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Jan MacDonald
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2020 – today
- 2024
- [i12]Joana Reuss, Jan MacDonald, Simon Becker, Lorenz Richter, Marco Körner:
EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification. CoRR abs/2407.17458 (2024) - 2023
- [j4]Martin Genzel
, Jan MacDonald
Solving Inverse Problems With Deep Neural Networks - Robustness Included? IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1119-1134 (2023) - 2022
- [b1]Jan MacDonald:
The reliability of deep learning for signal and image processing: Interpretability, robustness, and accuracy. Technical University of Berlin, Germany, 2022 - [c4]Jan MacDonald, Stephan Wäldchen:
A Complete Characterisation of ReLU-Invariant Distributions. AISTATS 2022: 1457-1484 - [c3]Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März:
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. ICML 2022: 7368-7381 - [c2]Jan MacDonald, Mathieu Besançon, Sebastian Pokutta:
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. ICML 2022: 14699-14716 - [i11]Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März:
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. CoRR abs/2206.07050 (2022) - [i10]Theophil Trippe, Martin Genzel, Jan MacDonald, Maximilian März:
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. CoRR abs/2211.10103 (2022) - 2021
- [j3]Luis Oala
Detecting failure modes in image reconstructions with interval neural network uncertainty. Int. J. Comput. Assist. Radiol. Surg. 16(12): 2089-2097 (2021) - [j2]Stephan Wäldchen, Jan MacDonald, Sascha Hauch, Gitta Kutyniok
The Computational Complexity of Understanding Binary Classifier Decisions. J. Artif. Intell. Res. 70: 351-387 (2021) - [c1]Jan MacDonald, Maximilian März, Luis Oala, Wojciech Samek:
Interval Neural Networks as Instability Detectors for Image Reconstructions. Bildverarbeitung für die Medizin 2021: 324-329 - [i9]Martin Genzel, Jan MacDonald, Maximilian März:
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry. CoRR abs/2106.00280 (2021) - [i8]Jan MacDonald, Mathieu Besançon, Sebastian Pokutta:
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. CoRR abs/2110.08105 (2021) - [i7]Jan MacDonald, Stephan Wäldchen:
A Complete Characterisation of ReLU-Invariant Distributions. CoRR abs/2112.06532 (2021) - 2020
- [i6]Luis Oala, Cosmas Heiss, Jan MacDonald, Maximilian März, Wojciech Samek, Gitta Kutyniok:
Interval Neural Networks: Uncertainty Scores. CoRR abs/2003.11566 (2020) - [i5]Jan MacDonald, Maximilian März, Luis Oala, Wojciech Samek:
Interval Neural Networks as Instability Detectors for Image Reconstructions. CoRR abs/2003.13471 (2020) - [i4]Martin Genzel, Jan MacDonald, Maximilian März:
Solving Inverse Problems With Deep Neural Networks - Robustness Included? CoRR abs/2011.04268 (2020)
2010 – 2019
- 2019
- [i3]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, Christian Kümmerle, Gitta Kutyniok, Sebastian Lunz, Jan MacDonald, Ryan Malthaner, Gregory Naisat, Ariel Neufeld, Philipp Christian Petersen, Rafael Reisenhofer, Jun-Da Sheng, Laura Thesing, Philipp Trunschke, Johannes von Lindheim, David Weber, Melanie Weber:
The Oracle of DLphi. CoRR abs/1901.05744 (2019) - [i2]Stephan Wäldchen, Jan MacDonald, Sascha Hauch, Gitta Kutyniok:
The Computational Complexity of Understanding Network Decisions. CoRR abs/1905.09163 (2019) - [i1]Jan MacDonald, Stephan Wäldchen, Sascha Hauch, Gitta Kutyniok:
A Rate-Distortion Framework for Explaining Neural Network Decisions. CoRR abs/1905.11092 (2019) - 2018
- [j1]Jan MacDonald, Lars Ruthotto
Improved Susceptibility Artifact Correction of Echo-Planar MRI using the Alternating Direction Method of Multipliers. J. Math. Imaging Vis. 60(2): 268-282 (2018)
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last updated on 2024-08-25 19:14 CEST by the dblp team
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