数学:Mathematics

Protected: Computation of graphical models with hidden variables

Parameter learning of graphical models with hidden variables using variational EM algorithm in stochastic generative models (wake-sleep algorithm, MCEM algorithm, stochastic EM algorithm, Gibbs sampling, contrastive divergence method, constrained Boltzmann machine, EM algorithm, KL divergence)

2022.09.01

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 Matrix Decomposition Models with Missing Values

Application of variational Bayesian algorithm to matrix factorization models with missing values as a stochastic generative model computation for use in digital transformation, artificial intelligence, and machine learning tasks

2022.08.31

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 Nonparametric Bayesian Structural Change Estimation

Nonparametric Bayesian structural change estimation using Gibbs sampling as an application of probabilistic generative models for digital transformation, artificial intelligence, and machine learning tasks

2022.08.30

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: Stochastic Generative Models and Gaussian Processes(2)Maximum Likelihood and Bayesian Estimation

Maximum Likelihood and Bayesian Estimation Overview for Probabilistic Generative Models and Gaussian Process Fundamentals Used in Digital Transformation, Artificial Intelligence, and Machine Learning Tasks

2022.08.29

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

Clojure and Python integration and machine learning

Implementation of a library (libpython-clj) and sample code (transformers, lime, autoencoder, etc.) for integration with Clojure for Python modules used for digital transformation, artificial intelligence and machine learning tasks

2022.08.25

ClojurepythonRアルゴリズム:Algorithms数学:Mathematics機械学習:Machine Learning

GPy – A Python-based framework for Gaussian processes

GPy Gaussian regression problem, auxiliary variable method, sparse Gaussian regression, Bayesian GPLVM, latent variable model with Gaussian processes, a Python-based implementation of Gaussian processes, an application of stochastic generative models used in digital transformation, artificial intelligence and machine learning tasks.

2022.08.24

pythonアルゴリズム:Algorithmsスパースモデリングスパースモデリング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集合論:Set theory

Implementation of Gaussian Processes in Clojure

Implementation of Gaussian processes in Clojure using fastmath as an extension of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.08.23

ClojureJavaアルゴリズム:Algorithms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

Protected: Support Vector Machines for Weak Label Learning (1) Semi-supervised Learning

Weak label learning (semi-supervised learning where label information is given only for a subset of training cases) as an application of support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.08.19

アルゴリズム:Algorithms微分積分:Calculus数理論理学最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics線形代数:Linear Algebra自然言語処理:Natural Language Processing音声信号認識技術

Protected: Application of Variational Bayesian Algorithm to Matrix Decomposition Models

Variational Bayesian learning and empirical variational Bayesian learning algorithms for matrix factorization models as computational methods for stochastic generative models utilized in digital transformation , artificial intelligence , and machine learning tasks

2022.08.18

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: Computation of graphical models without hidden variables

Maximum likelihood, Bayesian, and variational computations of graphical models without hidden variables in probabilistic generative models utilized in digital truss formation, artificial intelligence, and machine learning tasks, learning by the pseudolikelihood function, Bethe approximation, parameter estimation by TRW upper bound, variational methods, entropy functions, IPF algorithm, MAP estimators

2022.08.17

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