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Only run the
Markov chain for a few time steps.
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– |
This
gets negative samples very quickly.
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It
works well in practice.
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Why does it work?
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If
we start at the data, the Markov chain wanders
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away
from them data and towards things that it likes
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more.
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We
can see what direction it is wandering in after only
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a
few steps. It’s a big waste of time to let it go all the
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way
to equilibrium.
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All
we need to do is lower the probability of the
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“confabulations”
it produces and raise the probability
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of
the data. Then it will stop wandering away.
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• |
The
learning cancels out once the confabulations and the
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data
have the same distribution.
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