Hi everyone! We have a guest speaker Michaël Defferrard from École Polytechnique Fédérale de Lausanne (EPFL) and you are all cordially invited to the AMLab Seminar on February 25th (Thursday) at 4:00 p.m. CET on Zoom. And then Michaël will give a talk titled “Learning from graphs: a spectral perspective”. Michaël is an inspiring researcher, who has done a lot of interesting works on graph deep learning and you can find additional information from his website. The following is the information on this talk.
Title: Learning from graphs: a spectral perspective
Abstract: The architecture of a neural network constrains the space of functions it can implement. Equivariance is one such constraint—enabling weight sharing and guaranteeing generalization. But symmetries alone might not be enough: for example, social networks, finite grids, and sampled spheres have few automorphisms. I will discuss how spectral graph theory yields vertex representations and a generalized convolution that shares weights beyond symmetries.
To gain more insight into Graph Deep Learning, feel free to join and discuss it! See you there 🙂 !