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Max Welling    @wellingmax   ·   10/7/2021
The next generation of fully steerable E(3) equivariant GNN architecture. The paper also presents a neat unified view on equivariant message passing methods. Brilliant work by @jo_brandstetter, @robdhess, @ElisevanderPol, @erikjbekkers. (Johannes is on ELLIS exchange from Linz)
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Max Welling    @wellingmax   ·   10/7/2021
The next generation fully steerable E(3) Equivariant Graph Neural Network. The paper also explains a neat unification between various equivariant message passing architectures. Fantastic work by @jo_brandstetter, @robdhess, @ElisevanderPol, @erikjbekkers.
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Johannes Brandstetter    @jo_brandstetter   ·   10/7/2021
Are you interested in graph structured data? Do you want to include geometry and physics to boost your GNNs? Check out our paper on Steerable E(3) Equivariant Graph Neural Networks. Joint work with @robdhess @ElisevanderPol @erikjbekkers @wellingmax https://t.co/exBcDhQ44k
 
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Johannes Brandstetter    @jo_brandstetter   ·   10/7/2021
Through the lens of equivariant nonlinear convolutions, successful components of SEGNNs are exposed: (i) non-linear message aggregation improves classic linear (steerable) point convolutions; (ii) steerable messages improve recent equivariant networks that send invariant messages
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Max Welling    @wellingmax   ·   9/7/2021
Brilliant work by Andy Keller on unsupervised learning of approximately equivariant capsules.
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Johannes Brandstetter
Tenure Track Researcher at JKU Linz, currently ELLIS PostDoc at University of Amsterdam

Jian Tang
Assistant Professor at Mila. Working on deep learning for graphs with applications in knowledge graphs, Drug Discovery and material discovery.

DeepMind
Our team research and build safe AI systems. We're committed to solving intelligence, to advance science and humanity.

TsinghuaNLP
Natural Language Processing Lab at Tsinghua University