20 citations · 36 across the 8 of their papers we have counts for
4 papers · 1 filter
A framework for benchmarking uncertainty in deep regression
Franko Schmähling, Jörg Martin, Clemens Elster
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…
Deep Ensembles from a Bayesian Perspective
Lara Hoffmann, Clemens Elster
Deep ensembles can be considered as the current state-of-the-art for uncertainty quantification in deep learning. While the approach was originally proposed as a non-Bayesian techn…
Hypothesis-based acceptance sampling for modules F and F1 of the European Measuring Instruments Directive
Katy Klauenberg, Cord A. Müller, Clemens Elster
Millions of measuring instruments are verified each year before being placed on the markets worldwide. In the EU, such initial conformity assessments are regulated by the Measuring…
Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements
Lara Hoffmann, Ines Fortmeier, Clemens Elster
Uncertainty quantification by ensemble learning is explored in terms of an application from computational optical form measurements. The application requires to solve a large-scale…