70 citations · 174 across the 7 of their papers we have counts for
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hep-ph2023★ 1 cited
19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
Alexander Bogatskiy, Timothy Hoffman, Jan T. Offermann
As particle accelerators increase their collision rates, and deep learning solutions prove their viability, there is a growing need for lightweight and fast neural network architec…
hep-ph2023★ 30 cited
Explainable Equivariant Neural Networks for Particle Physics: PELICAN
Alexander Bogatskiy, Timothy Hoffman, David W. Miller +2
PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle…