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| • |
Each RBM
converts its data distribution
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into a posterior
distribution over its
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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 posterior
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distribution
over the hidden units
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back
into the data.
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– |
Task
2: Learn to model the
posterior
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distribution
over the hidden units.
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The
RBM does a good job of task 1
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and
a not so 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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posterior
distribution is closer to a
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distribution
that an RBM can model
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perfectly.
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