Can we use dropout and weight norm constraint with deep belief networks? How?
Theoretically is it equivalent to train an RBM with infinite number of hidden units and DBN with infinite number of layers? Would infinite depth DBN converge to something (e.g. boltzmann machine) in terms of representational power?
What is mean field approximation and when do you need to use it?
What is the difference between the training of DBN and sigmoid belief networks. Why it is easier to train and draw samples from DBN compared to sigmoid belief networks. What is the advantage of using greedy layerwise training to wake sleep algorithm?
Representation Learning 