Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks

6 November 2023

Papers citing "Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks"

7 / 7 papers shown

Title
Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods Daniel Maître Vishal S. Ngairangbam M. Spannowsky 16 4 0 24 Oct 2024
On the design space between molecular mechanics and machine learning force fields Yuanqing Wang Kenichiro Takaba Michael S. Chen Marcus Wieder Yuzhi Xu ... Kyunghyun Cho Joe G. Greener Peter K. Eastman Stefano Martiniani M. Tuckerman AI4CE 32 4 0 03 Sep 2024
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics Siqi Miao Zhiyuan Lu Mia Liu Javier Duarte Pan Li 31 4 0 19 Feb 2024
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics A. Bogatskiy Timothy Hoffman David W. Miller Jan T. Offermann 16 30 0 01 Nov 2022
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics Rui Wang Robin G. Walters Rose Yu 25 73 0 28 Jan 2022
Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction Cenk Tüysüz C. Rieger Kristiane Novotny B. Demirköz D. Dobos K. Potamianos S. Vallecorsa J. Vlimant Richard Forster 99 52 0 26 Sep 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021