

• On the Softmax Bottleneck of Recurrent Language Models

  Dwarak Govind Parthiban, Yongyi Mao, Diana Inkpen

• Forecasting Reservoir Inflow via Recurrent Neural ODEs

  Fan Zhou, Liang Li

clustering

• Hierarchical Multiple Kernel Clustering

  Jiyuan Liu, Xinwang Liu, Siwei Wang, Sihang Zhou, Yuexiang Yang

• Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

  Yuhang Yao, Carlee Joe-Wong

• Clustering Ensemble Meets Low-Rank Tensor Approximation

  Yuheng Jia, Hui Liu, Junhui Hou, Qingfu Zhang

• Contrastive Clustering

  Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, Xi Peng

• GoT: a Growing Tree Model for Clustering Ensemble

  Feijiang Li, Yuhua Qian, Jieting Wang

• Unified Tensor Framework for Incomplete Multi-View Clustering and Missing-View Inferring

  Jie Wen, Zheng Zhang, Zhao Zhang, Lei Zhu, Lunke Fei, Bob Zhang, Yong Xu

• LRSC: Learning Representations for Subspace Clustering

  Changsheng Li, Chen Yang, Bo Liu, Ye Yuan, Guoren Wang

• Automated Clustering of High-Dimensional Data with a Feature Weighted Mean-Shift
  Algorithm

  Saptarshi Chakraborty, Debolina Paul, Swagatam Das

• Learning Representations for Incomplete Time Series Clustering

  Qianli Ma, Chuxin Chen, Sen Li, Garrison Cottrell

• Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning

  Zhenwen Ren, Quansen Sun, Dong Wei

• Tri-Level Robust Clustering Ensemble with Multiple Graph Learning

  Peng Zhou, Liang Du, Yi-Dong Shen, Xuejun Li

• Deep Mutual Information Maximin for Cross-Modal Clustering

  Yiqiao Mao, Xiaoqiang Yan, Qiang Guo, Yangdong Ye

• Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with
  Stochastic Pairwise Constraints

  Brian Brubach, Darshan Chakrabarti, John P Dickerson, Aravind Srinivasan, Leonidas Tsepenekas

• Deep Fusion Clustering Network

  Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, Jieren Cheng

• The Parameterized Complexity of Clustering Incomplete Data

  Eduard Eiben, Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider

• Objective-Based Hierarchical Clustering of Deep Embedding Vectors

  Dmitrii Avdiukhin, Stanislav Naumov, Grigory Yaroslavtsev

• Variational Fair Clustering

  Imtiaz Masud Ziko, Jing Yuan, Eric Granger, Ismail Ben Ayed

• Extreme k-Center Clustering

  MohammadHossein Bateni, Hossein Esfandiari, Manuela Fischer, Vahab Mirrokni

• Differentially Private Clustering via Maximum Coverage

  Matthew Jones, Huy Nguyen, Thy D Nguyen

data augmentation

• AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing

  Qi Song, Kangfu Mei, Rui Huang

• How Does Data Augmentation Affect Privacy in Machine Learning?

  Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

• SnapMix: Semantically Proportional Mixing for Augmenting Fine-Grained Data

  Shaoli Huang, Xinchao Wang, Dacheng Tao

• Inferring Emotion from Large-Scale Internet Voice Data: A Semi-Supervised Curriculum
  Augmentation Based Deep Learning Approach

  Suping Zhou, Jia Jia, Zhiyong Wu, Zhihan Yang, Yanfeng Wang, Wei Chen, Fanbo Meng, Shuo
  Huang, Jialie Shen, Xiaochuan Wang

• Kernel-Convoluted Deep Neural Networks with Data Augmentation

  Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik

• Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation

  Ignacio Iacobacci, Ieva Staliūnaitė, Philip John Gorinski

• Self-Supervised Multi-View Stereo via Effective Co-Segmentation and Data-Augmentation

  Hongbin Xu, Zhipeng Zhou, Yu Qiao, Wenxiong Kang, Qiuxia Wu

• Joint-Label Learning by Dual Augmentation for Time Series Classification

  Qianli Ma, Zhenjing Zheng, Jiawei Zheng, Sen Li, Wanqing Zhuang, Garrison Cottrell

• Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-
  Training

  Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira
  dos Santos, Bing Xiang

• Two-Stream Convolution Augmented Transformer for Human Activity Recognition

  Bing Li, Wei Cui, Wei Wang, Le Zhang, Zhenghua Chen, Min Wu

• Data Augmentation for Graph Neural Networks

  Tong Zhao, Yozen Liu, Leonardo Neves, Oliver J Woodford, Meng Jiang, Neil Shah

About distribution

• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease
  Identification

  Yi Zhou, Lei Huang, Tianfei Zhou, Ling Shao

• Robust Lightweight Facial Expression Recognition Network with Label Distribution Training

  Zengqun Zhao, Qingshan Liu, Feng Zhou

• Wasserstein Distributionally Robust Inverse Multiobjective Optimization

  Chaosheng Dong, Bo Zeng

• The Gap on Gap: Tackling the Problem of Differing Data Distributions in Bias-Measuring
  Datasets

  Vid Kocijan, Oana-Maria Camburu, Thomas Lukasiewicz