20 citations · 36 across the 8 of their papers we have counts for
11 papers
Assessment of sub-sampling schemes for compressive nano-FTIR imaging
Selma Metzner, Bernd Kästner, Manuel Marschall +6
Nano-FTIR imaging is a powerful scanning-based technique at nanometer spatial resolution which combines Fourier transform infrared spectroscopy (FTIR) and scattering-type scanning…
Generative models and Bayesian inversion using Laplace approximation
Manuel Marschall, Gerd Wübbeler, Franko Schmähling +1
The Bayesian approach to solving inverse problems relies on the choice of a prior. This critical ingredient allows the formulation of expert knowledge or physical constraints in a…
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…