Improved uncertainty quantification for neural networks with Bayesian last layer
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: IEEE access ; 11
- Event
-
Veröffentlichung
- (where)
-
Dortmund
- (who)
-
Universitätsbibliothek Dortmund
- (when)
-
2023
- Creator
-
Fiedler, Felix
Lucia, Sergio
- Handle
-
2003/42444
- URN
-
urn:nbn:de:101:1-2404190342379.693806393540
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:44 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Fiedler, Felix
- Lucia, Sergio
- Universitätsbibliothek Dortmund
Time of origin
- 2023
Other Objects (12)
Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
Multifaceted uncertainty quantification
Gaussian processes for sensitivity analysis, Bayesian inference, and uncertainty quantification in landslide research
Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach
A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes
Uncertainty Quantification in Reacting Flows
Uncertainty quantification with shallow water equations
Uncertainty quantification for ocean biogeochemical models
Uncertainty Quantification for Complex Engineering Systems
Uncertainty Quantification for Modular Co-Simulation
Uncertainty quantification with advanced simulation methods
Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
Multifaceted uncertainty quantification
Gaussian processes for sensitivity analysis, Bayesian inference, and uncertainty quantification in landslide research
Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach
A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes
Uncertainty Quantification in Reacting Flows
Uncertainty quantification with shallow water equations
Uncertainty quantification for ocean biogeochemical models
Uncertainty Quantification for Complex Engineering Systems
Uncertainty Quantification for Modular Co-Simulation
Uncertainty quantification with advanced simulation methods
Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
Multifaceted uncertainty quantification
Gaussian processes for sensitivity analysis, Bayesian inference, and uncertainty quantification in landslide research
Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach
A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes
Uncertainty Quantification in Reacting Flows
Uncertainty quantification with shallow water equations
Uncertainty quantification for ocean biogeochemical models
Uncertainty Quantification for Complex Engineering Systems
Uncertainty Quantification for Modular Co-Simulation