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Deep Anti-Regularized Ensembles provide reliable out-of-distribution
  uncertainty quantification

Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification

8 April 2023
Antoine de Mathelin
Francois Deheeger
Mathilde Mougeot
Nicolas Vayatis
    OOD
    UQCV
ArXivPDFHTML

Papers citing "Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification"

4 / 4 papers shown
Title
Scalable Ensemble Diversification for OOD Generalization and Detection
Scalable Ensemble Diversification for OOD Generalization and Detection
Alexander Rubinstein
Luca Scimeca
Damien Teney
Seong Joon Oh
BDL
OOD
366
1
0
25 Sep 2024
On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and
  Conflictual Loss
On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss
Mohammed Fellaji
Frédéric Pennerath
Brieuc Conan-Guez
Miguel Couceiro
UQCV
43
1
0
16 Jul 2024
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
276
5,661
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
285
9,138
0
06 Jun 2015
1