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Varieties of Helmholtz machine

Dayan, P; Hinton, GE; (1996) Varieties of Helmholtz machine. NEURAL NETWORKS , 9 (8) 1385 - 1403.

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Abstract

The Helmholtz machine is a new unsupervised learning architecture that uses top-down connections to build probability density models of input 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. Copyright (C) 1996 Elsevier Science Ltd.

Type: Article
Title: Varieties of Helmholtz machine
Keywords: expectation-maximization, unsupervised learning, feedback connections, NEURAL-NETWORK, VISUAL-CORTEX, EM ALGORITHM, MODEL, CAT
UCL classification: UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
URI: http://discovery.ucl.ac.uk/id/eprint/85105
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