288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

July 2006, Vol. 18, No. 7, Pages 1527-1554
(doi: 10.1162/neco.2006.18.7.1527)
© 2006 Massachusetts Institute of Technology
A Fast Learning Algorithm for Deep Belief Nets
Article PDF (769.31 KB)

We show how to use “complementary priors” to eliminate the explaining-away effects thatmake inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of thewake-sleep algorithm. After fine-tuning, a networkwith three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to displaywhat the associativememory has in mind.