Learning Deep Belief Nets
It is easy to generate an
unbiased example at the
leaf nodes, so we can see
what kinds of data the
network believes in.
It is hard to infer the
posterior distribution over
all possible configurations
of hidden causes.
It is hard to even get a
sample from the posterior.
So how can we learn deep
belief nets that have
millions of parameters?
stochastic
hidden
cause
visible
effect