1 code implementation • 15 Feb 2024 • Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré
Experimental results show that our method is able to outperform both equivariant and simplicial graph neural networks on a variety of geometric tasks.
no code implementations • 22 Oct 2023 • Floor Eijkelboom, Erik Bekkers, Michael Bronstein, Francesco Di Giovanni
This suggests that the importance of message passing is limited when the model can construct strong structural encodings.
no code implementations • 11 May 2023 • Floor Eijkelboom, Rob Hesselink, Erik Bekkers
This paper presents $\mathrm{E}(n)$ Equivariant Message Passing Simplicial Networks (EMPSNs), a novel approach to learning on geometric graphs and point clouds that is equivariant to rotations, translations, and reflections.
