17 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.LG2025
BLIPs: Bayesian Learned Interatomic Potentials
Dario Coscia, Pim de Haan, Max Welling
Machine Learning Interatomic Potentials (MLIPs) are becoming a central tool in simulation-based chemistry. However, like most deep learning models, MLIPs struggle to make accurate…
hep-ph2024★ 17 cited
A Lorentz-Equivariant Transformer for All of the LHC
Johann Brehmer, Víctor Bresó, Pim de Haan +4
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Colli…
cs.LG2024★ 1 cited
Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim de Haan +1
Given large datasets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem? Or is it more efficient to learn them…