Four reasons why learning is impractical
in Boltzmann Machines
If there are many hidden layers, it can take a long time to
reach thermal equilibrium when a data-vector is clamped
on the visible units.
It takes even longer to reach thermal equilibrium in the
“negative” phase when the visible units are unclamped.
The unconstrained energy surface needs to be highly
multimodal to model the data.
The learning signal is the difference of two sampled
correlations which is very noisy.
Many weight updates are required.