グラフ理論 The World of Bayesian Modeling
Overview
This presentation provides an overview of the contemporary world of Bayesian modeling from the perspec...
自然言語処理:Natural Language Processing
グラフ理論 ベイズ推定 Machine Learning with Bayesian Inference and Graphical Model
Machine Learning with Bayesian Inference and Graphical Model
Machine learning using Bayesian inference is a stati...
ベイズ推定 Machine Learning Professional Series “Variational Bayesian Learning” reading notes
Summary
Variational Bayesian learning applies a variational approach to stochastic models in Bayesian estimatio...
Symbolic Logic Zen and Buddha Bot as Told by Artificial No-Brains
The early history of the dialogue engine, starting with the importance of dialogue and Eliza to the meaning of words, philosophy by Wittgenstein, meta-literature by James Joyce, Zen thought (the Ten Oxen) for enlightenment, Zen questions and answers and Buddhabot.
Clojure AI Dialogue Engine
I will describe the technical classification of the AI dialogue engine at the center of chatbot technology, the use of natural language processing, the use of deep learning technology, and the combination of knowledge graphs, which has attracted attention in recent years, as well as an overview of the technology, benchmarks, and implementation using Clojure.
Clojure Chatbots and Question&Answer Technology
Overview of chubbot technology used in digital transformation , artificial intelligence, etc., from a business perspective, as well as question-and-answer systems that combine natural language processing, deep learning, and knowledge network technologies
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.
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.
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.