• Instead of trying to explicitly extract the coordinates of a datapoint on the manifold, map the datapoint  to an energy valley in a high-dimensional space. • The learned energy function in the high-dimensional space restricts the available configurations to a low- dimensional manifold. – We do not need to know the manifold dimensionality in advance and it can vary along the manifold. – We do not need to know the number of manifolds. – Different manifolds can share common structure. • But we cannot create the right energy valleys by direct interactions between pixels. – So learn a multilayer non-linear mapping between the data and a high-dimensional latent space in which we can construct the right valleys.