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If we really
need distributed representations (which we
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nearly always
do), we can make inference much simpler
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by using three
tricks:
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Use
an RBM for the interactions between hidden and
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visible
variables. This ensures that the main source of
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information
wants the posterior to be factorial.
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Model
short-range temporal information by allowing
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several
previous frames to provide input to the hidden
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units
and to the visible units.
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This leads to a
temporal module that can be stacked
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So
we can use greedy learning to learn deep models
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of
temporal structure.
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