Jeremias Sulam (Johns Hopkins University): “What’s In my Network? On Learned Proximals and Testing for Explanations”

Abstract:

Modern machine learning methods are revolutionizing what we can do with data, from tiktok video recommendations to biomarkers discovery in cancer research. Yet, the complexity of these deep models makes it harder to understand what functions these data-dependent models are computing, and which features they detect regarding as important for a given task. In this talk, I will review two approaches for turning general deep learning models more interpretable, both in an unsupervised setting in the context of imaging inverse problems—through learned proximal networks—as well as in supervised classification problems for computer vision—by testing for the semantic importance of concepts via betting.

Biography:

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 the Early CAREER award of the National Science Foundation. His research interests include inverse problems, sparse representation modeling and machine learning.

Recording: https://drive.google.com/file/d/1hoihWxdEKAnuM3Cw7CwQylIBxQGWxln0/view?usp=sharing