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SUMMARY:Neural diffusion PDEs\, differential geometry\, and graph neural networks
DESCRIPTION:Speaker: Michael Bronstein\, University of Oxford and Twitter \nTitle: Neural diffusion PDEs\, differential geometry\, and graph neural networks \nAbstract: 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.
URL:https://cmsa.fas.harvard.edu/event/2-2-2022-new-technologies-in-mathematics/
LOCATION:MA
CATEGORIES:New Technologies in Mathematics Seminar
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