




















• 
Only run the
Markov chain for a few time steps.



– 
This
gets negative samples very quickly.



– 
It
works well in practice.


• 
Why does it work?



– 
If
we start at the data, the Markov chain wanders



away
from them data and towards things that it likes



more.



– 
We
can see what direction it is wandering in after only


a
few steps. It’s a big waste of time to let it go all the



way
to equilibrium.



– 
All
we need to do is lower the probability of the



“confabulations”
it produces and raise the probability



of
the data. Then it will stop wandering away.



• 
The
learning cancels out once the confabulations and the



data
have the same distribution.

