Why greedy learning works
	• Each time we learn a new layer, the inference at the layer below becomes incorrect, but the variational bound on the log prob of the data improves provided we start the learning from the tied weights that implement the complementary prior. • Now that we have a guarantee we can loosen the restrictions and still feel confident. – Allow layers to vary in size. – Do not start the learning at each layer from the weights in the layer below.