Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 2008, Vol. 20, No. 11, Pages 2629-2636
(doi: 10.1162/neco.2008.12-07-661)
© 2008 Massachusetts Institute of Technology
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators
Article PDF (82.48 KB)
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.