I was studying Deep Belief Network (DBN) and have questions.
1) According to the definition of DBN, DBN is formed by stacking RBM on top of each other such that the hidden layer in a lower layer becomes the input layer in the above layer. However, when I read the papers by Geoff Hinton (for example, "a fast learning algorithm for deep belief nets"), his DBN doesn't seem to have multiple RBMs. His DBN has only one RBM (i.e., undirected Markov Random Field with two layers) which sits on top while the other layers have only directional edges. I am confused by this difference.
Are these two architectures essentially same? When the term, "DBN" is used in literatures, does it refer to Geoff Hinton's DBN? Or does it refer to a general class of multi-layer architectures with one or multiple RBMs.
2) Following up the above question, does anybody know why Geoff Hinton's DBN has undirected RBM (called associative memory) on top? What role does it play? What would happen if, instead of undirected edges, directed edges are used on top?