Skip Nav Destination
1-1 of 1
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Publisher: Journals Gateway
Neural Computation (2008) 20 (11): 2629–2636.
Published: 01 November 2008
AbstractView article PDF
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.