Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression

9 March 2022

Papers citing "Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression"

7 / 7 papers shown

Title
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 203 1,146 0 07 Jul 2021
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 202 2,108 0 08 Oct 2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference Jiayu Yao Weiwei Pan S. Ghosh Finale Doshi-Velez UQCV BDL 154 113 0 24 Jun 2019
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems M. A. Mohamad T. Sapsis 26 113 0 19 Apr 2018
Snapshot Ensembles: Train 1, get M for free Gao Huang Yixuan Li Geoff Pleiss Zhuang Liu John E. Hopcroft Kilian Q. Weinberger OOD FedML UQCV 118 950 0 01 Apr 2017
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 288 8,091 0 13 Aug 2016
MCMC using Hamiltonian dynamics Radford M. Neal 288 3,276 0 09 Jun 2012