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Neural diffusion PDEs, differential geometry, and graph neural networks

February 2, 2022 @ 6:00 pm - 7:05 pm


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.


February 2, 2022
6:00 pm - 7:05 pm
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