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 🙂 !