スポーツ:Sports Surfboard
The history of surfboards, an important surfing gear (wood boards, fiberglass boards, molded (epoxy) boards, soft boards)
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Deux Ex Machina
スポーツ:Sports 微分積分:Calculus Protected: Topic models – maximum likelihood estimation, variational Bayesian estimation, estimation by Gibbs sampling
Maximum likelihood, variational Bayesian, and Gibbs sampling estimation of topic models for digital transformation , artificial intelligence , and natural language processing tasks.
グラフ理論 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.
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.
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.
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.
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.
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
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)
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.