Sören Dittmer

Orcid: 0000-0003-2919-4956

According to our database1, Sören Dittmer authored at least 27 papers between 2018 and 2024.

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Bibliography

2024
Recent methodological advances in federated learning for healthcare.
Patterns, 2024

Publisher Correction: The curious case of the test set AUROC.
Nat. Mac. Intell., 2024

The curious case of the test set AUROC.
Nat. Mac. Intell., 2024

Equivariant neural operators for gradient-consistent topology optimization.
J. Comput. Des. Eng., 2024

A study on the adequacy of common IQA measures for medical images.
CoRR, 2024

A study of why we need to reassess full reference image quality assessment with medical images.
CoRR, 2024

FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization.
CoRR, 2024

Data-Driven Convex Regularizers for Inverse Problems.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
Navigating the development challenges in creating complex data systems.
Nat. Mac. Intell., July, 2023

Recent Methodological Advances in Federated Learning for Healthcare.
CoRR, 2023

Reinterpreting survival analysis in the universal approximator age.
CoRR, 2023

Bayesian view on the training of invertible residual networks for solving linear inverse problems.
CoRR, 2023

Invertible residual networks in the context of regularization theory for linear inverse problems.
CoRR, 2023

DL4TO : A Deep Learning Library for Sample-Efficient Topology Optimization.
Proceedings of the Geometric Science of Information - 6th International Conference, 2023

2022
Navigating the challenges in creating complex data systems: a development philosophy.
CoRR, 2022

SELTO: Sample-Efficient Learned Topology Optimization.
CoRR, 2022

Classification of datasets with imputed missing values: does imputation quality matter?
CoRR, 2022

Unsupervised Learning of the Total Variation Flow.
CoRR, 2022

2020
On deep learning applied to inverse problems: a chicken-and-egg problem.
PhD thesis, 2020

Singular Values for ReLU Layers.
IEEE Trans. Neural Networks Learn. Syst., 2020

Regularization by Architecture: A Deep Prior Approach for Inverse Problems.
J. Math. Imaging Vis., 2020

Learned convex regularizers for inverse problems.
CoRR, 2020

Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
CoRR, 2020

Ground Truth Free Denoising by Optimal Transport.
CoRR, 2020

A Deep Prior Approach to Magnetic Particle Imaging.
Proceedings of the Machine Learning for Medical Image Reconstruction, 2020

2019
A Projectional Ansatz to Reconstruction.
CoRR, 2019

2018
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
CoRR, 2018


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