[Lisa_seminaires] Alexandros Dimakis, June 15th 11AM, AA6214

Junyoung Chung elecegg at gmail.com
Dim 11 Juin 22:42:31 EDT 2017


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
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