テクノロジー:Technology Technology Discussion – A look at some of the latest technologies
Technology Discussion - A look at some of the latest technologies
Introduction
This section discusses v...
人工知能:Artificial Intelligence
テクノロジー:Technology アルゴリズム:Algorithms Protected: Explainable Machine Learning (19)prototype and criticism
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アルゴリズム:Algorithms Protected: Research Trends in Deep Reinforcement Learning: Meta-Learning and Transfer Learning, Intrinsic Motivation and Curriculum Learning
Research trends in deep reinforcement learning for digital transformation, artificial intelligence, and machine learning tasks: meta-learning and transfer learning, intrinsic motivation and curriculum learning automatic curriculum generation, automatic task decomposition, task difficulty adjustment, intrinsic reward, robot domain transformation, robot domain transformation, simulator to simulator transfer learning, BERT, Metric/Representation Base, Memory/Knowledge Base, active learning, meta-learning, and robot domain transformation) Robot domain transformation, transfer learning from simulators, BERT, Model-Agnostic Meta-Learning, Active Learning, Metric/Representation Base, Memory/Knowledge Base, Weigh Base, and Learning to Optimize
アルゴリズム:Algorithms Protected: Optimal arm bandit and Bayesian optimal when the player’s candidate actions are huge or continuous (2)
Bayesian optimization for digital transformation, artificial intelligence, machine learning tasks and bandit when player behavior is massive/continuous Markov chain Monte Carlo, Monte Carlo integration, turn kernels, scale parameters, Gaussian kernels, covariance function parameter estimation, Simultaneous Optimistic Optimazation policy, SOO strategy, algorithms, GP-UCB policy, Thompson's law, expected value improvement strategy, GP-UCB policy
アルゴリズム:Algorithms Protected: Sparse Machine Learning with Overlapping Sparse Regularization
Sparse machine learning with overlapping sparse regularization for digital transformation, artificial intelligence, and machine learning tasks main problem, dual problem, relative dual gap, dual norm, Moreau's theorem, extended Lagrangian, alternating multiplier method, stopping conditions, groups with overlapping L1 norm, extended Lagrangian, prox operator, Lagrangian multiplier vector, linear constraints, alternating direction multiplier method, constrained minimization problem, multiple linear ranks of tensors, convex relaxation, overlapping trace norm, substitution matrix, regularization method, auxiliary variables, elastic net regularization, penalty terms, Tucker decomposition Higher-order singular value decomposition, factor matrix decomposition, singular value decomposition, wavelet transform, total variation, noise division, compressed sensing, anisotropic total variation, tensor decomposition, elastic net
機械学習:Machine Learning Linear Algebra Overview and Library and Reference Books
Linear Algebra and Machine Learning
Linear algebra is a field of mathematics that uses vectors and matrices to...
アルゴリズム:Algorithms Protected: Optimization for the main problem in machine learning
Optimization for main problems in machine learning used in digital transformation, artificial intelligence, and machine learning tasks (barrier function method, penalty function method, globally optimal solution, eigenvalues of Hesse matrix, feasible region, unconstrained optimization problem, linear search, Lagrange multipliers for optimality conditions, integration points, effective constraint method)
アルゴリズム:Algorithms Protected: Applied Bayesian inference in non-negative matrix factorization: model construction and inference
Non-negative matrix factorization as a construction and inference of applied Bayesian inference models used in digital transformation, artificial intelligence, and machine learning tasks Poisson distribution, latent variable, gamma distribution, approximate posterior distribution, variational inference, spectogram of organ performance data, missing value interpolation, restoration of high frequency components, super-resolution, graphical models, hyperparameters, modeling, auxiliary variables, linear dimensionality reduction, recommendation algorithms, speech data, Fast Fourier Transform, natural language processing
アルゴリズム:Algorithms Protected: Implementation of two approaches to improve environmental awareness, a weak point of deep reinforcement learning.
Implementation of two approaches to improve environment awareness, a weakness of deep reinforcement learning used in digital transformation, artificial intelligence, and machine learning tasks (inverse predictive, constrained, representation learning, imitation learning, reconstruction, predictive, WorldModels, transition function, reward function Weaknesses of representation learning, VAE, Vision Model, RNN, Memory RNN, Monte Carlo methods, TD Search, Monte Carlo Tree Search, Model-based learning, Dyna, Deep Reinforcement Learning)
アルゴリズム:Algorithms Protected: Regression analysis using Clojure (2) Multiple regression model
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