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ECS 289G Talk: On the Bottleneck of Graph Neural Networks and its Practical Implications
Understanding Over-Squashing and Bottlenecks on Graphs via Curvature | Jake Topping & F. Di Giovanni
CONCERTO: A graph neural network approach for molecule carcinogenicity prediction
Theory of Graph Neural Networks: Representation and Learning | Derek Lim
Machine Learning with Graphs - Scaling up GNNs
Author Interview - Equivariant Subgraph Aggregation Networks
Meta Regression Graph Neural Network (Meta-RegGNN)| PRIME MICCAI 2022
Graph Convolutional Neural Network - Part A
ECS 289G Talk: Diffusion Models Beat GANs on Image Synthesis
Breaking the Bottleneck: From fast programs to fast projects
Theory of Graph Neural Networks (GNNs) Part 2 | Third Nepal Winter School in AI | Day 8 (2021)
#TALK: Distributed Neural Network Training via Independent Subnets - Rice University
