Clemens Elster

38 papers Journal 36Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Deep learning based posterior sampling for high-dimensional linear inverse problems. Comput. Stat. Gerd Wübbeler, Franko Schmähling, Manuel Marschall, Clemens Elster
2025	J	`jnl`	Deep Errors-in-Variables using a diffusion model. Mach. Learn. Josua Faller, Jörg Martin, Clemens Elster
2024	J	`jnl`	Finding the input features that reduce the entropy of a neural network's prediction. Appl. Intell. Narbota Amanova, Jörg Martin, Clemens Elster
2024	J	`jnl`	Generative models and Bayesian inversion using Laplace approximation. Comput. Stat. Manuel Marschall, Gerd Wübbeler, Franko Schmähling, Clemens Elster
2023	J	`jnl`	A framework for benchmarking uncertainty in deep regression. Appl. Intell. Franko Schmähling, Jörg Martin, Clemens Elster
2023	J	`jnl`	About the generalizability of deep learning based image quality assessment in mammography. Mach. Learn. Sci. Technol. Josua Faller, Narbota Amanova, Ruben E. van Engen, Jörg Martin, Clemens Elster
2023	J	`jnl`	Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression. Neural Process. Lett. Jörg Martin, Clemens Elster
2022	J	`jnl`	Assessment of Subsampling Schemes for Compressive Nano-FTIR Imaging. IEEE Trans. Instrum. Meas. Selma Metzner, Bernd Kästner, Manuel Marschall, Gerd Wübbeler, Stefan Wundrack, Andrey Bakin, Arne Hoehl, Eckart Rühl, Clemens Elster
2022	J	`jnl`	Assessment of sub-sampling schemes for compressive nano-FTIR imaging. CoRR Selma Metzner, Bernd Kästner, Manuel Marschall, Gerd Wübbeler, Stefan Wundrack, Andrey Bakin, Arne Hoehl, Eckart Rühl, Clemens Elster
2022	J	`jnl`	Explainability for deep learning in mammography image quality assessment. Mach. Learn. Sci. Technol. Narbota Amanova, Jörg Martin, Clemens Elster
2022	J	`jnl`	Generative models and Bayesian inversion using Laplace approximation. CoRR Manuel Marschall, Gerd Wübbeler, Franko Schmähling, Clemens Elster
2021	J	`jnl`	A framework for benchmarking uncertainty in deep regression. CoRR Franko Schmähling, Jörg Martin, Clemens Elster
2021	J	`jnl`	Deep Ensembles from a Bayesian Perspective. CoRR Lara Hoffmann, Clemens Elster
2021	J	`jnl`	Detecting unusual input to neural networks. Appl. Intell. Jörg Martin, Clemens Elster
2021	J	`jnl`	Errors-in-Variables for deep learning: rethinking aleatoric uncertainty. CoRR Jörg Martin, Clemens Elster
2021	J	`jnl`	Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements. CoRR Lara Hoffmann, Ines Fortmeier, Clemens Elster
2021	J	`jnl`	Uncertainty quantification by ensemble learning for computational optical form measurements. Mach. Learn. Sci. Technol. Lara Hoffmann, Ines Fortmeier, Clemens Elster
2020	J	`jnl`	Deep Neural Networks for Computational Optical Form Measurements. CoRR Lara Hoffmann, Clemens Elster
2020	J	`jnl`	Detecting unusual input to neural networks. CoRR Jörg Martin, Clemens Elster
2020	J	`jnl`	Inspecting adversarial examples using the fisher information. Neurocomputing Jörg Martin, Clemens Elster
2020	J	`jnl`	Mammography Image Quality Assurance Using Deep Learning. IEEE Trans. Biomed. Eng. Tobias Kretz, Klaus-Robert Müller, Tobias Schaeffter, Clemens Elster
2019	J	`jnl`	Inspecting adversarial examples using the Fisher information. CoRR Jörg Martin, Clemens Elster
2019	J	`jnl`	Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging. SIAM J. Imaging Sci. Judith Lehnert, Christoph Kolbitsch, Gerd Wübbeler, Amedeo Chiribiri, Tobias Schaeffter, Clemens Elster
2018	J	`jnl`	Assessment of CT Image Quality Using a Bayesian Framework. IEEE Trans. Medical Imaging Alexander Khanin, Mathias Anton, Marcel Reginatto, Clemens Elster
2018	J	`jnl`	Reducing sample size by tightening test conditions. Qual. Reliab. Eng. Int. Katy Klauenberg, Rainer Kramer, Corinna Kroner, Jürgen Rose, Clemens Elster
2017	J	`jnl`	A Large-Scale Optimization Method Using a Sparse Approximation of the Hessian for Magnetic Resonance Fingerprinting. SIAM J. Imaging Sci. Gerd Wübbeler, Clemens Elster
2017	J	`jnl`	Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances. Comput. Stat. Clemens Elster, Gerd Wübbeler
2016	J	`jnl`	On the evaluation of uncertainties for state estimation with the Kalman filter. CoRR Sascha Eichstädt, Natallia Makarava, Clemens Elster
2012	J	`jnl`	Analysis of Key Comparisons Incorporating Knowledge About Bias. IEEE Trans. Instrum. Meas. Ignacio Lira, Anna Chunovkina, Clemens Elster, Wolfgang Woger
2012	J	`jnl`	On the choice of a noninformative prior for Bayesian inference of discretized normal observations. Comput. Stat. Clemens Elster, Ignacio Lira
2010	J	`jnl`	Dynamic Uncertainty for Compensated Second-Order Systems. Sensors Sascha Eichstädt, Alfred Link, Clemens Elster
2010	—	`conf`	Noise Adjusted PCA for Finding the Subspace of Evoked Dependent Signals from MEG Data. LVA/ICA Florian Kohl, Gerd Wübbeler, Dorothea Kolossa, Clemens Elster, Markus Bär, Reinhold Orglmeister
2009	J	`jnl`	Determination of the Complex Residual Error Parameters of a Calibrated One-Port Vector Network Analyzer. IEEE Trans. Instrum. Meas. Gerd Wübbeler, Clemens Elster, Thomas M. Reichel, Rolf Judaschke
2009	—	`conf`	Non-independent BSS: A Model for Evoked MEG Signals with Controllable Dependencies. ICA Florian Kohl, Gerd Wübbeler, Dorothea Kolossa, Clemens Elster, Markus Bär, Reinhold Orglmeister
2007	J	`jnl`	Verification of humans using the electrocardiogram. Pattern Recognit. Lett. Gerd Wübbeler, Manuel Stavridis, Dieter Kreiseler, Ralf-Dieter Bousseljot, Clemens Elster
2005	J	`jnl`	Diameter determination of Avogadro Spheres #1 and #2. IEEE Trans. Instrum. Meas. R. Arnold Nicolaus, Clemens Elster
2004	J	`jnl`	Systematic latency variation of the auditory evoked M100: from average to single-trial data. NeuroImage A. Salajegheh, Alfred Link, Clemens Elster, Martin Burghoff, Tilmann Heinrich Sander, Lutz Trahms, David Poeppel
1997	J	`jnl`	A Method of Trust Region Type for Minimizing Noisy Functions. Computing Clemens Elster, Arnold Neumaier