Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann MachinesDownload PDF

Published: 09 Nov 2021, Last Modified: 05 May 2023NeurIPS 2021 PosterReaders: Everyone
Keywords: Restricted Boltzmann Machine, Out of Equilibrium, Monte Carlo mixing time, Unsupervised learning, Energy Based Model, Disordered Systems
TL;DR: Restricted Boltzmann Machines operate in 2 distinct regimes: equilibrium and out-of-equilibrium. In the former, the statistical properties of the dataset are encoded in the Gibbs measure. In the latter, they are recovered through a dynamical process.
Abstract: Training Restricted Boltzmann Machines (RBMs) has been challenging for a long time due to the difficulty of computing precisely the log-likelihood gradient. Over the past decades, many works have proposed more or less successful recipes but without studying systematically the crucial quantity of the problem: the mixing time i.e. the number of MCMC iterations needed to sample completely new configurations from a model. In this work, we show that this mixing time plays a crucial role in the behavior and stability of the trained model, and that RBMs operate in two well-defined distinct regimes, namely equilibrium and out-of-equilibrium, depending on the interplay between this mixing time of the model and the number of MCMC steps, $k$, used to approximate the gradient. We further show empirically that this mixing time increases along the learning, which often implies a transition from one regime to another as soon as $k$ becomes smaller than this time. In particular, we show that using the popular $k$ (persistent) contrastive divergence approaches, with $k$ small, the dynamics of the fitted model are extremely slow and often dominated by strong out-of-equilibrium effects. On the contrary, RBMs trained in equilibrium display much faster dynamics, and a smooth convergence to dataset-like configurations during the sampling. Finally, we discuss how to exploit in practice both regimes depending on the task one aims to fulfill: (i) short $k$s can be used to generate convincing samples in short learning times, (ii) large $k$ (or increasingly large) must be used to learn the correct equilibrium distribution of the RBM. Finally, the existence of these two operational regimes seems to be a general property of energy based models trained via likelihood maximization.
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Supplementary Material: pdf
Code: https://github.com/AurelienDecelle/TorchRBM
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