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2020 – today
- 2026
[j10]Svetlana Ionova, Henri Greil, Patrick Mäder, Marco Seeland:
Deep learning based detection of wild bee parasites under natural conditions. Ecol. Informatics 95: 103754 (2026)- [j9]Marius Schlegel
, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder:
Lowering barriers to federated learning: collaboration management and provenance. J. Big Data 13(1): 28 (2026) - [j8]Niklas Penzel, Daniel Scheliga, Hannes Oppermann, Patrick Mäder, Jens Haueisen, Joachim Denzler, Marco Seeland:
Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets. J. Biomed. Informatics 177: 105010 (2026) - [c4]Chenchang Liu, Svetlana Ionova, Patrick Mäder, Marco Seeland:
Order-Level Arthropod Detection Using Deep Learning: Addressing Scale Variability through Synthetic Data. VISAPP (2) 2026: 210-217 - [i5]Chenchang Liu, Felix Fornoff, Annika Grasreiner, Patrick Maeder, Henri Greil, Marco Seeland:
Efficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage. CoRR abs/2603.16652 (2026) - 2025
- [j7]Daniel Scheliga, Patrick Mäder, Marco Seeland:
Privacy preserving federated learning with convolutional variational bottlenecks. Cybersecur. 8(1): 33 (2025) - 2024
- [j6]Daniel Scheliga, Patrick Mäder, Marco Seeland:
Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data. Appl. Artif. Intell. 38(1) (2024) - [c3]Marius Schlegel, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder:
Collaboration Management for Federated Learning. ICDEW 2024: 291-300 - 2023
- [c2]Daniel Scheliga, Patrick Maeder, Marco Seeland:
Dropout Is NOT All You Need to Prevent Gradient Leakage. AAAI 2023: 9733-9741 - [i4]Daniel Scheliga, Patrick Mäder, Marco Seeland:
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks. CoRR abs/2309.04515 (2023) - 2022
- [c1]Daniel Scheliga, Patrick Mäder, Marco Seeland:
PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage. WACV 2022: 3605-3614 - [i3]Daniel Scheliga, Patrick Mäder, Marco Seeland:
Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage. CoRR abs/2208.04767 (2022) - [i2]Daniel Scheliga, Patrick Mäder, Marco Seeland:
Dropout is NOT All You Need to Prevent Gradient Leakage. CoRR abs/2208.06163 (2022) - 2021
- [i1]Daniel Scheliga, Patrick Mäder, Marco Seeland:
PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage. CoRR abs/2108.04725 (2021) - 2020
- [j5]David Boho, Michael Rzanny, Jana Wäldchen, Fabian Nitsche, Alice Deggelmann, Hans Christian Wittich, Marco Seeland, Patrick Mäder:
Flora Capture: a citizen science application for collecting structured plant observations. BMC Bioinform. 21(1): 576 (2020)
2010 – 2019
- 2019
- [j4]Marco Seeland, Michael Rzanny, David Boho, Jana Wäldchen, Patrick Mäder:
Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinform. 20(1): 4:1-4:13 (2019) - [j3]Martin Hofmann, Marco Seeland, Patrick Mäder:
Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices. Int. J. Comput. Vis. 127(2): 207-224 (2019) - 2018
- [j2]Hans Christian Wittich, Marco Seeland, Jana Wäldchen, Michael Rzanny, Patrick Mäder:
Recommending plant taxa for supporting on-site species identification. BMC Bioinform. 19(1): 190:1-190:17 (2018) - [j1]Jana Wäldchen, Michael Rzanny, Marco Seeland, Patrick Mäder:
Automated plant species identification - Trends and future directions. PLoS Comput. Biol. 14(4) (2018)
Coauthor Index
aka: Patrick Maeder
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