Deus Ex Machina

Protected: Sparse Modeling and Multivariate Analysis (6) Image Processing and Sparsity (Overview of Machine Learning for Signal Processing)

Overview of Sparse Models for Machine Learning of Image Information for Artificial Intelligence (AI) and Digital Transformation (DX), JPEG, DCT, Sparse Land Model

2021.08.31

スパースモデリング機械学習:Machine Learning

Protected: Sparse modeling and multivariate analysis (5) Graphical lasso and its application (anomaly detection, etc.)

Graph sparse models used for dimensionality reduction of graph data and explanation of machine learning models, introduction of sparsity to relations and its application to graphical lasso and anomaly detection etc.

2021.08.30

スパースモデリング機械学習:Machine Learning

Protected: Sparse Modeling and Multivariate Analysis (4) Introducing Sparsity into Relationships

The graph sparsity model, which is used to reduce the dimensionality of graph data and to explain machine learning models, is discussed in terms of introducing sparsity into relationships and graph lasso.

2021.08.29

スパースモデリング機械学習:Machine Learning

Protected: Two approaches to language meaning (fusion of symbolic and distributed representations)

Symbolic and vector representation approaches to natural language meaning that can be used for artificial intelligence (AI) and digital transformation (DX) tasks and their integration, content relation recognition, paraphrase recognition, semantic similarity recognition, datasets (RTE, RITE, STS)

2021.08.28

機械学習:Machine Learning自然言語処理:Natural Language Processing

Protected: Teaching the meaning of words to a computer (on various language models)

Meaning of Natural Language by WordNet (Dictionary), Distributed Hypothesis, PPMI, Singularity Decomposition (SVD), Word2Vec (Distributed Representation) in Natural Language Processing

2021.08.27

機械学習:Machine Learning自然言語処理:Natural Language Processing

Protected: Topic models that capture the individuality of language

Topic models to capture latent meanings behind sentences, differences between various probabilistic approaches and deep learning, supervised LDA, Boltzmann machines,Naive Bayes

2021.08.26

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

Introduction to models of language (probabilistic unigram models and Bayesian probability)

Natural language processing as it applies to digital transformation (DX), artificial intelligence (AI), machine learning, etc. Modeling of natural language, application of unigram models and Bayesian probabilistic models.

2021.08.25

確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Strategies in similarity matching methods (7) Improved alignment disambiguation

Alignment disambiguation for optimization of natural language simirality and ontology matching for digital transformation (DX) and artificial intelligence (AI) applications

2021.08.24

セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing

Strategies in similarity matching methods (6) Alignment extraction approach

Optimization and alignment extraction for natural language simirality and ontology matching for digital transformation (DX) and artificial intelligence (AI) applications

2021.08.23

セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing

Strategies in similarity matching methods (5) Tuning approaches

Digital transformation (DX), similarity of natural language for artificial intelligence (AI), tuning by machine learning for ontology matching, stacked generalization, genetic algorithms

2021.08.22

セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing