research
∙
12/14/2022
Lorentz Group Equivariant Autoencoders
There has been significant work recently in developing machine learning ...
share
research
∙
12/09/2022
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data princi...
research
∙
11/18/2022
On the Evaluation of Generative Models in High Energy Physics
There has been a recent explosion in research into machine-learning-base...
research
∙
11/17/2022
Do graph neural networks learn traditional jet substructure?
At the CERN LHC, the task of jet tagging, whose goal is to infer the ori...
research
∙
03/01/2022
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation ...
research
∙
11/24/2021
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Autoencoders have useful applications in high energy physics in anomaly ...
research
∙
11/24/2021
Explaining machine-learned particle-flow reconstruction
The particle-flow (PF) algorithm is used in general-purpose particle det...
research
∙
06/22/2021
Particle Cloud Generation with Message Passing Generative Adversarial Networks
In high energy physics (HEP), jets are collections of correlated particl...
research
∙
11/30/2020
