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