April 2023 | Page 3 | Deus Ex Machina

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: Regression analysis using Clojure (1) Single regression model

Regression analysis using Clojure for digital transformation, artificial intelligence, and machine learning tasks (1) Single regression model (coefficient of determination R2, correlation coefficient R, variance of residuals, variance, mean square error, explanatory variables, goodness of fit, linear regression model, dependent variable, independent variable, modeling error, heteroscedasticity, residual plot, regression line function, linear equation, regression model,incanter)

2023.04.12

Clojure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: Static type checking with mypy in Python

Static type checking with mypy in Pyhton as a base programming technology for digital transformation, artificial intelligence , and machine learning tasks Protocol, Class, inheritance relations, Structural subtyping, Nominal subtyping, type hinting, type checking, type annotation, dynamic typing language, static typing language, gradual typing

2023.04.11

プログラミング言語:Programming Language

Overview of Terraform, the infrastructure management tool, Hello World and reference books

Overview of Terraform, an infrastructure management tool used for digital transformation, artificial intelligence , and machine learning tasks, and about Hello World and reference books AWS, Setup, Hello World, Cloud Infrastructure,Environment Settings

2023.04.10

ICT技術:ICT Technology

The “Kaisha Shikiho: Industry Map” source of information on issues in DX and an example of business analysis in the manufacturing industry.

A source of information on issues in DX used for digital transformation, artificial intelligence, and machine learning tasks "Company Quarterly: Industry Map" and examples of business analysis in the manufacturing industry prototyping, workflow analysis, business issue analysis, manufacturing, KPI, KGI, OKR, KJ method, Porter s Five Competitive Analysis, PEST method, SWOT analysis

2023.04.09

ICT技術:ICT Technology課題解決:Problem solving

Artificial Intelligence Technologies Drawing Attention at Recent International Conferences

Artificial Intelligence techniques of interest in recent international conferences that are used in Digital Transformation, Artificial Intelligence and Machine Learning tasks Multimodal techniques, Federated Learning, Question and Answer Learning, Automated Machine Learning, AutoML, Few-Shot Learning, One-Shot Learning, Meta-Learning, Graph Neural Networks, GNN, Self-Supervised Learning, IJCAI, AAAI, TNNLS, CVPR, ACM SIGKDD, ICLR, NeurIPS, ICML

2023.04.08

python人工知能:Artificial Intelligence

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