Paper
The Helmholtz Machine
Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterised stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially manyways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns, We describe a way of finessing this combinatorial explosion by maximising an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways. 1 Introduction Following Helmholtz, we view the human perceptual system as a statistical inference engine whose function is to infer the probable causes of sensory input. We show that a device of this kind can learn h...
Authors: Peter Dayan · Geoffrey E. Hinton · Radford M. Neal · Richard S. Zemel