Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs

TL;DR

This paper introduces an adaptive method for Parallel Tempering in training RBMs, automating temperature selection and chain spawning to improve sampling efficiency and likelihood scores without extra computational cost.

Contribution

It proposes an automated temperature optimization and dynamic chain spawning technique for Parallel Tempering in RBM training, reducing manual tuning and computational overhead.

Findings

Improved likelihood scores on synthetic data

Automated temperature selection enhances sampling efficiency

Dynamic chain spawning reduces computational overhead

Abstract

Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, because of the intractibility of their partition function. The maximum likelihood gradient requires a very robust sampler which can accurately sample from the model despite the loss of ergodicity often incurred during learning. While using Parallel Tempering in the negative phase of Stochastic Maximum Likelihood (SML-PT) helps address the issue, it imposes a trade-off between computational complexity and high ergodicity, and requires careful hand-tuning of the temperatures. In this paper, we show that this trade-off is unnecessary. The choice of optimal temperatures can be automated by minimizing average return time (a concept first proposed by [Katzgraber et al., 2006]) while chains can be spawned dynamically, as needed, thus minimizing the computational overhead. We show on a synthetic dataset, that this results in better likelihood scores.