Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the non-query variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.
Exploiting Contextual Information via Dynamic Memory Network for Event Detection
Institute of Computing Technology, Chinese Academy of Science (CAS)（中国科学院计算技术研究所）；School of Computer and Control Engineering, The University of CAS（中国科学院大学计算机与控制工程学院）
Exploiting Global Contextual Information for Document-level Named Entity Recognition
Huazhong University of Science and Technology（华中科技大学）；Beijing Institute of Technology（北京理工大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Naval University of Engineering（海军工程大学）
Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction
Institute of Engineering & Management（工程与管理学院）；Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；BCET Durgapur（杜尔加布尔BCET工程学院）；National Institute of Technology Durgapur（杜尔加布尔国家理工学院）
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Technical University of Munich（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Oxford（牛津大学）；Lund University（隆德大学）；Imperial College London（伦敦帝国学院）
Transformer Normalisation Layers and the Independence of Semantic Subspaces
University of Manchester（曼彻斯特大学）；Aalto University（阿尔托大学）；Idiap Research Institute（Idiap研究所）；National Biomarker Centre, CRUK-MI, University of Manchester（英国癌症研究中心曼彻斯特研究所国家生物标志物中心（曼彻斯特大学））
