Training restricted Boltzmann machines: An introduction

  title={Training restricted Boltzmann machines: An introduction},
  author={Asja Fischer and C. Igel},
  journal={Pattern Recognit.},
  • Asja Fischer, C. Igel
  • Published 2014
  • Computer Science
  • Pattern Recognit.
  • Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. [...] Key Method Different learning algorithms for RBMs, including contrastive divergence learning and parallel tempering, are discussed. As sampling from RBMs, and therefore also most of their learning algorithms, are based on Markov chain Monte Carlo (MCMC) methods, an introduction to Markov chains and MCMC techniques is provided. Experiments demonstrate relevant aspects of RBM…Expand Abstract
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    An Introduction to Restricted Boltzmann Machines
    • 367
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