A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets
In this study, we provide a direct comparison of the Stochastic Maximum Likelihood algorithm and Contrastive Divergence for training Restricted Boltzmann Machines using the MNIST data set. We demonstrate that Stochastic Maximum Likelihood is superior when using the Restricted Boltzmann Machine as a...
| Main Authors: | , , , |
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| Format: | Conference item |
| Published: |
2010
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