Exponential Family Restricted Boltzmann Machines and Annealed Importance Sampling
Yifeng Li, Xiaodan Zhu · 2018
In this paper, we investigate restricted Boltzmann machines (RBMs) from the exponential family perspective, en-abling the visible units to follow any suitable distributions from the exponential family. We derive a unified view to compute the free energy function for exponential family RBMs (exp-RBMs). Based on that, annealed important sampling (AIS) is generalized to the entire exponential family, allowing for estimating the log-partition function and log-likelihood. Our experiments on a document processing task demonstrate that the generalized free energy functions and AIS estimation perform well in helping capture useful knowledge from the data; the estimated log-partition functions are stable. The appropriate instances of exp-RBMs can generate novel and meaningful samples and can be applied to classification tasks.