The Helmholtz Machine

DMCA

The Helmholtz Machine (1995)

Cached

Download Links

by Peter Dayan , Geoffrey E. Hinton , Radford M. Neal , Richard S. Zemel

Citations:

236 - 24 self

BibTeX

@MISC{Dayan95thehelmholtz,
    author = {Peter Dayan and Geoffrey E. Hinton and Radford M. Neal and Richard S. Zemel},
    title = { The Helmholtz Machine},
    year = {1995}
}

OpenURL

Abstract

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 parameterized 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 many ways. 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 maximizing 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.

Keyphrases

helmholtz machine hierarchical self-supervised learning fruitful approach observed pattern parameterized stochastic generative model independent draw fundamental aim structure inherent generative model combinatorial explosion statistical inference many way top-down cortical processing pathway

DMCA