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A fast learning algorithm for deep belief nets Abstract 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 com-plementary priors, we derive a fast, greedy algo-rithm that can learn deep, directed belief networks one layer at a time, provided the top two lay-ers form an undirected associative memory The