Colloquium: Extracting structures from data: The black-box, the manual and the discovered

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Raymond Yeh (University of Illinois at Urbana-Champaign), will be giving a talk titled "Bridging algorithmic and statistical randomness in machine learning".

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.

Biography

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.