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Training Restricted Boltzmann Machines with Multi-tempering: Harnessing Parallelization

  • Philemon Brakel
  • Sander Dieleman
  • Benjamin Schrauwen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

Restricted Boltzmann Machines (RBM’s) are unsupervised probabilistic neural networks that can be stacked to form Deep Belief Networks. Given the recent popularity of RBM’s and the increasing availability of parallel computing architectures, it becomes interesting to investigate learning algorithms for RBM’s that benefit from parallel computations. In this paper, we look at two extensions of the parallel tempering algorithm, which is a Markov Chain Monte Carlo method to approximate the likelihood gradient. The first extension is directed at a more effective exchange of information among the parallel sampling chains. The second extension estimates gradients by averaging over chains from different temperatures. We investigate the efficiency of the proposed methods and demonstrate their usefulness on the MNIST dataset. Especially the weighted averaging seems to benefit Maximum Likelihood learning.

Keywords

Markov Chain Monte Carlo Restricted Boltzmann Machines Neural Networks Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philemon Brakel
    • 1
  • Sander Dieleman
    • 1
  • Benjamin Schrauwen
    • 1
  1. 1.Department of Electronics and Information SystemsGhent UniversityGentBelgium

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