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The Analects of Confucius, a book of comprehensive “anthropology

Confucius' Analects: A Comprehensive Book of "Anthropology" (Communist Party, Chōkyū, Confucianism, Analects and Arithmetic, Shibusawa Eiichi, Loyalty, Filial Piety, Civility, Yushima Seido, Kodokan, Spring and Autumn Period)

2023.01.28

中国古典:classics哲学:philosophy組織活性化:revitalize an organization課題解決:Problem solving

Protected: Hedge Algorithm and Exp3 Measures in the Adversary Bandid Problem

Hedge algorithm and Exp3 measures in adversarial bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks pseudo-regret upper bound, expected cumulative reward, optimal parameters, expected regret, multi-armed bandit problem, Hedge Algorithm, Expert, Reward version of Hedge algorithm, Boosting, Freund, Chabile, Pseudo-Code, Online Learning, PAC Learning, Question Learning

2023.01.27

アルゴリズム:Algorithmsバンディッド問題強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Representation Theorems and Rademacher Complexity as the Basis for Kernel Methods in Statistical Mathematics Theory

Representation theorems and Rademacher complexity as a basis for kernel methods in statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks Gram matrices, hypothesis sets, discriminant bounds, overfitting, margin loss, discriminant functions, predictive semidefiniteness, universal kernels, the reproducing kernel Hilbert space, prediction discriminant error, L1 norm, Gaussian kernel, exponential kernel, binomial kernel, compact sets, empirical Rademacher complexity, Rademacher complexity, representation theorem

2023.01.26

アルゴリズム:Algorithmsグラフ理論幾何学:Geometry微分積分:Calculus数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Batch Stochastic Optimization – Stochastic Variance-Reduced Gradient Descent and Stochastic Mean Gradient Methods

Batch stochastic optimization for digital transformation, artificial intelligence, and machine learning tasks - stochastic variance reduced gradient descent and stochastic mean gradient methods (SAGA, SAG, convergence rate, regularization term, strongly convex condition, improved stochastic mean gradient method, unbiased estimator, SVRG, algorithm, regularization, step size, memory efficiency, Nekaterov's acceleration method, mini-batch method, SDCA)

2023.01.25

アルゴリズム:Algorithms幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Gauss-Newton and natural gradient methods as continuous optimization for machine learning

Gauss-Newton and natural gradient methods as continuous machine learning optimization for digital transformation, artificial intelligence, and machine learning tasks Sherman-Morrison formula, one rank update, Fisher information matrix, regularity condition, estimation error, online learning, natural gradient method, Newton method, search direction, steepest descent method, statistical asymptotic theory, parameter space, geometric structure, Hesse matrix, positive definiteness, Hellinger distance, Schwarz inequality, Euclidean distance, statistics, Levenberg-Merkert method, Gauss-Newton method, Wolf condition

2023.01.24

アルゴリズム:Algorithms幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Approximate computation of various models in machine learning by Bayesian inference

Approximate computation of various models in machine learning using Bayesian inference for digital transformation, artificial intelligence, and machine learning tasks (structured variational inference, variational inference algorithms, mixture models, conjugate prior, KL divergence, ELBO, evidence lower bound, collapsed Gibbs sampling, blocking Gibbs sampling, approximate inference)

2023.01.23

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

ISWC2022Papers

ISWC2022Papers From ISWC2022, an international conference on Semantic Web technologies, one of the artificial ...

2023.01.22

web技術:web technologyセマンテックウェブ技術:Semantic web Technology機械学習:Machine Learning

Various approaches to realize optical computers

Various approaches to realize optical computers to exploit digital transformation, artificial intelligence, and machine learning tasks (fractals, ultra-high definition spatial light modulators, stereoscopic images, self-reproducing software, self-reproducing hardware, Spatial Light Modulator, SLM, Spatial Modulator, Colloidal diamond, Plastic, Photonic crystal, Diamond, 5D data storage, Nanostructure, Superman Memory Crystal, Superman, Fortress of Solitude)

2023.01.21

コンピューターテクノロジー:Technology読書

Protected: Explainable Artificial Intelligence (12) Model-Independent Interpretation (Global Surrogate)

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2023.01.20

推論技術:inference Technology機械学習:Machine Learning

Protected: Application of Neural Networks to Reinforcement Learning Value Function Approximation, which implements value evaluation as a function with parameters.

Application of Neural Networks to Reinforcement Learning used for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Examples of implementing value evaluation with functions with parameters (CartPole, Q-table, TD error, parameter update, Q-Learning, MLPRegressor, Python)

2023.01.19

アルゴリズム:Algorithmsグラフ理論幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra