Deep learning ; Bayesian neural networks ; Deep regression ; Reference prior
Zusammenfassung
We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear combination of nonlinear functions. Basically, any level of complexity can be realized through the choice of the nonlinear functions and the dimensionality of their domain. Results of an uncertainty quantification for deep regression are compared against those obtained by a statistical reference method. The reference method utilizes knowledge about the underlying nonlinear functions and is based on Bayesian linear regression using a prior reference. The flexibility, together with the availability of a reference solution, makes the framework suitable for defining benchmark sets for uncertainty quantification. Reliability of uncertainty quantification is assessed in terms of coverage probabilities, and accuracy through the size of calculated uncertainties. We illustrate the proposed framework by applying it to current approaches for uncertainty quantification in deep regression. In addition, results for three real-world regression tasks are presented.
Kostenfreier Zugang
Open Access Hybrid
Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Schmähling, F., Martin, J., & Elster, C. (2023). A framework for benchmarking uncertainty in deep regression. Applied Intelligence, 53, 9499–9512. https://doi.org/10.1007/s10489-022-03908-3
