Bounding the Bias of Contrastive Divergence Learning

DOI10.1162/NECO_a_00085zbMath1226.62092WikidataQ51624398 ScholiaQ51624398MaRDI QIDQ3085329

Christian Igel, Asja Fischer

Publication date: 31 March 2011

Published in: Neural Computation (Search for Journal in Brave)

zbMATH Keywords

restricted Boltzmann machines

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Probabilistic models, generic numerical methods in probability and statistics (65C20) Learning and adaptive systems in artificial intelligence (68T05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (4)

Convergence analysis of contrastive divergence algorithm based on gradient method with errors ⋮ The flip-the-state transition operator for restricted Boltzmann machines ⋮ Training restricted Boltzmann machines: an introduction ⋮ A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines

Cites Work

• Reducing the Dimensionality of Data with Neural Networks
• Training Products of Experts by Minimizing Contrastive Divergence
• Markov Chains
• Justifying and Generalizing Contrastive Divergence
• A Fast Learning Algorithm for Deep Belief Nets

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