Date: 5:00pm, March 23, 2017
Location: CEPSR Davis Auditorium
Speaker: John Paisley, Assistant Professor of Electrical Engineering
Abstract: Advances in scalable machine learning have made it possible to learn highly structured models on large data sets. In this talk, I will discuss some of our recent work in this direction. I will first briefly review scalable probabilistic topic modeling with stochastic variational inference. I will then then discuss two structured developments of the LDA model in the form of tree-structured topic models and graph-structured topic models. I will present our recent work in each of these areas.