Machine Learning, Probabilistic Modelling, Rbm, Deep Belief Network.
inference shaper posterior distribution (according to Hilton paper). In Figure 1, RBM contains two layers of units, the visible units from x_1 , x_2 , . . . x_m stand for m data input features, and the n binary hidden units including h_1 , h_2 , . . . h_n to capture the dependencies between