Uncertainty Quantification for Deep Neural Networks: An Empirical
Comparison and Usage Guidelines

Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines

14 December 2022

Paolo Tonella

Papers citing "Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines"

6 / 6 papers shown

Title
A Closer Look at AUROC and AUPRC under Class Imbalance Matthew B. A. McDermott Lasse Hyldig Hansen Haoran Zhang Giovanni Angelotti Jack Gallifant 36 31 0 11 Jan 2024
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing Michael Weiss A. Gómez Paolo Tonella AAML 13 6 0 21 Jul 2022
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 186 273 0 28 Sep 2021
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring Michael Weiss Paolo Tonella AAML 45 29 0 01 Feb 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015