Generative model of autoencoders self-learning on images represented by count samples

DOI10.1134/S00051179220120098MaRDI QIDQ2691542

V. E. Antsiperov

Publication date: 29 March 2023

Published in: Automation and Remote Control (Search for Journal in Brave)

zbMATH Keywords

autoencoderEM algorithmmachine learningmixture of distributionsreceptive fieldgenerative model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)

Uses Software

• darch

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Self-organized formation of topologically correct feature maps
• Saddlepoint methods and statistical inference. With comments and a rejoinder by the author
• R. A. Fisher and the making of maximum likelihood 1912--1922
• Bayesian analysis of finite mixture models of distributions from exponential families
• Rényi Divergence and Kullback-Leibler Divergence
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• The EM Algorithm and Extensions, 2E
• Image deblurring with Poisson data: from cells to galaxies
• Least squares quantization in PCM
• 10.1162/153244303322753661
• Iterative Solution of Nonlinear Equations in Several Variables
• A Fast Learning Algorithm for Deep Belief Nets

This page was built for publication: Generative model of autoencoders self-learning on images represented by count samples