Part of the 2017–2018 Fellows' Presentation Series
Lecture by Michael Bronstein RI '18
Free and open to the public.
At Radcliffe, Michael Bronstein is working on developing formulations of deep learning for non-Euclidean structured data such as graphs and manifolds, which are becoming increasingly important in a variety of fields including computer vision, sensor networks, biomedicine, genomics, and computational social sciences. He hopes that new geometric deep learning paradigms will help achieve quantitatively and qualitatively better results in these fields.