Relaxations for inference in restricted Boltzmann machines

International Conference on Learning Representations, 2013.

Cited by: 6|Bibtex|Views34
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field. We experiment on MAP inference tasks in several restricted Boltzmann machines. We also use our underlying sampler to estimate the log-partition function of restricted Boltzmann machines and compare...More

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