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Fail-Safe Execution of Deep Learning based Systems through Uncertainty
  Monitoring

Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring

1 February 2021
Michael Weiss
Paolo Tonella
    AAML
ArXivPDFHTML

Papers citing "Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring"

5 / 5 papers shown
Title
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Ruben Grewal
Paolo Tonella
Andrea Stocco
27
9
0
29 Apr 2024
A Closer Look at AUROC and AUPRC under Class Imbalance
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott
Lasse Hyldig Hansen
Haoran Zhang
Giovanni Angelotti
Jack Gallifant
31
27
0
11 Jan 2024
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning
  Agents
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
Amirhossein Zolfagharian
Manel Abdellatif
Lionel C. Briand
S. Ramesh
13
5
0
03 Aug 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
268
5,635
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
9,042
0
06 Jun 2015
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