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BEGIN:VEVENT
UID:2026030606085920201204T11000020201204T12000069aa6f7b8886c@uic.edu
CATEGORIES:MEETING
STATUS:TENTATIVE
DTSTAMP:20201123T113832
DTSTART:20201204T110000
DTEND:20201204T120000
SUMMARY:Deep Generative Models and Inverse Problems
DESCRIPTION:Deep Generative Models and Inverse Problems    Presenter: Alexandros G. Dimakis, University of Texas, Austin      Abstract: Modern deep generative models like GANs, VAEs and invertible flows are achieving amazing results on modeling high-dimensional distributions, especially for images. In this lecture, you will learn how they can be used to solve inverse problems by generalizing compressed sensing beyond sparsity, extending the theory of Restricted Isometries to sets created by generative models. The general framework, new results, and open problems in this space will be presented.    Speaker bio: Alex Dimakis is a professor in the electrical and computer engineering department at the University of Texas at Austin. He holds the Archie W. Straiton Endowed Faculty Fellowship in Engineering, is the co-director of the Institute for Foundations of Machine Learning, and is the associate director of the Wireless Networking and Communications Group. He received his PhD from the University of California, Berkeley, and his bachelor's degree from the National Technical University of Athens. He received several awards including the IEEE Information Theory James Massey Award, NSF Career, a Google research award, and the Eli Jury dissertation award and the joint IEEE Information Theory and Communications Society Paper Award. His research interests include information theory, coding theory and machine learning with a current focus on unsupervised learning.    Faculty host: Daniela Tuninetti, danielat@uic.edu    This presentation will not be recorded     | Event post: https://ece.uic.edu/events?page_id=2803
LOCATION:online: https://us.bbcollab.com/collab/ui/session/guest/11c4db8fd1e64805a7a7c337715fb861   Chicago IL 60607
CLASS:PRIVATE
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