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Bayesian Autoencoders for Drift Detection in Industrial Environments

Bayesian Autoencoders for Drift Detection in Industrial Environments

28 July 2021
Bang Xiang Yong
Yasmin Fathy
Alexandra Brintrup
    UQCV
ArXivPDFHTML

Papers citing "Bayesian Autoencoders for Drift Detection in Industrial Environments"

5 / 5 papers shown
Title
Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design
Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design
Farzad Pourbabaee
S.
Peter McCrory
Luke Simon
Di Mo
36
0
0
04 Apr 2025
A method to benchmark high-dimensional process drift detection
A method to benchmark high-dimensional process drift detection
Edgar Wolf
Tobias Windisch
AI4TS
26
0
0
05 Sep 2024
Bayesian autoencoders with uncertainty quantification: Towards
  trustworthy anomaly detection
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
Bang Xiang Yong
Alexandra Brintrup
UQCV
15
24
0
25 Feb 2022
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised
  deep learning
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning
Bang Xiang Yong
Alexandra Brintrup
21
6
0
19 Oct 2021
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
282
9,136
0
06 Jun 2015
1