"Diverse Particle Selection for High-Dimensional Inference in Graphical Models" - Erik Sudderth, UCI
Channel: UCIBrenICS
Date: 20/10/2017

Erik Sudderth
Associate Professor of Computer Science 
University of California, Irvine
October 20, 2017 

"Diverse Particle Selection for High-Dimensional Inference in Graphical Models"

Abstract:
Rich graphical models for real-world scene understanding encode the shape and pose of objects via high-dimensional, continuous variables. We describe a particle-based max-product inference algorithm which maintains a diverse set of posterior mode hypotheses, and is robust to initialization. At each iteration, the set of particle hypotheses is augmented via stochastic proposals, and then reduced via an optimization algorithm that minimizes distortions in max-product messages. Our particle selection metric is submodular, and thus efficient greedy algorithms have rigorous optimality guarantees. By avoiding the stochastic resampling steps underlying standard particle filters, we also avoid common degeneracies where particles collapse onto a single hypothesis. Our approach significantly outperforms previous particle-based algorithms in the estimation of human pose from images and videos, and the prediction of protein side-chain conformations. 

Speaker Bio:
Erik B. Sudderth is an Associate Professor of Computer Science at the University of California, Irvine. He received the Bachelor's degree (summa cum laude, 1999) in Electrical Engineering from the University of California, San Diego, and the Master's and Ph.D. degrees (2006) in EECS from the Massachusetts Institute of Technology. His research interests include probabilistic graphical models; nonparametric Bayesian methods; and applications of statistical machine learning in computer vision and the sciences. He received an NSF CAREER award, the ISBA Mitchell Prize, and was named one of "AI's 10 to Watch" by IEEE Intelligent Systems Magazine.