The flaws in the wake-sleep algorithm
		• The recognition weights are trained to invert the generative model in parts of the space where there is no data. – This is wasteful. • The recognition weights follow the gradient of the wrong divergence. They minimize  KL(P||Q) but the variational bound requires minimization of KL(Q||P). – This leads to incorrect mode-averaging • The posterior over the top hidden layer is very far from independent because the independent prior cannot eliminate explaining away effects.