 |
Center for Nonlinear Studies |
In this talk I will explain how groups, representations, and equivariant maps, the fundamental concepts of geometric deep learning, are special cases of the concepts category, functor, and natural transformation. Like equivariant maps, natural transformations capture the idea that the way we process an input should be essentially independent of which one of a number of equivalent (isomorphic) ways to encode an input we choose. Being more general, the categorical concepts open up new possibilities for "structure preserving machine learning" beyond what is currently considered in geometric DL. We will discuss examples such as natural graph networks and natural causal models.
Bio: Taco Cohen is a machine learning researcher at Qualcomm AI Research in Amsterdam. He was a co-founder of Scyfer, a company focussed on deep active learning, acquired by Qualcomm in 2017. He received a BSc in theoretical computer science from Utrecht University, and a MSc in artificial intelligence and PhD in machine learning (with prof. Max Welling) from the University of Amsterdam (all three cum laude). His research is focused on equivariant networks and geometric deep learning, causality and interactive learning. He received the 2014 University of Amsterdam MSc thesis prize, a Google PhD Fellowship, ICLR 2018 best paper award for “Spherical CNNsâ€, was named one of 35 innovators under 35 by MIT Tech Review, and won the 2022 Kees Schouhamer Immink prize for his PhD research.
Host: Wenting Li, Arvind Mohan