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$\mathrm{E}(n)$ Equivariant Message Passing Simplicial Networks. (arXiv:2305.07100v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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. EMPSNs
can learn high-dimensional simplex features in graphs (e.g. triangles), and use
the increase of geometric information of higher-dimensional simplices in an
$\mathrm{E}(n)$ equivariant fashion. EMPSNs simultaneously generalize
$\mathrm{E}(n)$ Equivariant Graph Neural Networks to a topologically more
elaborate counterpart and provide an approach for including geometric
information in Message Passing Simplicial …
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