[🥳new video🧠] You thought that the curvature of space and Ricci flow (famously used by Perelman to solve the Poincaré Conjecture) have nothing to do with deep learning? #oversquashing
YT: https://t.co/Odt5Xuq1Cu
@jctopping@Francesco_dgv @b_p_chamberlain @epomqo@mmbronstein
Thrilled that our paper "Understanding over-squashing and bottlenecks on graphs via curvature" has been accepted for an oral presentation at #ICLR2022! Big thank you to the team @Francesco_dgv @b_p_chamberlain @epomqo@mmbronstein (https://t.co/LH6SuzkK10)
This Tuesday in LoGaG @Francesco_dgv@jctopping and @b_p_chamberlain present their "Understanding over-squashing and bottlenecks on graphs via curvature" https://t.co/BUBEqdXiqz - an absolute super-star paper from #iclr2022!🚀
Join the meeting here: https://t.co/9fxz75txpY
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Great article from @PetarV_93 and @mmbronstein on another exciting year of Geometric ML to come, and excited to see our recent work on curvature rewiring and bottlenecks with @Francesco_dgv @b_p_chamberlain @epomqo and @mmbronstein featured. Bring on 2022!
Geometric & Graph ML were a 2021 highlight, with exciting fundamental research & high-profile applications.
@mmbronstein and I interviewed distinguished experts to review this progress & predict 2022 trends. It's our longest post yet! See 🧵 for summary.
https://t.co/9znOfOAXV7
Over-squashing is a common plight of GNNs occurring when message passing fails to propagate information efficiently on the graph. In a new post, we discuss how this phenomenon can be understood and remedied through the concept of Ricci curvature
https://t.co/sXVWL2Ydok