Relaxations for inference in restricted Boltzmann machines

By Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning. 2013

8 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David A. Sontag
2. Simon Lacoste-Julien
3. Rahul G. Krishnan
4. Peter Richtárik
5. Alex Smola
6. Martin Jaggi
7. Stephan Mandt
8. Bernhard Schölkopf

In Statistics and Probability

1. Simon Lacoste-Julien
2. Rahul G. Krishnan
3. David A. Sontag

In Probabilistic & Bayesian Methods

1. David A. Sontag
2. Rahul G. Krishnan
3. Simon Lacoste-Julien
4. Stephan Mandt
5. Dit-Yan Yeung
6. Hao Wang
7. Bernie Wang
8. Alex Smola

Citations in the corpus

• Barrier Frank-Wolfe for Marginal Inference (2015, background): “RBMs: As in Salakhutdinov (2008), we observe for RBMs that the bound provided by TRW(~µ; ~θ,ρ) over Lδ is loose and does not get better when optimizing over T.”