Hi all,

we have another tea-talk next week. This talk will be given by Professor Alexandros Dimakis from the University of Texas at Austin. The talk will take place at AA6214 at 11AM on June 15th (Thursday).

Please NOTE that there is a tea-talk on 13th (Tuesday) by Jackie C. K. Cheung (McGill University) at Z109 (Pavillon Claire-McNicoll) at 2PM. Check Dima's email for more detailed information about the talk.

Alexandros' talk.

Title: Generative Models and Compressed Sensing

Abstract: The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain. For almost all results in this literature, the structure is represented by sparsity in a well-chosen basis. We show how to achieve guarantees similar to standard compressed sensing but without employing sparsity at all. Instead, we suppose that vectors lie near the range of a generative model, e.g. a GAN or a VAE. We show how the problems of image inpainting and super-resolution are special cases of our general framework. 

We show how to generalize the RIP condition for generative models and that random gaussian measurement matrices have this property with high probability. A Lipschitz condition for the generative neural network is a key technical condition. 

We will also discuss on-going work for adding causality and distributed training to these models. 

(based on joint work with Ashish Bora, Ajil Jalal and Eric Price)

Code: https://github.com/AshishBora/csgm

Homepage: users.ece.utexas.edu/~dimakis

Bio: Alex Dimakis is an Associate Professor at the ECE department, University of Texas at Austin. He received his Ph.D. in 2008 from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received an NSF Career award, a Google faculty research award and the Eli Jury dissertation award. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. He is currently serving as an associate editor for IEEE Transactions on Information Theory. His research interests include information theory, coding theory and machine learning.

--Junyoung