The flip-the-state transition operator for restricted Boltzmann machines

DOI10.1007/S10994-013-5390-3zbMath1293.68217OpenAlexW1964170959MaRDI QIDQ399904

Asja Fischer, Kai Brügge, Christian Igel

Publication date: 20 August 2014

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-013-5390-3

zbMATH Keywords

Markov chain Monte Carlo methodsGibbs samplingparallel temperingrestricted Boltzmann machinemixing ratecontrastive divergence learning

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items (3)

A topological insight into restricted Boltzmann machines ⋮ A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines ⋮ Algorithms for estimating the partition function of restricted Boltzmann machines

Uses Software

• SamplerCompare
• darch
• SHARK

Cites Work

• Bounding the Bias of Contrastive Divergence Learning
• Reducing the Dimensionality of Data with Neural Networks
• Training Products of Experts by Minimizing Contrastive Divergence
• Optimum Monte-Carlo sampling using Markov chains
• Markov Chains
• Justifying and Generalizing Contrastive Divergence
• Unnamed Item

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