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UCL

Varieties of Helmholtz machine

Peter Dayan and Geoffrey Hinton
Department of Computer Science
University of Toronto

Abstract

The Helmholtz machine is a new unsupervised learning architecture that uses top-down connections to build probability density models of input and and bottom up connections to build inverses to those models. The wake-sleep learning algorithm for the machine involves just the purely local delta rule. This paper suggests a number of different varieties of Helmholtz machines, each with its own strengths and weaknesses, and relates them to cortical information processing.

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Neural Networks, (1996) 9 1385-1403

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