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Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
2024-01-16
· ICLR 2024 poster ·
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Findings
IC-1399
Invariant GNNs (l=0) consistently fail to distinguish k-hop identical but globally distinct geometric graphs on the k-chain task, regardless of model depth
IC-1400
When steerable feature dimension is held constant, increasing the type-l of steerable features does not improve performance of ESCN or EquiformerV2 on IS2RE and S2EF molecular property prediction