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.
2 code implementations • ICLR 2022 • Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling
Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry.
1 code implementation • 11 Jun 2020 • Rob Hesselink, Wilker Aziz
Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters.