A fast learning algorithm for deep belief nets

Neural Computation 2006 Geoffrey E. HintonSimon OsinderoYee-Whye Teh

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... (read more)

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