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Instead of
taking the negative samples from the
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equilibrium
distribution, use slight corruptions of
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the datavectors..
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Much
less variance because a datavector and
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its
confabulation form a matched pair.
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Seems
to be very biased, but maybe it is
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optimizing
a different objective function.
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What
about regions far from the data that have
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high
density under the model?
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If the model is
perfect and there is an infinite
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amount of data,
the confabulations will be
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equilibrium
samples. So the shortcut will not cause
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learning to mess
up a perfect model.
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