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Journal Articles

Deep, Narrow Sigmoid Belief Networks Are Universal Approximators

Ilya Sutskever, Geoffrey E. Hinton
Publisher: Journals Gateway
Neural Computation (2008) 20 (11): 2629–2636.
DOI: https://doi.org/10.1162/neco.2008.12-07-661
Published: 01 November 2008
Abstract
In this note, we show that exponentially deep belief networks can approximate any distribution over binary vectors to arbitrary accuracy, even when the width of each layer is limited to the dimensionality of the data. We further show that such networks can be greedily learned in an easy yet impractical way.
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