16 citations · 35 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…
Benchmarking GPU and TPU Performance with Graph Neural Networks
xiangyang Ju, Yunsong Wang, Daniel Murnane +3
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing U…
Symmetry Group Equivariant Architectures for Physics
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf +8
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of mac…