Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

15 October 2019

Papers citing "Quantifying Classification Uncertainty using Regularized Evidential Neural Networks"

7 / 7 papers shown

Title
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning Zhen Guo Zelin Wan Qisheng Zhang Xujiang Zhao F. Chen Jin-Hee Cho Qi Zhang Lance M. Kaplan Dong-Ho Jeong A. Jøsang UQCV EDL 22 10 0 12 Jun 2022
OpenTAL: Towards Open Set Temporal Action Localization Wentao Bao Qi Yu Yu Kong EDL 37 26 0 10 Mar 2022
Evidential Deep Learning for Open Set Action Recognition Wentao Bao Qi Yu Yu Kong CML EDL 19 135 0 21 Jul 2021
Uncertainty-Aware Reliable Text Classification Yibo Hu Latifur Khan EDL UQCV 38 33 0 15 Jul 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 61 1,111 0 07 Jul 2021
Multidimensional Uncertainty-Aware Evidential Neural Networks Yibo Hu Yuzhe Ou Xujiang Zhao Jin-Hee Cho Feng Chen EDL UQCV AAML 33 23 0 26 Dec 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015