Speaker: John Paisley, Assistant professor, Columbia University
Date: 14:30, August 10
Abstract: Dictionary learning with Bayesian nonparametric priors is a promising technique for sparse coding. In this talk, I will review a dictionary learning method using the beta process for nonparametric sparse coding called BPFA, and show an example application to compressed sensing MRI problem. I then discuss two new directions: Scaling inference to large data sets using a stochastic extension of a new EM algorithm for BPFA, and modeling greater structure within the data by extending BPFA to modeling subspaces. This new model, called BPSA, can be viewed as a blending of the Bayesian mixture of factor analyzers (MFA) and non-Bayesian independent subspace analysis (ISA) models.
Bio: John Paisley is an assistant professor in the Department of Electrical Engineering at Columbia University, where he is also a core member of the Data Science Institute. He received the B.S. and Ph.D. degrees in Electrical and Computer Engineering from Duke University in 2004 and 2010. From 2010 to 2013 he was a postdoc in the Computer Science departments at Princeton University and UC Berkeley. His research focuses on Bayesian methods for machine learning, including Bayesian nonparametrics and variational inference techniques. He applies these techniques to several problems in signal and information processing, including compressed sensing and topic modeling.
Copyright © School of Information Science and Engineering