Accepted for publication in the International Conference on Neural Information Processing (ICONIP) 2020 Original paper is 12 pages, additional appendix is available on arxiv
Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods either ignore the dependency structures entirely or rely on the (unrealistic) assumption that the correlation structures are static over time. In this paper, we propose a Correlation-aware Dynamics Model for CPD, which explicitly models the correlation structure and dynamics of variables by incorporating graph neural networks into an encoder-decoder framework. Extensive experiments on synthetic and real-world datasets demonstrate the advantageous performance of the proposed model on CPD tasks over strong baselines, as well as its ability to classify the change-points as correlation changes or independent changes. Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所智能感知与计算研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；Alibaba Group（阿里巴巴集团）
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection
School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）；Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University（闽江学院福建省信息处理与智能控制重点实验室）
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
Telenor Research and Innovation（Telenor研究与创新）；Norwegian Research Council（挪威研究理事会）；Telenor Denmark OSS & Tech Analytics Denmark（Telenor丹麦OSS与技术分析丹麦）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；SINTEF Digital（SINTEF数字技术）；Hance
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Zhejiang University（浙江大学）；Westlake University（西湖大学）；UC Berkeley（加州大学伯克利分校）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖高等研究院前沿技术研究所）
Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Data
College of Computing and Data Science, Nanyang Technological University (NTU), Singapore（南洋理工大学计算与数据科学学院）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息通信研究所（I2R），A*STAR，新加坡）；School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息科学技术大学人工智能学院）
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Nankai University（南开大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Southern California（南加州大学）；China University of Mining and Technology（中国矿业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Joint Laboratory of Data Science and Business Intelligence, School of Statistics, Southwestern University of Finance and Economics（西南财经大学统计学院数据科学与商务智能联合实验室）；University of Kansas（堪萨斯大学）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学海洋地质国家重点实验室、自动化与智能传感学院）
Group-Invariant Unsupervised Skill Discovery: Symmetry-aware Skill Representations for Generalizable Behavior
School of Mechanical Engineering, Yonsei University（延世大学机械工程学院）；Department of Artificial Intelligence, Yonsei University（延世大学人工智能系）；Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
School of Computer Science and Engineering, Sun Yat-sen University（计算机科学与工程学院，中山大学）；School of Systems Science and Engineering, Sun Yat-sen University（系统科学与工程学院，中山大学）；School of Software Engineering, Sun Yat-Sen University（软件工程学院，中山大学）
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
Telenor Research and Innovation（Telenor研究与创新）；Telenor Denmark OSS & Tech Analytics（Telenor丹麦OSS与技术分析）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；SINTEF Digital（SINTEF数字）；Hance
DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction
GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；PCASO Laboratory, Department of Surgery, University of Pennsylvania（宾夕法尼亚大学外科PCASO实验室）；Department of Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）
Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Michigan State University（密歇根州立大学）；Alibaba Group（阿里巴巴集团）
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；The Hong Kong Polytechnic University（香港理工大学）；University of Southern California（南加州大学）
TransPeakNet: Solvent-Aware 2D NMR Prediction via Multi-Task Pre-Training and Unsupervised Learning
Brandeis University（布兰迪斯大学）；Harvard Medical School（哈佛医学院）；Complex Carbohydrate Research Center, University of Georgia（佐治亚大学复合碳水化合物研究中心）；Boston College（波士顿学院）
