LISP Considerations for a program for solving algebraic sentences
Web Technology Digital Transformation Artificial Intelligence Natural Language Processing Semantic Web Deep Learning Onl...
数理論理学:Mathematical logic
LISP python Mathematical logic and the GNN
Machine Learning Artificial Intelligence Natural Language Processing Semantic Web Python Collecting AI Conference Papers...
アルゴリズム:Algorithms Overview of combinatorial optimization and libraries and reference books for implementation
Machine Learning Artificial Intelligence Algorithm Digital Transformation Deep Learning Mathematics Probabilistic Genera...
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: Explainable Machine Learning (17) Counterfactual Explanations
Explanation of machine learning results by counterfactual explanations utilized in digital transformation, artificial intelligence, and machine learning tasks Anchor, Growing Spheres algorithm, Python, Alibi, categorical features, Rashomon effect, LIME, fully coupled neural networks, counterfactual generation algorithms, Euclidean distance, central absolute deviation, Nelder-Mead method, causal semantics, causes
推論技術:inference Technology Overview and Implementation of the Satisfiability Determination Problem (SAT: Boolean SAtisfiability) of Propositional Logic
Overview and implementation of the satisfiability decision problem (SAT: Boolean SAtisfiability) for propositional logic, which is used in digital transformation, artificial intelligence, and machine learning tasks Clojure Rollingstones, Pyhton, PySAT, z3-solver, C++, 2-SAT, game AI, natural language processing acceleration, combinatorial optimization problem efficiency, hyperparameter optimization, computer security, automatic software specification verification, automatic chip design verification, zChaff, WalkSAT, GRASP, CryptoMiniSat, MapleSAT, Scavel, PicoSAT, MiniSAT, CaDiCaL, Lingeling, Glucose, P≠NP prediction, logic problems
アルゴリズム:Algorithms 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
アルゴリズム:Algorithms Protected: Unsupervised Learning with Gaussian Processes (1)Overview and Algorithm of Gaussian Process Latent Variable Models
Overview and algorithms of unsupervised learning using Gaussian Process Latent Variable Models GPLVM, an application of probabilistic generative models used in digital transformation, artificial intelligence, and machine learning, Bayesian Gaussian Process Latent Variable Models ,Bayesian GPLVM
Symbolic Logic Integration of logic and rules with probability/machine learning
Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.
アルゴリズム:Algorithms Protected: Maximum Propagation Method for Calculating MAP Assignments in Graphical Models
Estimating the maximized state of probability values (MAP assignment) with the maximum propagation method in probabilistic generative models used in digital transformation, artificial intelligence, and machine learningtasks (TRW maximum propagation method, STA condition, maximum propagation method on a factor graph with cycles, maximum propagation on a tree graph, MAP estimation by message propagation)