A fast learning algorithm for deep belief nets.
We show how to use "complementary priors" to eliminate the explaining-away effects that make 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 the wake-sleep algorithm. After fine-tuning, a network with 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 display what the associative memory has in mind.
Hinton
Geoffrey E
GE
Department of Computer Science, University of Toronto, Canada. hinton@cs.toronto.edu
Osindero
Simon
S
Teh
Yee-Whye
YW
D000465:Algorithms; D000818:Animals; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*