57 citations · 98 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
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…
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
Alexander Bogatskiy, Timothy Hoffman, David W. Miller +1
Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limi…