topic model

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.

2021.12.29

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: 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

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

Topic Model Theory and Implementation

A topic model is a probability generation model for extracting topics from sentences, which is one of the natural language processing technologies used in digital transformation and artificial intelligence tasks.

2021.12.17

機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Machine Learning Professional Series: Topic Models Post-Reading Notes

Topic models using probability generation models to extract sentence topics to be used in digital transformation (DX) and artificial intelligence (AI) tasks.

2021.11.28

微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Unigram Model

Basics of topic model for classification of document data for education of beginners, unigram model

2021.04.30

アルゴリズム:Algorithms機械学習:Machine Learning確率・統計:Probability and Statistics