















• 
Instead of
taking the negative samples from the




equilibrium
distribution, use slight corruptions of



the datavectors.
Only add random momentum



once, and only
follow the dynamics for a few steps.



– 
Much
less variance because a datavector and



its
confabulation form a matched pair.




– 
Seems
to be very biased, but maybe it is




optimizing
a different objective function.



• 
If the model is
perfect and there is an infinite



amount of data,
the confabulations will be



equilibrium
samples. So the shortcut will not cause




learning to mess
up a perfect model.

