アルゴリズム:Algorithms

Protected: Overview of Weaknesses and Countermeasures in Deep Reinforcement Learning and Two Approaches to Improve Environment Recognition

An overview of the weaknesses and countermeasures of deep reinforcement learning utilized in digital transformation, artificial intelligence, and machine learning tasks and two approaches of improving environmental awareness Mixture Density Network, RNN, Variational Auto Encoder, World Modles, Expression Learning, Strategy Network Compression, Model Free Learning, Sample-Based Planning Model, Dyna, Simulation-Based, Sample-Based, Gaussian Process, Neural Network, Transition Function, Reward Function) World Modles, Representation Learning, Strategy Network Compression, Model-Free Learning, Sample-Based Planning Model, Dyna, Simulation-Based, Sample-Based, Gaussian Process, Neural Network, Transition Function, Reward Function, Simulator , learning capability, transition capability

2023.04.13

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学: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

Protected: Thompson Sampling, linear bandit problem on a logistic regression model

Thompson sampling, linear bandit problem on logistic regression models utilized in digital transformation, artificial intelligence, and machine learning tasks (Thompson sampling, maximum likelihood estimation, Laplace approximation, algorithms, Newton's method, negative log posterior probability, gradient vector, Hesse matrix, Laplace approximation, Bayesian statistics, generalized linear models, Lin-UCB measures, riglet upper bound)

2023.04.07

アルゴリズム:Algorithmsグラフ理論スパースモデリングバンディッド問題幾何学: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

Protected: Sparse learning based on group L1 norm regularization

Sparse machine learning based on group L1-norm regularization for digital transformation, artificial intelligence, and machine learning tasks relative dual gap, dual problem, gradient descent, extended Lagrangian function, dual extended Lagrangian method, Hessian, L1-norm regularization, and group L1-norm regularization, dual norm, empirical error minimization problem, prox operator, Nesterov's acceleration method, proximity gradient method, iterative weighted reduction method, variational representation, nonzero group number, kernel weighted regularization term, concave conjugate, regenerative kernel Hilbert space, support vector machine, kernel weight Multi-kernel learning, basis kernel functions, EEG signals, MEG signals, voxels, electric dipoles, neurons, multi-task learning

2023.04.06

アルゴリズム:Algorithmsグラフ理論スパースモデリングスパースモデリング微分積分: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: Optimality conditions for equality-constrained optimization problems in machine learning

Optimality conditions for equality-constrained optimization problems in machine learning utilized in digital transformation, artificial intelligence, and machine learning tasks (inequality constrained optimization problems, effective constraint method, Lagrange multipliers, first order independence, local optimal solutions, true convex functions, strong duality theorem, minimax theorem, strong duality, global optimal solutions, second order optimality conditions, Lagrange undetermined multiplier method, gradient vector, first order optimization problems)

2023.04.05

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学: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

Protected: Discriminant Conformal Losses in Multi-Valued Discriminant by Statistical Mathematics Theory and its Application to Various Loss Functions

Discriminant conformal loss of multi-valued discriminant and its application to various loss functions by statistical mathematics theory utilized in digital transformation, artificial intelligence, and machine learning tasks discriminant model loss, discriminant conformal, narrow order preserving properties, logistic model, maximum likelihood estimation, nonnegative convex function, one-to-other loss, constrained comparison loss, convex nonnegative-valued functions, hinge loss, pairwise comparison loss, multivalued surport vector machine, monotone nonincreasing function, predictive discriminant error, predictive ψ-loss, measurable function

2023.04.04

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学: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

Protected: Bayesian inference by variational and collapsed Gibbs sampling of Gaussian mixture models

Bayesian inference with variational and collapsed Gibbs sampling of Gaussian mixture models utilized in digital transformation, artificial intelligence, and machine learning tasks inference algorithms, analytic integral approximation, complex models, Gauss-Wishart distribution, clustering, multi-dimensional Student's t-distribution, categorical distribution, Poisson mixture models, Dirichlet distribution, approximate posterior distribution, latent variables

2023.04.03

アルゴリズム:Algorithmsグラフ理論スパースモデリングベイズ推定幾何学: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

Protected: Explainable Artificial Intelligence (16) Model independent interpretation (SHAP (SHapley Additive exPlanations))

Model independent interpretation with SHAP as an explainable artificial intelligence used for digital transformation, artificial intelligence and machine learning tasks scikit-learn, xgboost, LightGBM, tree boosting, R, shapper, fastshap, TreeSHAP, KernelSHAP, partial dependence plot, permutation feature importance, feature importance, feature dependence, interactions, clustering, summary plots clustering, summary plots, atomic unit, LIME, decision tree, game theory, clustering, SHAP interaction values, ALE plot, image mapping, consistency, missing, local correctness, efficiency, symmetry, dummyness, additivity, SHapley Additive exPlanations, local surrogate models

2023.03.31

アルゴリズム:Algorithms説明できる機械学習

Protected: Value Assessment and Policy and Weaknesses in Deep Reinforcement Learning

Value assessment and strategies and weaknesses in deep reinforcement learning used for digital transformation, artificial intelligence, and machine learning tasks poor sample efficiency, difficulty in validating methods as well, impact of implementation practices on performance, library initial values, poor reproducibility, over-training, local optimum, dexterity, TRPO, PPO, continuous value control, image control, policy-based, value-based

2023.03.30

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学: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

Parallel and Distributed Processing in Machine Learning

Parallel and Distributed Processing in Machine Learning The learning process of machine learning requires hi...

2023.03.26

Large-Scaleデータアルゴリズム:Algorithmsオンライン学習分散並列処理機械学習:Machine Learning非同期/並行処理:Asynchronous/parallel processing

Protected: Linear Bandit, Contextual Bandit, Linear Bandit Problem with LinUCB Policies

Linear Bandit, Contextual Bandit, LineUCB policy for linear bandit problems (Riglet, algorithm, least squares quantification, LinUCB score, reward expectation, point estimate, knowledge) utilized in digital transformation, artificial intelligence, machine learning tasks utilization-oriented measures, search-oriented measures, Woodbury's formula, LinUCB measures, LinUCB policy, contextual bandit, website optimization, maximum sales expectation, bandit optimal budget allocation)

2023.03.24

アルゴリズム:Algorithmsグラフ理論スパースモデリングバンディッド問題幾何学:Geometry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