Protected: Sparse Modeling and Multivariate Analysis (8) Sparsity of Time Transitions
Application of machine learning techniques and sparse modeling to time-varying information (changes in customers' purchasing interests)
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Deux Ex Machina
Protected: Sparse Modeling and Multivariate Analysis (7) Image Processing and Sparsity (Application of Sparse Land Model)
Application of sparse models, one of the machine learning techniques applicable to artificial intelligence (AI) and digital transformation (DX), to image processing (denoising, object extraction, homography transformation, etc.)
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
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.
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.
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)
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
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
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.
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