[Theory] NOW: [TTIC Talks] Talks at TTIC: Jeremias Sulam, Johns Hopkins University

[Brandie Jones]

*When:*         Monday, November 27th* at 11:30am CT  *


*Where:*        Talk will be given *live, in-person* at

                       TTIC, 6045 S. Kenwood Avenue

                        5th Floor, Room 530


*Virtually:*    via Panopto (Livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=fb0d086f-799a-4845-ab4b-b0ae0109ece9>
)



*Who:*           Jeremias Sulam, Johns Hopkins University



*Title*:           Understanding deep nets: on local Lipschitz functions
and learned proximal networks


*Abstract: *   Despite their prevalence, our foundational understanding of
deep neural networks (NNs) remains shallow. This talk aims to shed some
light on questions of robustness, generalization, and unsupervised learning
for inverse problems.

We begin by examining NNs through the lens of sparse local Lipschitz
functions. We will show how the characterization of the local neighborhoods
where these functions are locally Lipschitz and sparse allows us to develop
tighter bounds of their stability. In turn, this observation will lead to
tighter adversarial robustness certificates as well as non-uniform, and
often non-vacuous, generalization bounds.

The second part of the talk studies how NNs are deployed to solve inverse
problems. We will provide a framework to develop learned proximal networks,
which provide exact proximal operators for a data-driven nonconvex
regularizer. We will see how a new training strategy, dubbed proximal
matching, provably promotes the recovery of the log-prior of the true data
distribution. These networks provide general, unsupervised, expressive
proximal operators that can be used for general inverse problems with
convergence guarantees, while providing a window into the resulting priors
learned from data.


*Bio*: Jeremias Sulam received his bioengineering degree from Universidad
Nacional de Entre Ríos, Argentina, in 2013, and his PhD in Computer Science
from the Technion – Israel Institute of Technology, in 2018. He joined the
Biomedical Engineering Department at Johns Hopkins University in 2018 as an
assistant professor, and he is also a core faculty at the Mathematical
Institute for Data Science (MINDS) and the Center for Imaging Science at
JHU. He is the recipient of the Best Graduates Award of the Argentinean
National Academy of Engineering, and of the Early CAREER award of the
National Science Foundation. His research interests focus on robust,
interpretable, and trustworthy machine learning, biomedical imaging, and
inverse problems.


*Host:* *Sam Buchanan <sam at ttic.edu>*

-- 
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
Working Remotely on Tuesdays
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