Raghav Kansal
Raghav Kansal
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Evaluating generative models in high energy physics
A systematic investigation of evaluation metrics for fast simulations, including two new ones we propose, the Fréchet and kernel physics distances, which we find to be the most sensitive. We also introduce the generative adversarial particle transformer (GAPT) model, which is significantly faster than MPGAN.
Raghav Kansal
,
Anni Li
,
Javier Duarte
,
Nadezda Chernyavskaya
,
Maurizio Pierini
,
Breno Orzari
,
Thiago Tomei
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DOI
Generative transformers and how to evaluate them
With the increase in luminosity and detector granularity, simulation will be a significant computational challenge in the HL-LHC. To …
Feb 14, 2023
CERN
Project
Slides
FastSim on GPUs
Dec 13, 2022
CERN
Project
Slides
Generative transformers and how to evaluate them
Nov 21, 2022
CERN
Project
Slides
On the Evaluation of Generative Models in HEP
Nov 1, 2022
Rutgers (Virtual)
Project
Slides
Discussion on Generative Models
Sep 14, 2022
Galileo Galilei Institute, Florence
Project
Particle Cloud Generation with Message Passing GANs
Sep 9, 2022
Galileo Galilei Institute, Florence
Project
Slides
Video
Overview and Outlook: Machine Learning for Simulation
Jul 21, 2022
CERN
Project
Slides
Particle Cloud Generation with Message Passing GANs
Jul 17, 2022
UW Seattle
Project
Machine Learning for LHC Simulations
Jul 14, 2022
Fermilab
Project
Slides
Video
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