Improved Susceptibility Artifact Correction of Echo-Planar MRI using the Alternating Direction Method of Multipliers. - Jan MacDonald; Lars Ruthotto (J. Math. Imaging Vis., 2018)

The Oracle of DLphi. - Dominik Alfke; Weston Baines; Jan Blechschmidt; Mauricio J. del Razo Sarmina; Amnon Drory; Dennis Elbrächter; Nando Farchmin; Matteo Gambara; Silke Glas; Philipp Grohs; Peter Hinz; Danijel Kivaranovic; et al. (CoRR, 2019)

The Computational Complexity of Understanding Network Decisions. - Stephan Wäldchen; Jan MacDonald; Sascha Hauch; Gitta Kutyniok (CoRR, 2019)

A Rate-Distortion Framework for Explaining Neural Network Decisions. - Jan MacDonald; Stephan Wäldchen; Sascha Hauch; Gitta Kutyniok (CoRR, 2019)

Interval Neural Networks: Uncertainty Scores. - Luis Oala; Cosmas Heiss; Jan MacDonald; Maximilian März; Wojciech Samek; Gitta Kutyniok (CoRR, 2020)

Interval Neural Networks as Instability Detectors for Image Reconstructions. - Jan MacDonald; Maximilian März; Luis Oala; Wojciech Samek (Bildverarbeitung für die Medizin, 2021)

Detecting failure modes in image reconstructions with interval neural network uncertainty. - Luis Oala; Cosmas Heiß; Jan MacDonald; Maximilian März; Gitta Kutyniok; Wojciech Samek (Int. J. Comput. Assist. Radiol. Surg., 2021)

AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry. - Martin Genzel; Jan MacDonald; Maximilian März (CoRR, 2021)

The Computational Complexity of Understanding Binary Classifier Decisions. - Stephan Wäldchen; Jan MacDonald; Sascha Hauch; Gitta Kutyniok (J. Artif. Intell. Res., 2021)

Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. - Jan MacDonald; Mathieu Besançon; Sebastian Pokutta (Zenodo, 2021)

A Complete Characterisation of ReLU-Invariant Distributions. - Jan MacDonald; Stephan Wäldchen (AISTATS, 2022)

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. - Martin Genzel; Ingo Gühring; Jan MacDonald; Maximilian März (ICML, 2022)

Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. - Jan MacDonald; Mathieu Besançon; Sebastian Pokutta (ICML, 2022)

Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. - Theophil Trippe; Martin Genzel; Jan MacDonald; Maximilian März (CoRR, 2022)

The reliability of deep learning for signal and image processing: Interpretability, robustness, and accuracy. - Jan MacDonald (2022)

Solving Inverse Problems With Deep Neural Networks - Robustness Included? - Martin Genzel; Jan MacDonald; Maximilian März (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification. - Joana Reuss; Jan MacDonald; Simon Becker; Lorenz Richter; Marco Körner 001 (CoRR, 2024)

Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML Dataset. - Joana Reuss; Jan MacDonald; Simon Becker; Konrad Schultka; Lorenz Richter; Marco Körner 001 (CoRR, 2025)