Relaxations for inference in restricted Boltzmann machines

Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning

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 against other sampling-based methods.

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