Publications
F. Altekrüger, A. Denker, P. Hagemann, J. Hertrich, P. Maass, G. Steidl.
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
Inverse Problems 39 (6), 064006, 2023.
J. Antorán, R. Barbano, J. Leuschner, J.M. Hernández-Lobato, B. Jin
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
Transactions on Machine Learning Research, ISSN: 2835-8856, 2023.
(https://openreview.net/forum?id=FWyabz82fH)
C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
Applied Mathematics for Modern Challenges, 2023.
C. Arndt, A. Denker, S. Dittmer, N. Heilenkoetter, M. Iske, T. Kluth, P. Maass and J. Nickel.
Invertible residual networks in the context of regularization theory for linear inverse problems,
Inverse Problems 39 125018, 2023.
DOI: 10.1088/1361-6420/ad0660.
C. W. Bang and V. Didelez.
Do we become wiser with time? On causal equivalence with tiered background knowledge.
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, pages 119 – 129. PMLR, 2023.
E. Dierkes, C. Offen, S. Ober-Blöbaum, and K. Flaßkamp
Hamiltonian neural networks with automatic symmetry detection.
Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(6), 2023
J. Gödeke, G. Rigaud.
Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods.
Inverse Problems, 39(3), 2023.
DOI: 10.1088/1361-6420/acb2ed
D. Hinse, M. Thode, A. Rademacher, K. Pantke, C. Spura.
Numerical identification of position-dependent friction coefficients from measured displacement data in a bolt-nut connection.
Results in Engineering, 19: 101214, 2023.
G. Klaila, V. Vutov, A. Stefanou.
Supervised topological data analysis for MALDI imaging applications
BMC bioinformatics, 2023, 24. Jg., Nr. 1, S. 279.
R. Luschei and W. Brannath .
The effect of estimating prevalences on the population-wise error rate.
ArXiv: 2304.09988, 2023
https://doi.org/10.48550/arXiv.2304.09988
D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenkötter, A. Rademacher, U. Iben, P. Maaß.
Deep learning methods for partial differential equations and related parameter identification problems.
Inverse Problems, 2023.
DOI: 10.1088/1361-6420/ace9d4
D. Ochieng, A.-T. Hoang, and T. Dickhaus .
Multiple testing of composite null hypotheses for discrete data using randomized p-values
Biometrical Journal, 2023.
Wichmann, M., Eden, M, Zvegincev, D. et al.
Modeling the wetting behavior of grinding wheels.
Int J Adv Manuf Technol 128, 1741-1747 (2023).
https://doi.org/10.1007/s00170-023-12002-y
F. Wiesener, B. Bergmann, M. Wichmann, M. Eden, T. Freudenberg, A. Schmidt.
Modeling of heat transfer in tool grinding for multiscale simulations.
Procedia CIRP, 2023.
V. Vutov, T. Dickhaus.
Multiple multi-sample testing under arbitrary covariance dependency
Erscheint in Statistics in Medicine, 2023
online unter: https://arxiv.org/abs/2202.12347.
H. Albers, T. Kluth, T. Knopp.
Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging.
Journal of Magnetism and Magnetic Materials, 541, 168508, Elsevier, 2022.
DOI: 10.1016/j.jmmm.2021.168508
online unter: https://www.sciencedirect.com/science/article/abs/pii/S0304885321007678
H. Albers, T. Knopp, M. Möddel, M. Boberg, T. Kluth.
Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging.
Journal of Magnetism and Magnetic Materials, 543, 168534, Elsevier, 2022.
DOI: 10.1016/j.jmmm.2021.168534
C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, G. Rigaud.
In Focus - hybrid deep learning approaches to the HDC2021 challenge.
Inverse Problems and Imaging, , 2022.
DOI: 10.3934/ipi.2022061
R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maaß, B. Jin.
An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction.
IEEE Transactions on Computational Imaging, 8:1210-1222, 2022.
DOI: 10.1109/TCI.2022.3233188
P. Rink, W. Brannath .
Post-Selection Confidence Bounds for Prediction Performance.
Preprint arXiv:2210.13206. 2022.
Submitted to Machine Learning (Springer).
P. Fernsel .
Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization..
J. Imaging 2021, 7, 194, 2021.
S. Arridge, P. Fernsel, A. Hauptmann.
Joint reconstruction and low-rank decomposition for dynamic inverse problems..
Inverse Problems & Imaging, 2021
DOI: 10.3934/ipi.2021059
J. Le'Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner, J. Schaller, P. Maaß.
Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma..
J. Imaging 2021, 7(4), 71, 2021
S. Schulze, J. Leuschner, E. King.
Blind source separation in polyphonic music recordings using deep neural networks trained via policy gradients..
Signals 2021, 2(4), 637-661, 2021
S. Schulze, E. King.
Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music Recordings.
J AUDIO SPEECH MUSIC PROC.2021, 6 (2021).
DOI: 10.1186/s13636-020-00190-4
A.T. Hoang, T. Dickhaus.
Combining independent p-values in replicability analysis: a comparative study.
Journal of Statistical Computation ans Simulation, 2022.
DOI: 10.1080/00949655.2021.2022678
A.T. Hoang, T. Dickhaus.
On the usage of randomized p-values in the Schweder-Spjøtvoll estimator .
Annals of the Institute of Statistical Mathematics, 2021.
A.T. Hoang, T. Dickhaus.
Randomized p-values for multiple testing and their application
in replicability analysis .
Biometrical Journal, 2021;1-16.
A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
Conditional Invertible Neural Networks for Medical Imaging .
MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021.
E. Dierkes and K. Flaßkamp .
Learning Hamiltonian Systems considering System Symmetries in Neural Networks.
IFAC-PapersOnLine, 54(19):210–216. 2021.
E. Dierkes, C. Meerpohl, K. Flaßkamp and C. Büskens (2021).
Estimation and Mapping of System-Surface Interaction by Combining Nonlinear Optimization and Machine Learning.
IFAC-PapersOnLine, 54(14):138-143. 2021.
E. Dierkes, F. Jung and C. Büskens .
Data-based models of drive technology for automation in automotive production.
Proc. Appl. Math. Mech., 20(1). 2021.
E. Dierkes and K. Flaßkamp (2021).
Learning Mechanical Systems by Hamiltonian Neural Networks.
In Proc. Appl. Math. Mech., 21(1), 2021.
J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
Scientific Data, 8(109), 2021.
DOI: 10.1038/s41597-021-00893-z
J. Leuschner, M. Schmidt, P. Ganguly, V. Andriiashen, S. Coban, A. Denker, D. Bauer, A. Hadjifaradji, K. Batenburg, B. Maass, M. von Eijnatten.
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
MDPI Journal of Imaging, 7(3), 44 S., 2021.
DOI: 10.3390/jimaging7030044 online unter: https://www.mdpi.com/2313-433X/7/3/44
H. Albers, T. Kluth, T. Knopp.
MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
Int. J. Mag. Part. Imag. 6(2), Suppl. 1, 2020,
Article ID: 2009020,
DOI: 10.18416/IJMPI.2020.2009020 (Conference Proceedings)
M. Beckmann, P. Maass and J. Nickel.
Error analysis for filtered back projection reconstruc-tions in Besov spaces.
Inverse Problems, 37(1), IOP Science. 2020.
DOI: doi.org/10.1088/1361-6420/aba5ee.
D. Otero Baguer, J. Leuschner, M. Schmidt.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
Inverse Problems, 36(9), IOPscience, 2020.
DOI: https://doi.org/10.1088/1361-6420/aba415
T. Gerken, S. Grützner.
Dynamic Inverse Wave Problems – Part I: Regularity for the Direct Problem.
Inverse Problems, 36(2), IOPscience, 2020.
DOI: 10.1088/1361-6420/ab47ec
online at: https://iopscience.iop.org/article/10.1088/1361-6420/ab47ec
T. Gerken.
Dynamic Inverse Wave Problems – Part II: Operator Identification and Applications.
Inverse Problems, 36(2), IOPscience, 2020.
DOI: 10.1088/1361-6420/ab47f4
online at: https://iopscience.iop.org/article/10.1088/1361-6420/ab47f4
J. von Schroeder, T. Dickhaus.
Efficient Calculation of the Joint Distribution of Order Statistics.
Computational Statistics & Data Analysis, 144, Elsevier, 2020.
online at: https://doi.org/10.1016/j.csda.2019.106899
H. Haddar, A. Konschin.
Factorization Method for Imaging a Local Perturbation in Inhomogeneous Periodic Layers from Far Field Measurements.
Inverse Problems and Imaging, 14(1):133-152, 2020.
DOI: 10.3934/ipi.2019067
online at: https://www.aimsciences.org/article/doi/10.3934/ipi.2019067
L. Siemer, I. Ovsyannikov, J. Rademacher.
Inhomogeneous domain walls in spintronic nanowires.
Nonlinearity, 2905 33(6), IOPscience, 2020.
T. Kluth, C. Bathke, M. Jiang, P. Maaß.
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
Inverse Problems, 36 124006, 2020
S. Seo, A. Richter, A.-M. Blechschmidt, I. Bougoudis, J.P. Burrows.
Spatial distribution of enhanced BrO and its relation to meteorological parameters in Arctic and Antarctic sea ice regions.
Atmospheric Chemistry and Physics, 20, 12285-12312, 2020
DOI: 10.5194/acp-20-12285-2020
I. Bougoudis, A. Blechschmidt, A. Richter, S. Seo, J. P. Burrows, N. Theys, A. Rinke.
Long-term Time-series of Arctic Tropospheric BrO derived from UV-VIS Satellite Remote Sensing and its Relation to First Year Sea Ice.
Atmospheric Chemistry and Physics, 20, 11869-11892, 2020
DOI: 10.5194/acp-20-11869-2020
M. Steinherr Zazo, J. Rademacher.
Lyapunov coefficients for Hopf bifurcations in systems with piecewise smooth nonlinearity.
SIAM Journal on Applied Dynamical Systems, 19(4):2847-2886, 2020
DOI: 10.1137/20M1343129
S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
Regularization by architecture: A deep prior approach for inverse problems.
Journal of Mathematical Imaging and Vision, :456-470, Springer Verlag, 2020.
DOI: 10.1007/s10851-019-00923-x
online at: http://link.springer.com/article/10.1007/s10851-019-00923-x
M. Lachmann, J. Maldonado, W. Bergmann, F. Jung, M. Weber, C. Büskens.
Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System.
Energies 2020, 13(8), 2084, 2020.
DOI: 10.3390/en13082084
J. Clemens, T. Kluth, T. Reineking.
β - SLAM: Simultaneous Localization an Grid Mapping with Beta Distributions.
Information Fusion, 52:62-75, Elsevier, 2019.
DOI: 10.1016/j.inffus.2018.11.005
T. Kluth, B. Jin.
Enhanced Reconstruction in Magnetic Particle Imaging by Whitening and Randomized SVD Approximation.
Physics in Medicine and Biology, Article ID 125026 64(12), 2019.
M. Westphal, W. Brannath.
Evaluation of Multiple Prediction Models: A Novel View on Model Selection and Performance Assessment.
Statistical Methods in Medical Research, , 2019.
S. Seo, A. Richter, A. Blechschmidt, I. Bougoudis, J. P. Burrows.
First high-resolution BrO column retrievals from TROPOMI .
Atmospheric Measurement Techniques, 12:2913-2932, 2019.
K. Demertzis, L. Iliadis, I. Bougoudis.
Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network.
Neural Computing and Applications, , Springer Verlag, 2019.
DOI: 10.1007/s00521-019-04363-x
A. Konschin, A. Lechleiter.
Reconstruction of a Local Perturbation in Inhomogeneous Periodic Layers from Partial Near Field Measurements.
Inverse Problems, 35(11), 114006, IOPscience, 2019.
S. Dittmer, E. King, P. Maaß.
Singular values for ReLU layers.
IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.
online at: https://ieeexplore.ieee.org/document/8891761
S. Saha, W. Brannath, B. Bornkamp.
Testing multiple dose combinations in clinical trials.
Statistical Methods in Medical Research, , 2019.
T. Kluth, P. Szwargulski, T. Knopp.
Towards Accurate Modeling of the Multidimensional Magnetic Particle Imaging Physics.
New Journal of Physics, Article ID 10303 21, 10 pp., 2019.
online at: https://iopscience.iop.org/article/10.1088/1367-2630/ab4938/pdf
P. Fernsel, P. Maaß.
A Survey on Surrogate Approaches to Non-negative Matrix Factorization.
Vietnam Journal of Mathematics, 46(4):987-1021, Springer Verlag, 2018.
DOI: 10.1007/s10013-018-0315-x
S. Saha, W. Brannath.
Comparison of different approaches for dose response analysis.
Biometrical Journal, 61(1):83-100, WILEY-VCH, 2018.
J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte, J. Kriegsmann, P. Maaß.
Deep Learning for Tumor Classification in Imaging Mass Spectrometry.
Bioinformatics, 34(7):1215-1223, Oxford University Press, 2018.
DOI: 10.1093/bioinformatics/btx724
I. Bougoudis, K. Demertzis, L. Iliadis, V. . Anezakis, A. Papaleonidas.
FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens.
Neural Computing and Applications, 7, 2018.
T. Kluth.
Mathematical models for magnetic particle imaging.
Inverse Problems, Article ID 083001 34(8), 2018.
T. Kluth, B. Jin, G. Li.
On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
Inverse Problems, Article ID 095006 34(9), 2018.
J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maaß.
Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
Bioinformatics, bty909 , 2018.
DOI: 10.1093/bioinformatics/bty909
C. Bathke, T. Kluth, C. Brandt, P. Maaß.
Improved image reconstruction in magnetic particle imaging using structural a priori information.
International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.
DOI: 10.18416/ijmpi.2017.1703015
T. Kluth, P. Maaß.
Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction.
International Journal on Magnetic Particle Imaging, Article ID 1707004 3(2), 10 pages, 2017.
DOI: 10.18416/ijmpi.2017.1707004
T. Gerken, A. Lechleiter.
Reconstruction of a Time-dependent Potential from Wave Measurements.
Inverse Problems, Article ID 094001 33(9), IOPscience, 2017.
(Highlight Paper)
DOI: 10.1088/1361-6420/aa7e07
online at: http://iopscience.iop.org/article/10.1088/1361-6420/aa7e07
C. W. Bang, V. Didelez.
Do we become wiser with time? On causal equivalence with tiered background knowledge.
Erscheint in : Proceedings of the 39th Annual Conference on Uncertainty in Artifical Intelligence, 2023.
F. Wiesener, B. Bergmann, M. Wichmann, M. Eden, T. Freudenberg, A. Schmidt.
Modeling of heat transfer in tool grinding for multiscale simulations.
CIRP CMMO 2023, 31.05.-02.06.2023, Karlsruhe, Deutschland.
Procedia CIRP, S. 6 p., 2023.
H. Albers, T. Kluth.
Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional lissajous-type excitation: An equilibrium model approach.
International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.
DOI: 10.18416/IJMPI.2022.2203048
M. Nitzsche, H. Albers, T. Kluth, B. Hahn.
Compensating model imperfections during image reconstruction via resesop.
International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.
DOI: 10.18416/IJMPI.2022.2203062
P. Rink, W. Brannath.
Multiplicity-adjusted confidence intervals for conditional prediction performance measures.
12th International Conference on Multiple Comparison Procedures, Bremen 30.08.- 02.09.2022.
online unter: https://www.mcp-conference.org/wp-content/uploads/sites/2/2022/08/Abstract-Book_MCP2022_final.pdf
E. Dierkes, K. Flaßkamp.
Learning Mechanical Systems by Hamiltonian Neural Networks.
7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control LHMNC 2021, 11.-13.10.2021.
Proc. Appl.Math. Mech., 20(1).
DOI: 10.1002/pamm.202100116
E. Dierkes, K. Flaßkamp.
Learning Hamiltonian Systems considering System Symmetries in Neural Networks.
7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control LHMNC 2021, 11.-13.10.2021.
IFAC PapersOnLine 54-19 (2021) 210-216.
online unter: https://www.sciencedirect.com/science/article/pii/S2405896321021042
E. Dierkes, C. Meerpohl, K. Flaßkamp, C. Büskens.
Estimation and Mapping of System-Surface Interaction by Combining Nonlinear Optimization and Machine Learning.
3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2021, 15.-17.09.2021.
IFAC PapersOnLine 54-14 (2021) 138-143.
online unter: https://www.sciencedirect.com/science/article/pii/S240589632101747X
I. Mykhailiuk, K. Schäfer, K. Flaßkamp, C. Büskens.
Preferable Minima in Nonlinear Optimization: Definition and Algorithmic Approaches.
13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, 22.09.-24.09.2021.
Erscheint in Proceedings of the 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles.
online unter: https://hessenbox.uni-kassel.de/dl/fi226HzF3AJV3g4LFWM4fWE6/daily_program_2020.pdf?inline
I. Mykhailiuk, K. Schäfer, C. Büskens.
Stability Score for Local Solutions of Unconstrained Parametric Nonlinear Programs.
GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.
DOI: 10.1002/pamm.202100215
M. Schmidt, A. Denker, J. Leuschner.
The Deep Capsule Prior - advantages through complexity.
GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.
DOI: 10.1002/pamm.202100166
M. Schmidt.
Around the clock - capsule networks and image transformations.
PAMM. Proceedings in Applied Mathematics and Mechanics, 20(1):e202000179, 2021.
DOI: https://doi.org/10.1002/pamm.202000179 online unter: https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202000179
A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.
online unter: https://invertibleworkshop.github.io/accepted_papers/index.html
L. Evers.
Benchmarking pre-trained Encoders for real-time Semantic Road Scene Segmentation.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
M. Lachmann, F. Jung, C. Büskens.
Computationally efficient identification of databased models applied to a milk cooling system.
Conference of Computational Interdisciplinary Science, CCIS, 19.03.-22.03.2019, Atlanta, USA.
Campinas: Galoa, 2020.
A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.
online at: https://invertibleworkshop.github.io/accepted_papers/index.html
E. Dierkes, F. Jung, C. Büskens.
Data-based models of drive technology for automation in automotive production.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
M. Möddel, F. Griese, T. Kluth, T. Knopp.
Estimating orientation using multi-contrast MPI.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Infinite Science Publishing, T. Knopp, T. M. Buzug (Eds.), 6(2) pp. 2 pages.
DOI: 10.18416/IJMPI.2020.2009023
F. Tramer, J. Behrmann, N. Carlini, N. Papernot, J. Jacobsen.
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.
International Conference on Machine Learning (ICML), 12.07 - 18.07.2020, Wien, Österreich.
online at: https://arxiv.org/abs/2002.04599
H. Albers, T. Kluth, T. Knopp.
MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Infinite Science Publishing, T. Knopp, T. M. Buzug (Eds.), 6(2) Suppl. 1, 2020.
Article ID 2009020.
DOI: 10.18416/IJMPI.2020.2009020
M. Runge, K. Flaßkamp, C. Büskens.
Model Predictive Control with Online Nonlinear Parameter Identification for a Robotic System.
International Conference on Control, Decision and Information Technologies (CoDIT), 29.06.-02.07.2020, Prag, Tschechien.
Proceedings of CoDIT, 7th International Conference on Control, Decision and Information Technologies (CoDIT), 2020, pp. 312-318.
DOI: 10.1109/CoDIT49905.2020.9263886
I. Mykhailiuk, K. Schäfer, K. Flaßkamp, C. Büskens.
On the Computation of Convergence Regions for Sequential Nonlinear Programming Problems.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
PAMM 20(1), 2021.
I. Mykhailiuk, K. Schäfer, K. Flaßkamp, C. Büskens.
Preferable Minima in Nonlinear Optimization: Definition and Algorithmic Approaches.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.
online at: https://hessenbox.uni-kassel.de/dl/fi226HzF3AJV3g4LFWM4fWE6/daily_program_2020.pdf?inline
M. Runge, K. Flaßkamp, C. Büskens.
Real-time parameter estimation for sensitivity-based LQ regulator adaptation .
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
K. Schäfer, J. Fliege, K. Flaßkamp, C. Büskens.
Reformulating Bilevel Problems by SQP Embedding.
GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, Kassel, 15.03.2020 - 19.03.2020.
T. Kluth, P. Szwargulski, T. Knopp.
Towards accurate modeling of the multidimensional MPI physics.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Infinite Science Publishing, T. Knopp, T. M. Buzug (Eds.), 6(2) pp. 2 pages.
DOI: 10.18416/IJMPI.2020.2009004
J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
On the Invertibility of Invertible Neural Networks.
NeurIPS workshop on Machine Learning with Guarantees, 2019.
online at: https://sites.google.com/view/mlwithguarantees/accepted-papers
K. Schäfer, K. Flaßkamp, J. Fliege, C. Büskens.
A Combined Homotopy-Optimization Approach to Parameter Identification for Dynamical Systems.
GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics, 18.02-22.02.2019, Wien, Österreich.
90, Proc. Appl. Math. Mech., 19, Wiley, 2019.
S. Schulze, E. King.
A Frequency‐Uniform and Pitch‐Invariant Time‐Frequency Representation.
90th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM), 18.02.-22.02.2019, Wien, Österreich.
Proc. Appl. Math. Mech., 19(1):e201900374, 2019.
M. Rick, J. Clemens, L. Sommer, A. Folkers, K. Schill, C. Büskens.
Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion.
10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019), 03.07.-05.07.2019.
DOI: 10.1016/j.ifacol.2019.08.068
T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.
DOI: 10.1063/1.5112634
A. Folkers, M. Rick, C. Büskens.
Controlling an Autonomous Vehicle with Deep Reinforcement Learning.
Intelligent Vehicles Symposium, 09.06.-12.06.2019, Paris, Frankreich.
Proceedings of the 30th IEEE Intelligent Vehicles Symposium, pp. 2025-2031, 2019.
**Best Student Paper
J. Jacobsen, J. Behrmann, R. Zemel, M. Bethge.
Excessive Invariance Causes Adversarial Vulnerability.
International Conference on Learning Representations (ICLR), 2019.
online at: https://openreview.net/forum?id=BkfbpsAcF7
J. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot.
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.
SafeML Workshop, ICLR, 2019.
online at: https://arxiv.org/abs/1903.10484
C. Meerpohl, M. Rick, C. Büskens.
Free-space Polygon Creation based on Occupancy Grid Maps for Trajectory Optimization Methods.
10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019), 03.07.-05.07.2019.
DOI: 10.1016/j.ifacol.2019.08.107
M. Westphal, W. Brannath.
Improving Model Selection by Employing the Test Data.
36th International Conference on Machine Learning, 09.06.-15.06.2019, Los Angeles, USA.
PMLR 97, pp. 6747-6756, 2019.
online at: http://proceedings.mlr.press/v97/westphal19a.html
J. Behrmann, W. Grathwohl, R. T. Chen, D. Duvenaud, J. Jacobsen.
Invertible Residual Networks.
International Conference on Machine Learning (ICML).
Proceedings of Machine Learning Research, 97:573-582, 2019.
**Long Oral
online at: http://proceedings.mlr.press/v97/behrmann19a.html
C. Etmann, S. Lunz, P. Maaß, C. Schönlieb.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability.
36th International Conference on Machine Learning, 09.06.-15.06.2019, Los Angeles, USA.
PMLR 97, 97:1823-1832, 2019.
online at: http://proceedings.mlr.press/v97/etmann19a.html
R. T. Chen, J. Behrmann, D. Duvenaud, J. Jacobsen.
Residual Flows for Invertible Generative Modeling.
Advances in Neural Information Processing Systems (NeurIPS).
32, pp. 9916--9926, 2019.
**Spotlight
online at: https://papers.nips.cc/paper/9183-residual-flows-for-invertible-generative-modeling
T. Kluth, B. Hahn, C. Brandt.
Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging.
International Workshop on Magnetic Particle Imaging 2019.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2019, pp. 23-24, Infinite Science Publishing, 2019.
K. Schäfer, K. Flaßkamp, C. Büskens.
A Numerical Study of the Robustness of Transcription Methods for Parameter Identification Problems.
GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics, 19.03.-23.03.2018, München, Deutschland.
89, Proc. Appl. Math. Mech., 18, Wiley, 2018.
L. Sommer, M. Rick, A. Folkers, C. Büskens.
AO-Car: Transfer of Space Technology to Autonomous Driving with the use of WORHP.
7th International Conference on Astrodynamics Tools and Techniques, 2018.
J. Clemens, C. Meerpohl, V. Schwarting, M. Rick, K. Schill, C. Büskens.
Autonomous In-Ice Exploration of the Saturnian Moon Enceladus.
69th International Astronautical Congress (IAC), 01.10.-05.10.2018, Bremen, Deutschland.
T. Kluth, B. Jin.
Exploiting Ill-Posedness in Magnetic Particle Imaging - System Matrix Approximation via Randomized SVD.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, pp. 127-128, Infinite Science Publishing, 2018.
J. Flötotto, T. Kluth, M. Möddel, T. Knopp, P. Maaß.
Improving Generalization Properties of Measured System Matrices by Using Regularized Total Least Squares Reconstruction in MPI.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, pp. 53-54, Infinite Science Publishing, 2018.
D. Otero Baguer, I. Piotrowska, P. Maaß.
Inverse Problems in designing new structural materials.
7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.
P. Gralla, I. Piotrowska, D. . Rippel, M. Lütjen, P. Maaß.
Inverting Prediction Models in Micro Production for Process Design.
5TH INTERNATIONAL CONFERENCE ON NEW FORMING TECHNOLOGY, 18.09.-21.09.2018, Bremen, Deutschland.
DOI: 10.1051/matecconf/201819015007
C. Bathke, T. Kluth, P. Maaß.
MPI Reconstruction Using Structural Prior Information and Sparsity.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, pp. 129-130, Infinite Science Publishing, 2018.
C. Meerpohl, K. Flaßkamp, C. Büskens.
Optimization Strategies for Real-Time Control of an Autonomous Melting Probe.
2018 American Control Conference (ACC), 2018, Milwaukee, WI, USA.
DOI: 10.23919/ACC.2018.8430877
K. Schäfer, M. Runge, K. Flaßkamp, C. Büskens.
Parameter Identification for Dynamical Systems Using Optimal Control Techniques.
European Control Conference (ECC) 2018, 12.06.-15.06.2018, Limassol, Zypern.
DOI: 10.23919/ECC.2018.8550045
M. Runge, K. Flaßkamp, C. Büskens.
Sequential Solution of Parameter Identification and Optimal Control Problems for Robotic Systems.
89th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM), 19.03.-23.03.2018, München, Deutschland.
Proc. Appl. Math. Mech., 2018.
K. Tracht, A. Onken, P. Gralla, J. H. Emad, N. Kipry, P. Maaß.
Trend-specific clustering for micro mass production of linked parts.
CIRP General Assembly 2018, 19.08-25.08.2018.
CIRP Annals, Manufacturing Technology, 67(1):9-12, Elsevier, 2018.
DOI: 10.1016/j.cirp.2018.04.017
W. Heins, C. Büskens.
Two-Level Forecast-Based Energy and Load Management for Grid-Connected Local Systems Using General Load and Storage Models.
18th International Conference on Environment and Electrical Engineering (EEEIC), 12.06-15.06.2018, Palermo, Italien.
K. Flaßkamp, K. Schäfer, C. Büskens.
Variational Integrators for Parameter Identification of Mechanical Systems.
GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics, 19.03.-23.03.2018, München, Deutschland.
89, Proc. Appl. Math. Mech., 18, Wiley, 2018.
C. Bathke, T. Kluth, C. Brandt, P. Maaß.
Improved image reconstruction in magnetic particle imaging using structural a priori information.
International Workshop on Magnetic Particle Imaging 2017.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, pp. 85, Infinite Science Publishing, 2017.
T. Kluth, P. Maaß.
Model uncertainty in magnetic particle iamging: Motivating nonlinear problems by model-based sparse reconstruction.
International Workshop on Magnetic Particle Imaging 2017.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, pp. 83, Infinite Science Publishing, 2017.
F. Jung, M. Lachmann, C. Büskens.
SmartFarm - Data based optimization for optimal energy management.
88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
Proc. Appl. Math. Mech., 17(1):741-742, 2017.
P. Gralla, I. Piotrowska-Kurczewski, P. Maaß.
Tikhonov Functionals Incorporating Tolerances.
88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
To appear in Proc. Appl. Math. Mech.
P. Gralla, I. Piotrowska-Kurczewski, P. Maaß.
Parameter identification for micro milling processes using inverse problems incorporating tolerances.
International Congress on engineering, design and Manufacturing 2016, 08.09-10.09.2016, Barcelona, Spanien.
M. Schmidt.
Around the clock - capsule networks and image transformations.
PAMM.
To appear in Proceedings in Applied Mathematics and Mechanics.
P. Gralla, I. Piotrowska-Kurczewski, D. . Rippel, M. Lütjen, P. Maaß.
Eine Methode zur Invertierung von Vorhersagemodellen in der Mikrofertigung.
8. Kolloquium Mikroproduktion, 27.11.-28.11.2017, Bremen, Deutschland.
P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
Mathematische Architekturen für Neuronale Netze.
Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.
DOI: 10.1007/s00591-022-00325-y
T. Gerken.
Dynamic Inverse Problems for the Acoustic Wave Equation.
Time-dependent Problems in Imaging and Parameter Identification, Time-dependent Problems in Imaging and Parameter Identification, Springer Verlag, 2020.
S. Görres, S. Böttcher, P. Rink, W. Brannath.
StaVaCare 2.0 - Zusammenhänge zwischen Care-, Case-Mix, Organisation und Qualität in Pflegeheimen.
Schriftenreihe zur Weiterentwicklung der Pflegeversicherung, GKV Spitzenverband, 2020.
O. Riemer, P. Maaß, F. E. Elsner-Dörge, P. Gralla, J. Vehmeyer, M. Willert, A. Meier, I. Zahn.
Predictive compensation measures for the prevention of shape deviations of mircomilled dental products.
Cold Micro Metal Forming, Springer Verlag, 2018.
B. Denkena, P. Maaß, A. Schmidt, D. Niederwestberg, J. Vehmeyer, C. Niebuhr, P. Gralla.
Thermomechanical Deformation of Complex Workpieces in Milling and Drilling Processes.
Thermal Effects in Complex Machining Processes - Final Report of the DFG Priority Program 1480, D. Biermann, F. Hollmann (Eds.), LNPE, pp. 219-250, Springer Verlag, 2017.
T. Kluth
Model-based to data-driven approaches for parameter identification and image reconstruction in the applied inverse problem of magnetic particle imaging (submitted)
Dissertationsschrift, Universität Bremen, 2020.
S. Dittmer
On deep learning applied to inverse problems - A chicken-and-egg problem (submitted)
Dissertationsschrift, Universität Bremen, 2020.
C. Etmann.
Double Backpropagation with Applications to Robustness and Saliency Map Interpretability.
Dissertationsschrift, Universität Bremen, 2020.
D. Otero Baguer.
Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging.
Dissertationsschrift, Universität Bremen, 2020.
A. Konschin.
Direkte und inverse elektromagnetische Streuprobleme für lokal gestörte periodische Medien.
Dissertationsschrift, Universität Bremen, 2019.
online at: http://nbn-resolving.de/urn:nbn:de:gbv:46-00107835-13
T. Gerken.
Dynamic Inverse Problems for Wave Phenomena.
Dissertationsschrift, Universität Bremen, 2019.
online at: https://nbn-resolving.de/urn:nbn:de:gbv:46-00107730-18
M. Westphal.
Model Selection and Evaluation in Supervised Machine Learning.
Dissertationsschrift, Universität Bremen, 2019.
DOI: https://doi.org/10.26092/elib/16
J. Behrmann.
Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge.
Dissertationsschrift, Universität Bremen, 2019.
online at: https://elib.suub.uni-bremen.de/peid/D00108536.html
S. Saha.
Multiple testing and modeling in dose-response studies.
Dissertationsschrift, Universität Bremen, 2018.
H. Albers, T. Kluth.
Time-dependent parameter identification in a Fokker-Planck equation based magnetization model of large ensembles of nanoparticles.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2307.03560
J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2203.00479
C. Arndt, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, J. Nickel.
Bayesian view on the training of invertible residual networks for solving linear inverse problems.
Zur Veröffentlichung eingereicht.
online unter: https://www.x-mol.net/paper/article/1682514725633245184
R. Barbano, A. Denker, H. Chung, T. Roh, S. Arrdige, P. Maass, B. Jin, J. Ye.
Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems.
arXiv: arxiv.org/abs/2308.14409, Under Review.
R. Barbano, J. Antorán, J. Leuschner, J.M. Hernández-Lobato, Ž. Kereta, B. Jin (2023)
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.
arXiv preprint, under review (arXiv:2302.10279)
R. Barbano, J. Leuschner, J. Antorán, B. Jin, J. M. Hernández-Lobato.
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2207.05714
M. Beckmann, A. Bhandari, M. Iske (2023).
Fourier-Domain Inversion for the Modulo Radon Transform.
arxiv.org/abs/2307.13114
Beckmann, M. and Heilenkötter, N. (2023)
Equivariant neural networks for indirect measurements,
Preprint available at arXiv:2306.16506
J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2006.09347
J. Behrmann, S. Dittmer, P. Fernsel, P. Maaß.
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
Zur Veröffentlichung eingereicht
online at: https://arxiv.org/abs/1806.09730
I. Bougoudis, A. Blechschmidt, A. Richter, S. Seo, J. P. Burrows, N. Theys, A. Rinke.
Long-term Time-series of Arctic Tropospheric BrO derived from UV-VIS Satellite Remote Sensing and its Relation to First Year Sea Ice.
Zur Veröffentlichung eingereicht.
DOI: 10.5194/acp-2020-116
T. Dickhaus, R. Heller, A.T. Hoang.
Multiple testing of partial conjunction null hypotheses with conditional p-values based on combination test statistics..
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2110.06692
S. Dittmer, T. Kluth, D. Otero Baguer, P. Maaß.
A deep prior approach to magnetic particle imaging.
Zur Veröffentlichung eingereicht.
S. Dittmer, T. Kluth, M. Henriksen, P. Maaß.
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2007.01593
S. Dittmer, C. Schönlieb, P. Maaß.
Ground Truth Free Denoising by Optimal Transport.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2007.01575
S. Dittmer, P. Maaß.
A Projectional Ansatz to Reconstruction.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/1907.04675
M. Eden, T. Freudenberg.
Effective Heat Transfer Between a Porous Medium and a Fluid Layer: Homogenization and Simulation.
Zur Veröffentlichung eingereicht.
DOI: 10.48550/arXiv.2212.09291
C. Etmann.
A Closer Look at Double Backpropagation.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/1906.06637
C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maaß.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/1912.05459
P. Fernsel,P. Maass.
Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2112.07641
T. Freudenberg, M. Eden (2023).
Homogenization and simulation of heat transfer through a thin grain layer.
ArXiv, arXiv:2312.02704.
R. . Grotheer, T. . Strauss, P. Gralla, T. Khan.
Alternatives for Generating a Reduced Basis to Solve the Hyperspectral Diffuse Optical Tomography Model.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/1803.00948
R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2302.02181
G. Klaila, L. Ranke, A. Stefanou (2023).
Stability of the Persistence Transformation,
https://arxiv.org/pdf/2310.05559.pdf
T. Kluth, H. Albers.
Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging.
Erscheint in Oberwolfach Reports
T. Kluth, C. Bathke, M. Jiang, P. Maaß.
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2004.13091
T. Kluth, B. Jin.
L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2001.06083
T. Kluth, B. Jin.
Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
Zur Veröffentlichung eingereicht.
A. Konschin.
Electromagnetic wave scattering from locally perturbed periodic inhomogeneous layers.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/1908.08457
A. Konschin.
Numerical scheme for electromagnetic scattering on perturbed periodic inhomogeneous media and reconstruction of the perturbation.
Zur Veröffentlichung eingereicht.
J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.
Zur Veröffentlichung eingereicht.
online at: arXiv:1910.01113
T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
Zur Veröffentlichung eingereicht.
online unter: https://aps.arxiv.org/abs/2307.10474
M. Nittscher, M.F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß (2023)
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
Accepted at Medical Imaging with Deep Learning conference (arXiv:2303.15748)
I. Piotrowska-Kurczewski, G. Sfakianaki.
Tikhonov functionals with a tolerance measure introduced in the regularization.
Zur Veröffentlichung eingereicht.
online at: http://arxiv.org/abs/2007.06431
P. Rink, W. Brannath.
Post-Selection Confidence Bounds for Prediction Performance.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2210.13206.
S. Seo, A. Richter, A. Blechschmidt, I. Bougoudis, J. P. Burrows.
Spatial distribution of enhanced BrO and its relation to meteorological parameters in Arctic and Antarctic sea ice regions.
Zur Veröffentlichung eingereicht.
DOI: 10.5194/acp-2019-996
I. Singh, A. Denker, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maass, S. Arridge.
Score-Based Generative Models for PET Image Reconstruction.
arXiv: arxiv.org/abs/2308.14190
Under Review.
M. Steinherr Zazo, J.D.M. Rademacher.
Nonlinear effects of stabilization in ship models with non-smooth nonlinearities using P-control.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2104.10663
J. von Schroeder.
Stable Feature Selection with Applications to MALDI Imaging Mass Spectrometry Data.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/2006.15077
V. Vutov, T. Dickhaus.
Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2108.08123
M. Westphal, A. Zapf, W. Brannath.
A multiple testing framework for diagnostic accuracy studies with co-primary endpoints.
Zur Veröffentlichung eingereicht.
online at: https://arxiv.org/abs/1911.02982
M. Westphal.
Simultaneous Inference for Multiple Proportions: A Multivariate Beta-Binomial Model.
Zur Veröffentlichung eingereicht.online at: https://arxiv.org/abs/1911.00098
Dennis Zvegincev (March 2022).
A Tikhonov approach to level set curvature computation.
ArXiv, abs/2203.12558. [https://doi.org/10.48550/arXiv.2203.12558]
C. Brandt, M. Hamann, J. Leuschner.
Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
Berichte aus der Technomathematik 19–01, Universität Bremen, 2019.
J. von Schroeder, T. Dickhaus, T. Bodnar.
Reverse Stress Testing in Skew-Elliptical Models.
Research Report 2019:04, 2019.
online at: Mathematical Statistics, Stockholm University
A. Folkers.
Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning.
BestMasters, 75 pages, Springer Verlag, Universität Bremen, 2019.