14 search results - page 1 / 3 » Deep Belief Networks Are Compact Universal Approximators |
NECO
2010
13 years 5 months ago
2010
NECO
2008
13 years 7 months ago
2008
In this paper we show that exponentially deep belief networks [3, 7, 4] can approximate any distribution over binary vectors to arbitrary accuracy, even when the width of each lay...
NECO
2008
13 years 7 months ago
2008
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
NIPS
2007
13 years 8 months ago
2007
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...