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Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot
  Settings

Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

18 November 2021
Matias Valdenegro-Toro
    UQCV
ArXivPDFHTML

Papers citing "Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings"

4 / 4 papers shown
Title
Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty
Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty
Michal Tešnar
Bilal Wehbe
Matias Valdenegro-Toro
21
0
0
06 Apr 2025
Uncertainty Quantification in Machine Learning for Biosignal
  Applications -- A Review
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Ivo Pascal de Jong
A. Sburlea
Matias Valdenegro-Toro
12
1
0
15 Nov 2023
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
270
5,660
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
279
9,136
0
06 Jun 2015
1