Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends

Christopher Tosh

2016 ICML ICML 2016

Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends

Abstract

Alternating Gibbs sampling is a modification of classical Gibbs sampling where several variables are simultaneously sampled from their joint conditional distribution. In this work, we investigate the mixing rate of alternating Gibbs sampling with a particular emphasis on Restricted Boltzmann Machines (RBMs) and variants.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning

🧭 Keyword Pioneer — mixing rate

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Christopher Tosh

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Stochastic Processes Deep Learning > Architectures > Neural Networks

Keywords

markov chain monte carlo gibbs sampling restricted boltzmann machine mixing rate alternating gibbs sampler

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