[Theory] REMINDER: 3/4 Talks at TTIC: Raymond Yeh, UIUC
Mary Marre
mmarre at ttic.edu
Wed Mar 3 15:54:30 CST 2021
*When:* Thursday, March 4th at* 11:10 am CT*
*Where:* Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_byoLTO4SRAyopvO2kKizHQ>*)
*Who: * Raymond Yeh, UIUC
*Title: * Extracting Structures from Data: The Black-Box, the Manual
and the Discovered
*Abstract:* Representing structure in data is at the heart of computer
vision and machine learning, i.e., the act of converting raw data into a
useful mathematical form. In this talk, I will discuss solutions that are
broadly characterized into three themes: the black-box, the manual, and the
discovered. First, I will discuss how to use deep generative models to
learn structures for face images and its application to image inpainting.
Going beyond black-box models, I will explain how to manually impose
structures in deep-nets for human pose-regression. Specifically, I will
introduce chirality nets, a family of deep-nets that respects left/right
symmetry of human poses. Lastly, I will illustrate how to discover pairwise
word-to-object structures in the context of textual-grounding and discuss
current efforts towards discovering general structures.
*Bio:* Raymond A. Yeh is a PhD candidate at the University of Illinois at
Urbana-Champaign (UIUC) advised by Alexander Schwing, Minh Do, and Mark
Hasegawa-Johnson. Previously, he has spent time interning at Google AI and
Johns Hopkins University. He is a recipient of the Google PhD Fellowship,
the Mavis Future Faculty Fellowship and the Henry Ford II Scholarship. His
research interests lie at the intersection of machine learning and computer
vision.
*Host:* Greg Shakhnarovich <greg at ttic.edu>
Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL 60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
On Thu, Feb 25, 2021 at 8:19 PM Mary Marre <mmarre at ttic.edu> wrote:
> *When:* Thursday, March 4th at* 11:10 am CT*
>
>
>
> *Where:* Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_byoLTO4SRAyopvO2kKizHQ>*
> )
>
>
>
> *Who: * Raymond Yeh, UIUC
>
>
> *Title: * Extracting Structures from Data: The Black-Box, the
> Manual and the Discovered
>
> *Abstract:* Representing structure in data is at the heart of computer
> vision and machine learning, i.e., the act of converting raw data into a
> useful mathematical form. In this talk, I will discuss solutions that are
> broadly characterized into three themes: the black-box, the manual, and the
> discovered. First, I will discuss how to use deep generative models to
> learn structures for face images and its application to image inpainting.
> Going beyond black-box models, I will explain how to manually impose
> structures in deep-nets for human pose-regression. Specifically, I will
> introduce chirality nets, a family of deep-nets that respects left/right
> symmetry of human poses. Lastly, I will illustrate how to discover pairwise
> word-to-object structures in the context of textual-grounding and discuss
> current efforts towards discovering general structures.
>
> *Bio:* Raymond A. Yeh is a PhD candidate at the University of Illinois at
> Urbana-Champaign (UIUC) advised by Alexander Schwing, Minh Do, and Mark
> Hasegawa-Johnson. Previously, he has spent time interning at Google AI and
> Johns Hopkins University. He is a recipient of the Google PhD Fellowship,
> the Mavis Future Faculty Fellowship and the Henry Ford II Scholarship. His
> research interests lie at the intersection of machine learning and computer
> vision.
>
> *Host:* Greg Shakhnarovich <greg at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL 60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/theory/attachments/20210303/cb693bad/attachment.html>
More information about the Theory
mailing list