Clemens Elster

According to our database, Clemens Elster authored at least 31 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Generative models and Bayesian inversion using Laplace approximation.

Manuel Marschall, Gerd Wübbeler, Franko Schmähling, Clemens Elster

Comput. Stat., May, 2024

Finding the input features that reduce the entropy of a neural network's prediction.

Narbota Amanova, Jörg Martin, Clemens Elster

Appl. Intell., 2024

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., December, 2023

A framework for benchmarking uncertainty in deep regression.

Franko Schmähling, Jörg Martin, Clemens Elster

Appl. Intell., April, 2023

Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression.

Jörg Martin, Clemens Elster

Neural Process. Lett., 2023

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

Explainability for deep learning in mammography image quality assessment.

Narbota Amanova, Jörg Martin, Clemens Elster

Mach. Learn. Sci. Technol., 2022

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

2021

Uncertainty quantification by ensemble learning for computational optical form measurements.

Lara Hoffmann, Ines Fortmeier, Clemens Elster

Mach. Learn. Sci. Technol., September, 2021

Deep Ensembles from a Bayesian Perspective.

Lara Hoffmann, Clemens Elster

CoRR, 2021

Errors-in-Variables for deep learning: rethinking aleatoric uncertainty.

Jörg Martin, Clemens Elster

CoRR, 2021

Detecting unusual input to neural networks.

Jörg Martin, Clemens Elster

Appl. Intell., 2021

2020

Mammography Image Quality Assurance Using Deep Learning.

Tobias Kretz, Klaus-Robert Müller, Tobias Schaeffter, Clemens Elster

IEEE Trans. Biomed. Eng., 2020

Inspecting adversarial examples using the fisher information.

Jörg Martin, Clemens Elster

Neurocomputing, 2020

Deep Neural Networks for Computational Optical Form Measurements.

Lara Hoffmann, Clemens Elster

CoRR, 2020

2019

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

2018

Assessment of CT Image Quality Using a Bayesian Framework.

Alexander Khanin, Mathias Anton, Marcel Reginatto, Clemens Elster

IEEE Trans. Medical Imaging, 2018

Reducing sample size by tightening test conditions.

Katy Klauenberg, Rainer Kramer, Corinna Kroner, Jürgen Rose, Clemens Elster

Qual. Reliab. Eng. Int., 2018

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

Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances.

Clemens Elster, Gerd Wübbeler

Comput. Stat., 2017

2016

On the evaluation of uncertainties for state estimation with the Kalman filter.

Sascha Eichstädt, Natallia Makarava, Clemens Elster

CoRR, 2016

2012

Analysis of Key Comparisons Incorporating Knowledge About Bias.

Ignacio Lira, Anna Chunovkina, Clemens Elster, Wolfgang Woger

IEEE Trans. Instrum. Meas., 2012

On the choice of a noninformative prior for Bayesian inference of discretized normal observations.

Clemens Elster, Ignacio Lira

Comput. Stat., 2012

2010

Dynamic Uncertainty for Compensated Second-Order Systems.

Sascha Eichstädt, Alfred Link, Clemens Elster

Sensors, 2010

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

Proceedings of the Latent Variable Analysis and Signal Separation, 2010

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

Non-independent BSS: A Model for Evoked MEG Signals with Controllable Dependencies.

Florian Kohl, Gerd Wübbeler, Dorothea Kolossa, Clemens Elster, Markus Bär, Reinhold Orglmeister

Proceedings of the Independent Component Analysis and Signal Separation, 2009

2007

Verification of humans using the electrocardiogram.

Gerd Wübbeler, Manuel Stavridis, Dieter Kreiseler, Ralf-Dieter Bousseljot, Clemens Elster

Pattern Recognit. Lett., 2007

2005

Diameter determination of Avogadro Spheres #1 and #2.

R. Arnold Nicolaus, Clemens Elster

IEEE Trans. Instrum. Meas., 2005

2004

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

1997

A Method of Trust Region Type for Minimizing Noisy Functions.

Clemens Elster, Arnold Neumaier

Computing, 1997