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A fast learning algorithm for deep belief nets

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Academic year: 2017

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Figure 1: The network used to model the joint distributionof digit images and digit labels
Figure 4: This depicts a Markov chain that uses alternatingGibbs sampling. In one full step of Gibbs sampling, the hid-den units in the top layer are all updated in parallel by apply-ing Eq
Figure 5: A hybrid network. The top two layers have undi-rected connections and form an associative memory
Figure 6: All 49 cases in which the network guessed right buthad a second guess whose probability was within 0.3 of theprobability of the best guess
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A fast learning algorithm for deep belief nets

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