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

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
Ethan Pickering
T. Sapsis
    UQCV
ArXivPDFHTML

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

5 / 5 papers shown
Title
Information FOMO: The unhealthy fear of missing out on information. A
  method for removing misleading data for healthier models
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models
Ethan Pickering
T. Sapsis
16
6
0
27 Aug 2022
NeuralUQ: A comprehensive library for uncertainty quantification in
  neural differential equations and operators
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Zongren Zou
Xuhui Meng
Apostolos F. Psaros
George Karniadakis
AI4CE
25
36
0
25 Aug 2022
Discovering and forecasting extreme events via active learning in neural
  operators
Discovering and forecasting extreme events via active learning in neural operators
Ethan Pickering
Stephen Guth
George Karniadakis
T. Sapsis
AI4CE
11
57
0
05 Apr 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
273
5,660
0
05 Dec 2016
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,262
0
09 Jun 2012
1