activity
20202022
most citedLorentz Group Equivariant Neural Network for Particle Physics

70 citations · 77 across the 4 of their papers we have counts for

collaborators

5 papers

hep-ex20223 cited

Innovations in trigger and data acquisition systems for next-generation physics facilities

Rainer Bartoldus, Catrin Bernius, David W. Miller

Data-intensive physics facilities are increasingly reliant on heterogeneous and large-scale data processing and computational systems in order to collect, distribute, process, filt…

cs.LG20224 cited

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…

cs.LG2021

Quantized Gromov-Wasserstein

Samir Chowdhury, David Miller, Tom Needham

The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to allow for the comparison of probability distributions defined on different metric spaces. Scalable comp…

cs.AI2021

Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities

Chinmaya Mahesh, Kristin Dona, David W. Miller +1

Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being…

hep-ph202070 cited

Lorentz Group Equivariant Neural Network for Particle Physics

Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann +3

We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The…