OmniFold: A Method to Simultaneously Unfold All Observables

OmniFold: A Method to Simultaneously Unfold All Observables

20 November 2019

Anders Andreassen

Patrick T. Komiske

Benjamin Nachman

Papers citing "OmniFold: A Method to Simultaneously Unfold All Observables"

11 / 11 papers shown

Title
Generative Unfolding with Distribution Mapping A. Butter S. Diefenbacher Nathan Huetsch Vinicius Mikuni Benjamin Nachman Sofia Palacios Schweitzer Tilman Plehn DiffM 51 1 0 04 Nov 2024
Unifying Simulation and Inference with Normalizing Flows Haoxing Du Claudius Krause Vinicius Mikuni Benjamin Nachman Ian Pang David Shih 47 3 0 29 Apr 2024
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion Alexander Shmakov Kevin Greif M. Fenton A. Ghosh Pierre Baldi D. Whiteson DiffM 127 9 0 22 Apr 2024
Unbinned Profiled Unfolding Jay Chan Benjamin Nachman 32 7 0 10 Feb 2023
Latent Space Refinement for Deep Generative Models R. Winterhalder Marco Bellagente Benjamin Nachman BDL GAN DRL DiffM 22 27 0 01 Jun 2021
A Living Review of Machine Learning for Particle Physics Matthew Feickert Benjamin Nachman KELM AI4CE 39 178 0 02 Feb 2021
E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once Benjamin Nachman Jesse Thaler 37 33 0 18 Jan 2021
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference M. Vandegar Michael Kagan Antoine Wehenkel Gilles Louppe 34 27 0 11 Nov 2020
Simulation Assisted Likelihood-free Anomaly Detection Anders Andreassen Benjamin Nachman David Shih 22 112 0 14 Jan 2020
How to GAN away Detector Effects Marco Bellagente A. Butter Gregor Kasieczka Tilman Plehn R. Winterhalder GAN 21 86 0 01 Dec 2019
Machine learning approach to inverse problem and unfolding procedure N. Gagunashvili 50 20 0 12 Apr 2010