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The bottom-up
recognition weights are used to compute
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a sample from the
distribution Q over hidden
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configurations.
Q approximates the true posterior, P.
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In
each layer Q assumes the states are independent
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given
the states in the layer below. It ignores
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explaining
away.
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The changes to
the generative weights are designed to
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reduce the
average cost (i.e. energy) of generating the
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data when the
hidden configurations are sampled
from
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the approximate
posterior.
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The
updates to the generative weights follow the
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gradient
of the variational bound with respect to the
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parameters
of the model.
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