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.
Pedram Rooshenas is a Ph.D. candidate at the University of Oregon working with Prof. Daniel Lowd. Pedram’s research interests include learning and inference in graphical models and deep structured models.
Pedram has an MSc. degree in Information Technology, with a thesis on data reduction in wireless sensor networks, from Sharif University, Tehran and an MSc. degree in Computer Science from the University of Oregon.
Pedram also maintains Libra, an open-source toolkit for learning and inference with discrete probabilistic models.