Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?

9 February 2024

Papers citing "Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?"

6 / 6 papers shown

Title
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification Viktor Bengs Eyke Hüllermeier Willem Waegeman UQCV 202 25 0 30 Jan 2023
The Unreasonable Effectiveness of Deep Evidential Regression N. Meinert J. Gawlikowski Alexander Lavin UQCV EDL 177 35 0 20 May 2022
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation Dennis Ulmer Christian Hardmeier J. Frellsen BDL UQCV UD EDL PER 42 48 0 06 Oct 2021
Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification Matias Valdenegro-Toro UQCV 58 51 0 17 Oct 2019
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