Paper
Deep Belief Nets.
Deep belief nets are probabilistic generative models that are composed of multiple layers of stochastic latent variables (also called “feature detectors” or “hidden units”). The top two layers have undirected, symmetric connections between them and form an associative memory. The lower layers receive top-down, directed connections from the layer above. Deep belief nets have two important computational properties. First, there is an efficient procedure for learning the topdown, generative weights that specify how the variables in one layer determine the probabilities of variables in the layer below. This procedure learns one layer of latent variables at a time. Second, after learning multiple layers, the values of the latent variables in every layer can be inferred by a single, bottom-up pass that starts with an observed data vector in the bottom layer and uses the generative weights in the reverse direction.
Authors: Geoffrey E. Hinton