Date: 2019-09-20

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Symmetry has played a significant role in modern physics, in part by constraining the physical laws. I will discuss how it could play a fundamental role in AI by constraining the deep model design. In particular, I focus on discrete domain symmetries and through examples show how we can use this inductive bias as a principled means for constraining a feedforward layer and significantly improving its sample efficiency.

Speaker

Siamak Ravanbakhsh is an Assistant Professor, School of Computer Science, McGill University. His research interests include inference within structured, complex and combinatorial domains using graphical and structured deep models.