A fast learning algorithm for deep belief nets.

Journal article

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.

Authors: Hinton, G; Osindero, S; Teh, Y

• Publication status: Published
• Journal: Neural computation
• Volume: 18
• Issue: 7
• Pages: 1527-1554
• Publication date: 2006-07-01
• DOI: 10.1162/neco.2006.18.7.1527
• EISSN: 1530-888X
• ISSN: 0899-7667
• Language: English
• Keywords: Algorithms; Learning; Neurons; Humans; Neural Networks (Computer); Animals