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【2】A fast learning algorithm for deep belief nets.pdf
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上传时间: 2019-08-26
详细说明： We show how to use “complementary priors” to eliminate the explainingaway 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 fo rm 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.
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