Biased Monte Carlo sampling in RBMs

RBMs are generative models capable of fitting complex dataset's probability distributions. Thanks to their simple structure, they are particularly well suited for interpretability and pattern extraction, a feature particularly appealing for scientific use. Yet, in practice, it is hard to extract good equilibrium models for structured datasets (which are the standard case in most biologically interesting datasets) due to a divergence of the Monte Carlo mixing times. In this work, we show this barrier can be easily surmounted using biased Monte Carlo methods, just as commonly done in Statistical Mechanics, to reach equilibrium in the vicinity of first order phase transitions.