Mode-Assisted Joint Training of Deep Boltzmann Machines

TL;DR

This paper introduces a mode-assisted training method for deep Boltzmann machines that significantly reduces the number of parameters needed for effective data representation, enhancing training efficiency and hardware applicability.

Contribution

The paper presents a novel mode-assisted training approach for DBMs, achieving superior parameter efficiency and training performance over existing methods.

Findings

DBMs trained with mode-assisted method require fewer parameters.

Mode-assisted training improves unsupervised learning of DBMs.

Parameter reduction is especially significant with fan-in network topology.

Abstract

The deep extension of the restricted Boltzmann machine (RBM), known as the deep Boltzmann machine (DBM), is an expressive family of machine learning models which can serve as compact representations of complex probability distributions. However, jointly training DBMs in the unsupervised setting has proven to be a formidable task. A recent technique we have proposed, called mode-assisted training, has shown great success in improving the unsupervised training of RBMs. Here, we show that the performance gains of the mode-assisted training are even more dramatic for DBMs. In fact, DBMs jointly trained with the mode-assisted algorithm can represent the same data set with orders of magnitude lower number of total parameters compared to state-of-the-art training procedures and even with respect to RBMs, provided a fan-in network topology is also introduced. This substantial saving in number of parameters makes this training method very appealing also for hardware implementations.