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Clemens Elster
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2020 – today
- 2024
- [j24]Narbota Amanova
, Jörg Martin, Clemens Elster:
Finding the input features that reduce the entropy of a neural network's prediction. Appl. Intell. 54(2): 1922-1936 (2024) - [j23]Manuel Marschall
Generative models and Bayesian inversion using Laplace approximation. Comput. Stat. 39(3): 1321-1349 (2024) - 2023
- [j22]Franko Schmähling
A framework for benchmarking uncertainty in deep regression. Appl. Intell. 53(8): 9499-9512 (2023) - [j21]Josua Faller
About the generalizability of deep learning based image quality assessment in mammography. Mach. Learn. Sci. Technol. 4(4): 45001 (2023) - [j20]Jörg Martin
Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression. Neural Process. Lett. 55(4): 4799-4818 (2023) - 2022
- [j19]Narbota Amanova
Explainability for deep learning in mammography image quality assessment. Mach. Learn. Sci. Technol. 3(2): 25015 (2022) - [j18]Selma Metzner
Assessment of Subsampling Schemes for Compressive Nano-FTIR Imaging. IEEE Trans. Instrum. Meas. 71: 1-8 (2022) - [i10]Manuel Marschall, Gerd Wübbeler, Franko Schmähling, Clemens Elster:
Generative models and Bayesian inversion using Laplace approximation. CoRR abs/2203.07755 (2022) - [i9]Selma Metzner, Bernd Kästner, Manuel Marschall, Gerd Wübbeler, Stefan Wundrack, Andrey Bakin, Arne Hoehl, Eckart Rühl, Clemens Elster:
Assessment of sub-sampling schemes for compressive nano-FTIR imaging. CoRR abs/2204.05531 (2022) - 2021
- [j17]Jörg Martin
Detecting unusual input to neural networks. Appl. Intell. 51(4): 2198-2209 (2021) - [j16]Lara Hoffmann
Uncertainty quantification by ensemble learning for computational optical form measurements. Mach. Learn. Sci. Technol. 2(3): 35030 (2021) - [i8]Lara Hoffmann, Ines Fortmeier, Clemens Elster:
Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements. CoRR abs/2103.01259 (2021) - [i7]Jörg Martin, Clemens Elster:
Errors-in-Variables for deep learning: rethinking aleatoric uncertainty. CoRR abs/2105.09095 (2021) - [i6]Lara Hoffmann, Clemens Elster:
Deep Ensembles from a Bayesian Perspective. CoRR abs/2105.13283 (2021) - [i5]Franko Schmähling, Jörg Martin, Clemens Elster:
A framework for benchmarking uncertainty in deep regression. CoRR abs/2109.09048 (2021) - 2020
- [j15]Jörg Martin
Inspecting adversarial examples using the fisher information. Neurocomputing 382: 80-86 (2020) - [j14]Tobias Kretz
Mammography Image Quality Assurance Using Deep Learning. IEEE Trans. Biomed. Eng. 67(12): 3317-3326 (2020) - [i4]Jörg Martin, Clemens Elster:
Detecting unusual input to neural networks. CoRR abs/2006.08278 (2020) - [i3]Lara Hoffmann, Clemens Elster:
Deep Neural Networks for Computational Optical Form Measurements. CoRR abs/2007.00319 (2020)
2010 – 2019
- 2019
- [j13]Judith Lehnert, Christoph Kolbitsch
Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging. SIAM J. Imaging Sci. 12(4): 2035-2062 (2019) - [i2]Jörg Martin, Clemens Elster:
Inspecting adversarial examples using the Fisher information. CoRR abs/1909.05527 (2019) - 2018
- [j12]Katy Klauenberg
Reducing sample size by tightening test conditions. Qual. Reliab. Eng. Int. 34(3): 333-346 (2018) - [j11]Alexander Khanin
Assessment of CT Image Quality Using a Bayesian Framework. IEEE Trans. Medical Imaging 37(12): 2687-2694 (2018) - 2017
- [j10]Clemens Elster, Gerd Wübbeler:
Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances. Comput. Stat. 32(1): 51-69 (2017) - [j9]Gerd Wübbeler, Clemens Elster:
A Large-Scale Optimization Method Using a Sparse Approximation of the Hessian for Magnetic Resonance Fingerprinting. SIAM J. Imaging Sci. 10(3): 979-1004 (2017) - 2016
- [i1]Sascha Eichstädt, Natallia Makarava, Clemens Elster:
On the evaluation of uncertainties for state estimation with the Kalman filter. CoRR abs/1605.01235 (2016) - 2012
- [j8]Clemens Elster, Ignacio Lira:
On the choice of a noninformative prior for Bayesian inference of discretized normal observations. Comput. Stat. 27(2): 219-235 (2012) - [j7]Ignacio Lira, Anna Chunovkina, Clemens Elster, Wolfgang Woger:
Analysis of Key Comparisons Incorporating Knowledge About Bias. IEEE Trans. Instrum. Meas. 61(8): 2079-2084 (2012) - 2010
- [j6]Sascha Eichstädt
Dynamic Uncertainty for Compensated Second-Order Systems. Sensors 10(8): 7621-7631 (2010) - [c2]Florian Kohl, Gerd Wübbeler, Dorothea Kolossa
Noise Adjusted PCA for Finding the Subspace of Evoked Dependent Signals from MEG Data. LVA/ICA 2010: 442-449
2000 – 2009
- 2009
- [j5]Gerd Wübbeler, Clemens Elster, Thomas M. Reichel, Rolf Judaschke:
Determination of the Complex Residual Error Parameters of a Calibrated One-Port Vector Network Analyzer. IEEE Trans. Instrum. Meas. 58(9): 3238-3244 (2009) - [c1]Florian Kohl, Gerd Wübbeler, Dorothea Kolossa
Non-independent BSS: A Model for Evoked MEG Signals with Controllable Dependencies. ICA 2009: 443-450 - 2007
- [j4]Gerd Wübbeler, Manuel Stavridis, Dieter Kreiseler, Ralf-Dieter Bousseljot, Clemens Elster:
Verification of humans using the electrocardiogram. Pattern Recognit. Lett. 28(10): 1172-1175 (2007) - 2005
- [j3]R. Arnold Nicolaus, Clemens Elster:
Diameter determination of Avogadro Spheres #1 and #2. IEEE Trans. Instrum. Meas. 54(2): 872-876 (2005) - 2004
- [j2]A. Salajegheh, Alfred Link, Clemens Elster, Martin Burghoff, Tilmann Heinrich Sander, Lutz Trahms, David Poeppel:
Systematic latency variation of the auditory evoked M100: from average to single-trial data. NeuroImage 23(1): 288-295 (2004)
1990 – 1999
- 1997
- [j1]Clemens Elster, Arnold Neumaier:
A Method of Trust Region Type for Minimizing Noisy Functions. Computing 58(1): 31-46 (1997)
Coauthor Index
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last updated on 2024-06-10 20:32 CEST by the dblp team
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