数学:Mathematics

Protected: Fundamentals of Online Learning Stochastic Gradient Descent – Application to Perceptron, SVM, and Logistic Regression

Online learning applications to the perceptron, SVM, and logistic regression for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.06

オンライン学習微分積分:Calculus最適化:Optimization確率・統計:Probability and Statistics線形代数:Linear Algebra

Online learning and online prediction

Online learning is a sequential machine learning technique used in digital transformation , artificial intelligence , and machine learning tasks, and online prediction combines these techniques with decision-making problems.

2022.01.05

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Implementation in online learning – sparse vector computation and averaging perceptron, averaged stochastic gradient descent, lazy update

Various implementation techniques for online learning for digital transformation , artificial intelligence and machine learning tasks (sparse vector computation and averaging perceptrons, averaged stochastic gradient descent, lazy update).

2022.01.05

オンライン学習最適化:Optimization

Protected: Instance recognition and retrieval (2) General image retrieval

Search optimization using tree structure, hashing, sequential quantization, spectral hashing, k-means hashing, etc. for digital transformation and artificial intelligence tasks, and evaluation using mAP and recall@R.

2022.01.02

データベース技術:DataBase Technology幾何学:Geometry微分積分:Calculus検索技術:Search Technology機械学習:Machine Learning

Rule base, knowledge base, expert system and relational data

Rule-based and knowledge-based and expert systems and relational data as knowledge information processing handling for use in digital transformation and artificial intelligence tasks.

2022.01.01

Symbolic Logicデータベース技術:DataBase Technology数理論理学:Mathematical logic検索技術:Search Technology

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: 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: 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