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An Empirical Study of Uncertainty Estimation Techniques for Detecting
  Drift in Data Streams

An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams

22 November 2023
Anton Winter
Nicolas Jourdan
Tristan Wirth
Volker Knauthe
Arjan Kuijper
ArXivPDFHTML

Papers citing "An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams"

3 / 3 papers shown
Title
Extremely Simple Activation Shaping for Out-of-Distribution Detection
Extremely Simple Activation Shaping for Out-of-Distribution Detection
Andrija Djurisic
Nebojsa Bozanic
Arjun Ashok
Rosanne Liu
OODD
172
151
0
20 Sep 2022
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
9,145
0
06 Jun 2015
1