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 l...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2006
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