Understanding Event-Generation Networks via Uncertainties

Understanding Event-Generation Networks via Uncertainties

9 April 2021

Marco Bellagente

Manuel Haussmann

Michel Luchmann

Papers citing "Understanding Event-Generation Networks via Uncertainties"

10 / 10 papers shown

Title
Generative Unfolding with Distribution Mapping A. Butter S. Diefenbacher Nathan Huetsch Vinicius Mikuni Benjamin Nachman Sofia Palacios Schweitzer Tilman Plehn DiffM 41 1 0 04 Nov 2024
FAIR Universe HiggsML Uncertainty Challenge Competition W. Bhimji P. Calafiura Ragansu Chakkappai Yuan-Tang Chou S. Diefenbacher ... D. Rousseau Benjamin Sluijter Benjamin Thorne Ihsan Ullah Yulei Zhang 39 2 0 03 Oct 2024
Unifying Simulation and Inference with Normalizing Flows Haoxing Du Claudius Krause Vinicius Mikuni Benjamin Nachman Ian Pang David Shih 42 3 0 29 Apr 2024
Bias and Priors in Machine Learning Calibrations for High Energy Physics Rikab Gambhir Benjamin Nachman Jesse Thaler AI4CE 30 7 0 10 May 2022
Machine Learning in the Search for New Fundamental Physics G. Karagiorgi Gregor Kasieczka S. Kravitz Benjamin Nachman David Shih AI4CE 42 113 0 07 Dec 2021
Optimising simulations for diphoton production at hadron colliders using amplitude neural networks Joseph Aylett-Bullock S. Badger Ryan Moodie 13 22 0 17 Jun 2021
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows Claudius Krause David Shih AI4CE 34 81 0 09 Jun 2021
Latent Space Refinement for Deep Generative Models R. Winterhalder Marco Bellagente Benjamin Nachman BDL GAN DRL DiffM 10 27 0 01 Jun 2021
A Living Review of Machine Learning for Particle Physics Matthew Feickert Benjamin Nachman KELM AI4CE 27 178 0 02 Feb 2021
Towards a Computer Vision Particle Flow F. Bello S. Ganguly Eilam Gross Marumi Kado M. Pitt Lorenzo Santi Jonathan Shlomi 94 49 0 19 Mar 2020