probabilistic generative models

Protected: Unsupervised Learning with Gaussian Processes (2) Extension of Gaussian Process Latent Variable Model

Extension of Gaussian process latent variable models as unsupervised learning by Gaussian processes, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks ,infinite warp mixture models, Gaussian process dynamics models, Poisson point processes, log Gaussian Cox processes, latent Gaussian processes, elliptic slice sampling

2022.12.05

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology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: 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

2022.11.21

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus推論技術:inference Technology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: Calculation method for Gaussian processes based on a lattice arrangement of auxiliary points

Gaussian process method calculations based on lattice-like auxiliary point arrangements in Gaussian process models Kronecker method, Teblitz method, local kernel interpolation, KISS-GP method, an application of stochastic generative models used in digital transformation, artificial intelligence, machine learning

2022.10.24

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

Protected: Computational Methods for Gaussian Processes(2)Variational Bayesian Method and Stochastic Gradient Method

Calculations using variational Bayesian and stochastic gradient methods for Gaussian process models, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks Kullback-Leibler information content, Jensen inequality, evidence lower bound function, mini-batch method, evidence lower bound, variational posterior distribution, evidence variational lower bound

2022.10.10

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微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Equivalence of neural networks (deep learning) and Gaussian processes

On the equivalence of Gaussian processes and neural networks, an applied model of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks from Neal's paper

2022.10.01

アルゴリズム:Algorithmsベイズ推定微分積分: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: Application of Variational Bayesian Algorithm to Latent Dirichlet Models

Application of the variational Bayesian algorithm, a computational method for stochastic generative models utilized in digital transformation, artificial intelligence , and machine learning tasks, to latent Dirichlet models

2022.09.29

アルゴリズム:Algorithms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

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)

2022.09.28

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology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: Calculation of Gaussian processes (1) Calculation by the auxiliary variable method

Approximate computation of Gaussian process models, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks, using the partial data method and the auxiliary variable method

2022.09.26

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

Protected: Application of Variational Bayesian Algorithm to Mixed Gaussian Distribution Models

Application of variational Bayesian algorithms to mixed Gaussian distribution models for the computation of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (Dirichlet distribution, isotropic Gaussian distribution, free energy calculation)

2022.09.15

アルゴリズム:Algorithms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

Protected: Specific examples of graphical models

Computation of specific graphical models such as Boltzmann Machines, Mean Field Approximation, Bethe Approximation, Hidden Markov Models, Bayesian Hidden Markov Models, etc. as probabilistic generative models utilized in digital transformation, artificial intelligence and machine learning tasks.

2022.09.14

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