Quantifying Epistemic Uncertainty in Deep Learning

23 October 2021

Papers citing "Quantifying Epistemic Uncertainty in Deep Learning"

8 / 8 papers shown

Title
Generative vs. Discriminative modeling under the lens of uncertainty quantification Elouan Argouarc'h François Desbouvries Eric Barat Eiji Kawasaki UQCV 40 1 0 13 Jun 2024
Uncertainty Quantification on Graph Learning: A Survey Chao Chen Chenghua Guo Rui Xu Xiangwen Liao Xi Zhang Sihong Xie Hui Xiong Philip S. Yu AI4CE 31 1 0 23 Apr 2024
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks Ziyi Huang H. Lam Haofeng Zhang UQCV 18 4 0 09 Jun 2023
Model error and its estimation, with particular application to loss reserving G. Taylor G. McGuire 14 0 0 30 Sep 2022
The Infinitesimal Jackknife and Combinations of Models Indrayudh Ghosal Yunzhe Zhou Giles Hooker 28 4 0 31 Aug 2022
Improving Deep Neural Network Random Initialization Through Neuronal Rewiring Leonardo F. S. Scabini B. De Baets Odemir M. Bruno AI4CE 23 6 0 17 Jul 2022
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