機械学習:Machine Learning Protected: Higher-order relational data – an overview of tensor data processing Tensor data processing to analyze relationships between three or more objects for use in digital transformation and artificial intelligence tasks. 2021.12.27 機械学習:Machine Learning自然言語処理:Natural Language Processing関係データ学習
旅 Zen, Temples, and the History of Kamakura (Rinzai Zen and the Five Mountains of Kamakura) An introduction to Rinzai Zen, one of the roots of Japanese Zen, and the Five Temples of Kamakura (Kenchoji, Engakuji, Fukujuji, Jochiji, and Jomyoji), as well as Eisai, Rankei Doryu, and Mugaku Sougen. 2021.12.26 旅禅:Zen
微分積分:Calculus Protected: Estimating the number of topics in a topic model – Dirichlet process, Chinese restaurant process, stick-folding process A topic model using Dirichlet process, Chinese restaurant process, and stick-folding process for digital transformation and artificial intelligence tasks. 2021.12.25 微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
微分積分:Calculus Protected: Application of Topic Models to Information Other Than Documents – Application to Image Data and Graph Data (Stochastic Block Model, Mixed Member Probabilistic Block Model) Topic models for image and graph data using stochastic block models for digital transformation and artificial intelligence tasks. 2021.12.24 微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
推論技術:inference Technology MCMC and Bayesian estimation デジタルトランスフォーメーション(DX)、人工知能(AI)タスクに活用される確率関数の積分等に用いられるマルコフ連鎖モンテカルロ 2021.12.23 推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
微分積分:Calculus Protected: Extension of topic models (adding structure to topics) Correlation topic model, slingshot distribution model with hierarchical structure, probabilistic latent semantic visualization with low-dimensional spatial structure Overview of topic models with structure in correlated topics used in digital transformation and artificial intelligence tasks (correlated topic model, slingshot distribution model with hierarchical structure, probabilistic latent meaning visualization with low-dimensional spatial structure) 2021.12.23 微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
微分積分:Calculus Protected: Extending topic models (using other information as well) (2) Noisy correspondence topic model, author topic model, topic tracking model Among topic models that rely on auxiliary information for digital transformation and artificial intelligence tasks, we will discuss noisy topic models, author topic models, and topic tracking models. 2021.12.22 微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
推論技術:inference Technology Protected: Extending the topic model (using other information) (1) Combined topic model and corresponding topic model Create a topic model with auxiliary information to be used for digital transformation and artificial intelligence tasksJoining / Corresponding Topic Model Overview 2021.12.21 推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
セマンテックウェブ技術:Semantic web Technology ISWC2003 Papers Overview of ISWC (International Semantic Web Conference) 2003 2021.12.20 セマンテックウェブ技術:Semantic web Technology
セマンテックウェブ技術:Semantic web Technology ISWC2002 Papers Introduction to the papers of ISWC2002, the international conference on the Semantic Web, many of the papers are about the integration of Web services, which is the flexible processing of data after connecting the data. 2021.12.20 セマンテックウェブ技術:Semantic web Technology