  Restricted Boltzmann Machines (RBMs) are one of the fundamental building
blocks of deep learning. Approximate maximum likelihood training of RBMs
typically necessitates sampling from these models. In many training scenarios,
computationally efficient Gibbs sampling procedures are crippled by poor
mixing. In this work we propose a novel method of sampling from Boltzmann
machines that demonstrates a computationally efficient way to promote mixing.
Our approach leverages an under-appreciated property of deep generative models
such as the Deep Belief Network (DBN), where Gibbs sampling from deeper levels
of the latent variable hierarchy results in dramatically increased ergodicity.
Our approach is thus to train an auxiliary latent hierarchical model, based on
the DBN. When used in conjunction with parallel-tempering, the method is
asymptotically guaranteed to simulate samples from the target RBM. Experimental
results confirm the effectiveness of this sampling strategy in the context of
RBM training.
