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Neural diffusion PDEs, differential geometry, and graph neural networks
February 2, 2022 @ 2:00 pm - 3:00 pm
![CMSA-NTM-Seminar-02.02.2022-2-1583x2048](https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-02.02.2022-2-1583x2048-1.png)
Speaker: Michael Bronstein, University of Oxford and Twitter
Title: Neural diffusion PDEs, differential geometry, and graph neural networks
Abstract: In this talk, I will make connections between Graph Neural Networks (GNNs) and non-Euclidean diffusion equations. I will show that drawing on methods from the domain of differential geometry, it is possible to provide a principled view on such GNN architectural choices as positional encoding and graph rewiring as well as explain and remedy the phenomena of oversquashing and bottlenecks.