Many contrastive and meta-learning approaches learn representations by identifying common features in multiple views. However, the formalism for these approaches generally assumes features to be shared across views to be captured coherently. We consider the problem of learning a unified representation from partial observations, where useful features may be present in only some of the views. We approach this through a probabilistic formalism enabling views to map to representations with different levels of uncertainty in different components; these views can then be integrated with one another through marginalisation over that uncertainty. Our approach, Partial Observation Experts Modelling (POEM), then enables us to meta-learn consistent representations from partial observations. We evaluate our approach on an adaptation of a comprehensive few-shot learning benchmark, Meta-Dataset, and demonstrate the benefits of POEM over other meta-learning methods at representation learning from partial observations. We further demonstrate the utility of POEM by meta-learning to represent an environment from partial views observed by an agent exploring the environment.
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学研究院）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；State Key laboratory of Space Network and Communications, Tsinghua University（清华大学空间网络与通信国家重点实验室）
A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona（创新医学工程系，威尼斯大学）；Faculty of Computer and Information Science, University of Ljubljana（计算机与信息科学系，卢布尔雅那大学）；Qualyco S.r.l.（Qualyco公司）
SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Department of Computer Science, Shantou University（汕头大学计算机科学系）；Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University（西南大学电子信息工程学院非线性电路与智能信息处理重点实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Defence Industry Secrecy Examination and Certification Center（国防工业保密审查认证中心）
University of Leeds（利兹大学）；Shaoxing University（绍兴文理学院）；Nanjing University of Information Science and Technology（南京信息工程大学）；University of Eastern Finland（东芬兰大学）；Huzhou University（湖州师范学院）
CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction
University of Pittsburgh（匹兹堡大学）；Purdue University（普渡大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；NOAA Global Monitoring Laboratory（国家海洋大气管理局全球监测实验室）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Maryland（马里兰大学）
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Tencent AI Lab（腾讯AI实验室）；Megvii Research Shanghai（上海旷视研究院）；Tsinghua University（清华大学）；Peking University（北京大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
Zhejiang University of Technology（浙江工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能研究院）；Science and Technology on Communication Information Security Control Laboratory（通信信息安全控制技术实验室）
Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-{ \it i.i.d} function instantiations, CNPs are jointly optimized for in-instantiation observation prediction and cross-instantiation meta-representation adaptation within a generative reconstruction pipeline. There can be a challenge in tying together such two targets when the distribution of function observations scales to high-dimensional and noisy spaces. Instead, noise contrastive estimation might be able…
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs
University of New South Wales（新南威尔士大学）；Yonsei University（延世大学）；RIKEN AIP（理化学研究所人工智能项目）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL
ETH Zurich（苏黎世联邦理工学院）；University of Warsaw（华沙大学）；Princeton University（普林斯顿大学）；MPI IS Tubingen（图宾根马克斯·普朗克智能系统研究所）；Warsaw University of Technology（华沙理工大学）；IDEAS Research Institute（IDEAS研究所）
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；University of Trento（特伦托大学）；Zhuhai College of Science and Technology（珠海科技学院）
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学计算机科学与工程学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院）
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
National Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统国家重点实验室）
