Abstract: In this talk I will briefly describe (something old) some of our recent work with hierarchical probabilistic models that are not deep neural networks. Nevertheless, these are currently among the state of the art in classification and in topic modelling: k-dependence Bayesian networks and hierarchical topic models, respectively, and both are deep models in a different sense. On deep neural networks (something new), I will describe as a point of comparison some of the state of the art applications I am familiar with: multi-task learning and learning to learn. These build on the RNNs widely used in semi-structured learning. The old and the new are remarkably different. So what are the new capabilities deep neural networks have yielded? Do we even need the old technology? What can we do next? To complete the story, I’ll introduce some efforts to combine the two approaches (something borrowed), borrowing from earlier work in statistics. Time permitting, I’ll also describe something blue.
Speaker: Wray Buntine
Affiliation: Professor of Information Technology, Monash University
Place of Seminar: Seminar Room T6, Konemiehentie 2, Aalto University