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Each RBM
converts its data distribution
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into an
aggregated posterior distribution
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over its hidden
units.
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This divides the
task of modeling its
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data into two
tasks:
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Task
1: Learn generative weights
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that
can convert the aggregated
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posterior
distribution over the hidden
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units
back into the data distribution.
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– |
Task
2: Learn to model the
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aggregated
posterior distribution
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over
the hidden units.
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The
RBM does a good job of task 1
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and
a moderately good job of task 2.
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Task 2 is easier
(for the next RBM) than
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modeling the
original data because the
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aggregated
posterior distribution is
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closer to a
distribution that an RBM can
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model perfectly.
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