Learning Tractable Graphical Models

Date and time: 
Tuesday, March 7, 2017 - 11:00
Location: 
220 Deschutes
Author(s):
Pedram Rooshenas
University of Oregon
Host/Committee: 
  • Daniel Lowd (Chair)
  • Dejing Dou
  • Chris Wilson
  • John Conery (Biology)
  • Yashar Ahmadian (Biology)

Abstract

Probabilistic graphical models have been successfully applied to a wide variety of fields such as computational biology, computer vision, natural language processing, robotics, and many more. However, in probabilistic models for many real-world domains, exact inference is intractable, and approximate inference may be inaccurate. In this talk, we discuss how we can learn tractable models such as arithmetic circuits (ACs) and sum-product networks (SPNs), in which marginal and conditional queries can be answered efficiently. We also discuss how we can learn these tractable graphical models in a discriminative setting, in particular through introducing Generalized ACs, which combines ACs and neural networks.