3 citations · 5 across the 3 of their papers we have counts for
3 papers
hep-ph2024★ 1 cited
A Language Model for Particle Tracking
Andris Huang, Yash Melkani, Paolo Calafiura +4
Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. Ho…
cs.LG2023★ 3 cited
Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks
Savannah Thais, Daniel Murnane
Incorporating inductive biases into ML models is an active area of ML research, especially when ML models are applied to data about the physical world. Equivariant Graph Neural Net…
cs.LG2023★ 1 cited
Graph Structure from Point Clouds: Geometric Attention is All You Need
Daniel Murnane
The use of graph neural networks has produced significant advances in point cloud problems, such as those found in high energy physics. The question of how to produce a graph struc…