ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2208.03284
  4. Cited By
Interpretable Uncertainty Quantification in AI for HEP

Interpretable Uncertainty Quantification in AI for HEP

5 August 2022
Thomas Y. Chen
B. Dey
A. Ghosh
Michael Kagan
Brian D. Nord
Nesar Ramachandra
ArXivPDFHTML

Papers citing "Interpretable Uncertainty Quantification in AI for HEP"

4 / 4 papers shown
Title
FAIR Universe HiggsML Uncertainty Challenge Competition
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
32
1
0
03 Oct 2024
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in
  Gaussian Process Hybrid Deep Networks
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
60
171
0
08 Jul 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,652
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
1