Building Probabilistic Structure into Massively Parameterized Models
, Harvard and Google Brain
Date: Wednesday, May 10, 2017
Time: 4:30 PM to 5:30 PM Note: all times are in the Eastern Time Zone
Location: 32-141 (Stata Center, 1st Floor)
Host: Stefanie Jegelka, David Sontag, MIT CSAIL
Contact: Marcia G. Davidson, 617-253-3049, email@example.com
Speaker URL: None
TALK: Building Probabilistic Structure into Massively Parameterized Models
Scientific applications of machine learning typically involve the identification of interpretable structure from high-dimensionalobservations. It is often challenging, however, to balance the flexibility required for high dimensional problems against the parsimonious structure that helps us model physical reality. I view this challenge through the lense of semiparametric modeling, in which a massively-parameterized function approximator is coupled to a compact and interpretable probabilistic model. Of particular interest in this vein is the merging of deep neural networks with graphical models containing latent variables, which enables each component to play to its strengths. I will discuss several different classes of such models, and various applications, in areas such as astronomy, chemistry, neuroscience, and sports analytics.
Ryan P. Adams is a research scientist at Google Brain and the leader of the Harvard Intelligent Probabilistic Systems group. He received his PhD in physics from Cambridge University, where he was a Gates Scholar under David J.C. MacKay before spending two years as a CIFAR Junior Fellow at the University of Toronto. He was an Assistant Professor of Computer Science at Harvard from 2011 to 2016, and will be joining the faculty at Princeton this summer. Ryan has received the Alfred P. Sloan Fellowship, the DARPA Young Faculty Award, and paper awards at ICML, UAI, and AISTATS. He was a co-founder of Whetlab, a startup acquired by Twitter in 2015. He was also the co-host of the Talking Machines podcast.
Created by Marcia G. Davidson at Tuesday, May 02, 2017 at 6:39 PM.