推論技術:inference Technology Statistical Causal Inference and Causal Search
Statistical Causal Inference and Causal Search
When using machine learning, it is important to consider the diffe...
2022-01
推論技術:inference Technology 強化学習 Protected: Planning Problems(1) – Approaches Using Dynamic Programming and Theoretical Underpinnings
Reinforcement learning by planning problems (dynamic programming and linear programming) for sequential decision problems in known environments used for digital transformation , artificial intelligence and machine learning tasks.
Uncategorized Machine Learning Professional Series – Statistical Causal Search Reading Notes
Statistical causal search to find cause and effect relationships in vast amounts of data used for digital transformation , machine learning , and artificial intelligence tasks.
セマンテックウェブ技術:Semantic web Technology ISWC2008 Papers
ISWC2008, International Semantic Web Conference Proceedings Abstracts.
最適化:Optimization Machine Learning Professional Series Sparsity-Based Machine Learning Reading Notes
Overview of sparse modeling used for regularization and other applications in machine learning for digital transformation , artificial intelligence , and machine learning tasks.
セマンテックウェブ技術:Semantic web Technology ISWC2007 Papers
ISWC2007, International Semantic Web Conference Proceedings Abstracts.
オンライン学習 Protected: Evaluating the performance of online learning(Perceptron, Regret Analysis, FTL, RFTL)
Perceptron and Riglet Analysis (FTL, RFTL) for evaluating online learning used for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Advanced online learning (4) Application to deep learning (AdaGrad, RMSprop, ADADELTA, vSGD)
Application to online learning in AdaGrad, RMSprop, and vSGD used for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Advanced online learning (3) Application to deep learning (mini-batch stochastic gradient descent, momentum method, accelerated gradient method)
Improving computational efficiency by applying mini-batch stochastic gradient descent, momentum, and accelerated gradient methods to deep learning for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Advanced Online Learning (2) Distributed Parallel Processing(Parallelized mini-batch stochastic gradient method, IPM, BSP, SSP)
Distributed parallel processing of online learning (parallelized mini-batch stochastic gradient method, IPM, BSP, SSP) to efficiently process large scale data for digital transformation , artificial intelligence , and machine learning tasks.