A shortcut
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.