An apparently crazy idea
	• Its hard to learn complicated models like Sigmoid Belief Nets because its hard to infer (or sample from) the posterior distribution over hidden configurations. • Crazy idea: do inference wrong. – Maybe learning will still work – This turns out to be true for SBN’s. • At each hidden layer, we assume the posterior over hidden configurations factorizes into a product of distributions for each separate hidden unit.