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Adversarial domain adaptation to reduce sample bias of a high energy
  physics classifier

Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

1 May 2020
Jose M. Clavijo
Paul Glaysher
Judith M. Katzy
J. Jitsev
ArXivPDFHTML

Papers citing "Adversarial domain adaptation to reduce sample bias of a high energy physics classifier"

6 / 6 papers shown
Title
HateDebias: On the Diversity and Variability of Hate Speech Debiasing
HateDebias: On the Diversity and Variability of Hate Speech Debiasing
Nankai Lin
Hongyan Wu
Zhengming Chen
Zijian Li
Lianxi Wang
Shengyi Jiang
Dong Zhou
Aimin Yang
41
0
0
07 Jun 2024
Interpretable Uncertainty Quantification in AI for HEP
Interpretable Uncertainty Quantification in AI for HEP
Thomas Y. Chen
B. Dey
A. Ghosh
Michael Kagan
Brian D. Nord
Nesar Ramachandra
35
7
0
05 Aug 2022
Bias and Priors in Machine Learning Calibrations for High Energy Physics
Bias and Priors in Machine Learning Calibrations for High Energy Physics
Rikab Gambhir
Benjamin Nachman
Jesse Thaler
AI4CE
32
7
0
10 May 2022
Exploring the Universality of Hadronic Jet Classification
Exploring the Universality of Hadronic Jet Classification
K. Cheung
Yi Chung
Shih-Chieh Hsu
Benjamin Nachman
17
2
0
08 Apr 2022
Online-compatible Unsupervised Non-resonant Anomaly Detection
Online-compatible Unsupervised Non-resonant Anomaly Detection
Vinicius Mikuni
Benjamin Nachman
David Shih
31
35
0
11 Nov 2021
A Cautionary Tale of Decorrelating Theory Uncertainties
A Cautionary Tale of Decorrelating Theory Uncertainties
A. Ghosh
Benjamin Nachman
30
17
0
16 Sep 2021
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