Seminar • Artificial Intelligence — Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Abdullah Rashwan, PhD candidate
David R. Cheriton School of Computer Science
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network.