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

3 citations · 5 across the 3 of their papers we have counts for

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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…