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Instead of
getting the correct probability distribution over
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rivals by
running a Markov chain until it reaches the
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equilibrium
distribution, use slight corruptions of the
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data-vectors as
the rivals (Bartlett’s
experiments)
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Learn by
modifying the weights so as to lower the
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energy of the
true data and raise the energy of the
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slightly
corrupted data.
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The learning
stops when the model’s prejudices do not
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corrupt the data
in any systematic way.
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Its
OK if unlikely data-vectors are usually corrupted
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into
more likely ones.
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This
is exactly balanced by corrupting a small
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fraction
of the likely data-vectors into unlikely ones.
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