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.

Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/96232340757