A Method of Trust Region Type for Minimizing Noisy Functions. - Clemens Elster; Arnold Neumaier (Computing, 1997)

Systematic latency variation of the auditory evoked M100: from average to single-trial data. - A. Salajegheh; Alfred Link; Clemens Elster; Martin Burghoff; Tilmann Heinrich Sander; Lutz Trahms; David Poeppel (NeuroImage, 2004)

Diameter determination of Avogadro Spheres #1 and #2. - R. Arnold Nicolaus; Clemens Elster (IEEE Trans. Instrum. Meas., 2005)

Verification of humans using the electrocardiogram. - Gerd Wübbeler; Manuel Stavridis; Dieter Kreiseler; Ralf-Dieter Bousseljot; Clemens Elster (Pattern Recognit. Lett., 2007)

Non-independent BSS: A Model for Evoked MEG Signals with Controllable Dependencies. - Florian Kohl; Gerd Wübbeler; Dorothea Kolossa; Clemens Elster; Markus Bär; Reinhold Orglmeister (ICA, 2009)

Determination of the Complex Residual Error Parameters of a Calibrated One-Port Vector Network Analyzer. - Gerd Wübbeler; Clemens Elster; Thomas M. Reichel; Rolf Judaschke (IEEE Trans. Instrum. Meas., 2009)

Noise Adjusted PCA for Finding the Subspace of Evoked Dependent Signals from MEG Data. - Florian Kohl; Gerd Wübbeler; Dorothea Kolossa; Clemens Elster; Markus Bär; Reinhold Orglmeister (LVA/ICA, 2010)

Dynamic Uncertainty for Compensated Second-Order Systems. - Sascha Eichstädt; Alfred Link; Clemens Elster (Sensors, 2010)

On the choice of a noninformative prior for Bayesian inference of discretized normal observations. - Clemens Elster; Ignacio Lira (Comput. Stat., 2012)

Analysis of Key Comparisons Incorporating Knowledge About Bias. - Ignacio Lira; Anna Chunovkina; Clemens Elster; Wolfgang Woger (IEEE Trans. Instrum. Meas., 2012)

On the evaluation of uncertainties for state estimation with the Kalman filter. - Sascha Eichstädt; Natallia Makarava; Clemens Elster (CoRR, 2016)

Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances. - Clemens Elster; Gerd Wübbeler (Comput. Stat., 2017)

A Large-Scale Optimization Method Using a Sparse Approximation of the Hessian for Magnetic Resonance Fingerprinting. - Gerd Wübbeler; Clemens Elster (SIAM J. Imaging Sci., 2017)

Reducing sample size by tightening test conditions. - Katy Klauenberg; Rainer Kramer; Corinna Kroner; Jürgen Rose; Clemens Elster (Qual. Reliab. Eng. Int., 2018)

Assessment of CT Image Quality Using a Bayesian Framework. - Alexander Khanin; Mathias Anton; Marcel Reginatto; Clemens Elster (IEEE Trans. Medical Imaging, 2018)

Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging. - Judith Lehnert; Christoph Kolbitsch; Gerd Wübbeler; Amedeo Chiribiri; Tobias Schaeffter; Clemens Elster (SIAM J. Imaging Sci., 2019)

Deep Neural Networks for Computational Optical Form Measurements. - Lara Hoffmann; Clemens Elster (CoRR, 2020)

Inspecting adversarial examples using the fisher information. - Jörg Martin; Clemens Elster (Neurocomputing, 2020)

Mammography Image Quality Assurance Using Deep Learning. - Tobias Kretz; Klaus-Robert Müller; Tobias Schaeffter; Clemens Elster (IEEE Trans. Biomed. Eng., 2020)

Detecting unusual input to neural networks. - Jörg Martin; Clemens Elster (Appl. Intell., 2021)

Errors-in-Variables for deep learning: rethinking aleatoric uncertainty. - Jörg Martin; Clemens Elster (CoRR, 2021)

Deep Ensembles from a Bayesian Perspective. - Lara Hoffmann; Clemens Elster (CoRR, 2021)

Uncertainty quantification by ensemble learning for computational optical form measurements. - Lara Hoffmann; Ines Fortmeier; Clemens Elster (Mach. Learn. Sci. Technol., 2021)

Assessment of sub-sampling schemes for compressive nano-FTIR imaging. - Selma Metzner; Bernd Kästner; Manuel Marschall; Gerd Wübbeler; Stefan Wundrack; Andrey Bakin; Arne Hoehl; Eckart Rühl; Clemens Elster (CoRR, 2022)

Explainability for deep learning in mammography image quality assessment. - Narbota Amanova; Jörg Martin; Clemens Elster (Mach. Learn. Sci. Technol., 2022)

Assessment of Subsampling Schemes for Compressive Nano-FTIR Imaging. - Selma Metzner; Bernd Kästner; Manuel Marschall; Gerd Wübbeler; Stefan Wundrack; Andrey Bakin; Arne Hoehl; Eckart Rühl; Clemens Elster (IEEE Trans. Instrum. Meas., 2022)

Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression. - Jörg Martin; Clemens Elster (Neural Process. Lett., 2023)

A framework for benchmarking uncertainty in deep regression. - Franko Schmähling; Jörg Martin; Clemens Elster (Appl. Intell., 2023)

About the generalizability of deep learning based image quality assessment in mammography. - Josua Faller; Narbota Amanova; Ruben E. van Engen; Jörg Martin; Clemens Elster (Mach. Learn. Sci. Technol., 2023)

Finding the input features that reduce the entropy of a neural network's prediction. - Narbota Amanova; Jörg Martin; Clemens Elster (Appl. Intell., 2024)

Generative models and Bayesian inversion using Laplace approximation. - Manuel Marschall; Gerd Wübbeler; Franko Schmähling; Clemens Elster (Comput. Stat., 2024)

Deep Errors-in-Variables using a diffusion model. - Josua Faller; Jörg Martin; Clemens Elster (Mach. Learn., 2025)