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2020 – today
- 2024
[j7]David Erzmann, Sören Dittmer:
Equivariant neural operators for gradient-consistent topology optimization. J. Comput. Des. Eng. 11(3): 91-100 (2024)- [j6]Michael Roberts
, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb:
The curious case of the test set AUROC. Nat. Mac. Intell. 6(4): 373-376 (2024) - [j5]Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb:
Publisher Correction: The curious case of the test set AUROC. Nat. Mac. Intell. 6(4): 494 (2024) - [j4]Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, Martijn Schut, Folkert W. Asselbergs, Sujoy Kar, Suthesh Sivapalaratnam, Sophie Williams, Mickey Koh, Yvonne Henskens, Bart de Wit, Umberto D'alessandro, Bubacarr Bah, Ousman Secka, Parashkev Nachev, Rajeev Gupta, Sara Trompeter, Nancy Boeckx, Christine van Laer, Gordon A. Awandare, Kwabena Sarpong, Lucas Amenga-Etego, Mathie P. G. Leers, Mirelle Huijskens, Samuel McDermott, Willem H. Ouwehand, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas S. Gleadall, Michael Roberts:
Recent methodological advances in federated learning for healthcare. Patterns 5(6): 101006 (2024) - [c3]Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola B. Schönlieb:
Data-Driven Convex Regularizers for Inverse Problems. ICASSP 2024: 13386-13390 - [i19]Fan Zhang, Carlos Esteve Yagüe, Sören Dittmer, Carola-Bibiane Schönlieb, Michael Roberts:
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization. CoRR abs/2405.19000 (2024) - [i18]Anna Breger, Ander Biguri, Malena Sabaté Landman, Ian Selby, Nicole Amberg, Elisabeth Brunner, Janek Gröhl, Sepideh Hatamikia, Clemens Karner, Lipeng Ning, Sören Dittmer, Michael Roberts, AIX-COVNET Collaboration, Carola-Bibiane Schönlieb:
A study of why we need to reassess full reference image quality assessment with medical images. CoRR abs/2405.19097 (2024) - [i17]Anna Breger, Clemens Karner, Ian Selby, Janek Gröhl, Sören Dittmer, Edward Lilley, Judith Babar, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, Michael Roberts, Carola-Bibiane Schönlieb:
A study on the adequacy of common IQA measures for medical images. CoRR abs/2405.19224 (2024) - 2023
- [j3]Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, Ian Selby, Anna Breger, Matthew Thorpe, Jonathan R. Weir-McCall, Effrossyni Gkrania-Klotsas, Anna Korhonen, Emily R. Jefferson, Georg Langs, Guang Yang, Helmut Prosch, Jan Stanczuk, Jing Tang, Judith Babar, Lorena Escudero Sanchez, Philip Teare, Mishal Patel, Marcel Wassin, Markus Holzer, Nicholas Walton, Pietro Lió, Tolou Shadbahr, Evis Sala, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb:
Navigating the development challenges in creating complex data systems. Nat. Mac. Intell. 5(7): 681-686 (2023) - [c2]David Erzmann, Sören Dittmer, Henrik Harms, Peter Maaß:
DL4TO : A Deep Learning Library for Sample-Efficient Topology Optimization. GSI (1) 2023: 543-551 - [i16]Clemens Arndt, Alexander Denker, Sören Dittmer, Nick Heilenkötter, Meira Iske, Tobias Kluth, Peter Maass, Judith Nickel:
Invertible residual networks in the context of regularization theory for linear inverse problems. CoRR abs/2306.01335 (2023) - [i15]Clemens Arndt, Sören Dittmer, Nick Heilenkötter, Meira Iske, Tobias Kluth, Judith Nickel:
Bayesian view on the training of invertible residual networks for solving linear inverse problems. CoRR abs/2307.10431 (2023) - [i14]Sören Dittmer, Michael Roberts, Jacobus Preller, AIX-COVNET Collaboration, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb:
Reinterpreting survival analysis in the universal approximator age. CoRR abs/2307.13579 (2023) - [i13]Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, BloodCounts Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas S. Gleadall, Michael Roberts:
Recent Methodological Advances in Federated Learning for Healthcare. CoRR abs/2310.02874 (2023) - [i12]Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb:
The curious case of the test set AUROC. CoRR abs/2312.16188 (2023) - 2022
- [i11]Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb:
Unsupervised Learning of the Total Variation Flow. CoRR abs/2206.04406 (2022) - [i10]Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian D. Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramón Viñas Torné, Evis Sala, Pietro Lió, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb:
Classification of datasets with imputed missing values: does imputation quality matter? CoRR abs/2206.08478 (2022) - [i9]Sören Dittmer, David Erzmann, Henrik Harms, Peter Maass:
SELTO: Sample-Efficient Learned Topology Optimization. CoRR abs/2209.05098 (2022) - [i8]Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, AIX-COVNET Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb:
Navigating the challenges in creating complex data systems: a development philosophy. CoRR abs/2210.13191 (2022) - 2020
- [b1]Sören Dittmer:
On deep learning applied to inverse problems: a chicken-and-egg problem. University of Bremen, Germany, 2020 - [j2]Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer:
Regularization by Architecture: A Deep Prior Approach for Inverse Problems. J. Math. Imaging Vis. 62(3): 456-470 (2020) - [j1]Sören Dittmer, Emily J. King, Peter Maass:
Singular Values for ReLU Layers. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3594-3605 (2020) - [c1]Sören Dittmer, Tobias Kluth, Daniel Otero Baguer, Peter Maass:
A Deep Prior Approach to Magnetic Particle Imaging. MLMIR@MICCAI 2020: 113-122 - [i7]Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass:
Ground Truth Free Denoising by Optimal Transport. CoRR abs/2007.01575 (2020) - [i6]Sören Dittmer, Tobias Kluth, Mads Thorstein Roar Henriksen, Peter Maass:
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset. CoRR abs/2007.01593 (2020) - [i5]Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb:
Learned convex regularizers for inverse problems. CoRR abs/2008.02839 (2020)
2010 – 2019
- 2019
- [i4]Sören Dittmer, Peter Maass:
A Projectional Ansatz to Reconstruction. CoRR abs/1907.04675 (2019) - 2018
- [i3]Jens Behrmann, Sören Dittmer, Pascal Fernsel, Peter Maaß:
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks. CoRR abs/1806.09730 (2018) - [i2]Sören Dittmer, Emily J. King, Peter Maass:
Singular Values for ReLU Layers. CoRR abs/1812.02566 (2018) - [i1]Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer:
Regularization by architecture: A deep prior approach for inverse problems. CoRR abs/1812.03889 (2018)
Coauthor Index
aka: Carola B. Schönlieb
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