人工知能:Artificial Intelligence

Protected: Tensor decomposition – CP decomposition and Tucker decomposition

Processing of higher-order relational data and tensors using CP decomposition and Tucker decomposition for digital transformation and artificial intelligence tasks.

2021.12.28

グラフ理論微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

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

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

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

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

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

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

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

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

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