8 citations · 15 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 7 cited
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…
cs.LG2021
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…
eess.IV2020★ 8 cited
Deep Neural Networks for Computational Optical Form Measurements
Lara Hoffmann, Clemens Elster
Deep neural networks have been successfully applied in many different fields like computational imaging, medical healthcare, signal processing, or autonomous driving. In a proof-of…