Relaxations for Inference in Restricted Boltzmann Machines
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 against other sampling-based methods.
Cite
Text
Wang et al. "Relaxations for Inference in Restricted Boltzmann Machines." International Conference on Learning Representations, 2014.Markdown
[Wang et al. "Relaxations for Inference in Restricted Boltzmann Machines." International Conference on Learning Representations, 2014.](https://mlanthology.org/iclr/2014/wang2014iclr-relaxations/)BibTeX
@inproceedings{wang2014iclr-relaxations,
title = {{Relaxations for Inference in Restricted Boltzmann Machines}},
author = {Wang, Sida I. and Frostig, Roy and Liang, Percy and Manning, Christopher D.},
booktitle = {International Conference on Learning Representations},
year = {2014},
url = {https://mlanthology.org/iclr/2014/wang2014iclr-relaxations/}
}