Lukas Tuggener
Lukas Tuggener
ZHAW
School of Engineering
Machine Perception & Cognition Group
Technikumstrasse 71
8400 Winterthur
Projects
- Master3D – 3D-Master for a Digitized Manufacturing Platform / Team member / Project completed
- Practical data efficient deep learning trough contrastive self-supervised learning / Project leader / Project completed
- OSR4H – Open Set Recognition for Hematology / Team member / Project completed
- RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Team member / Project completed
- Ada – Advanced Algorithms for an Artificial Data Analyst / Team member / Project completed
- Libra: A One-Tool Solution for MLD4 Compliance / Team member / Project completed
- DeepScore: Digital Music Stand with Musical Understanding via Active Sheet Technology / Team member / Project completed
Publications
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Tuggener, Lukas; Emberger, Raphael; Ghosh, Adhiraj; Sager, Pascal; Satyawan, Yvan Putra; Montoya, Javier; Goldschagg, Simon; Seibold, Florian; Gut, Urs; Ackermann, Philipp; Schmidhuber, Jürgen; Stadelmann, Thilo,
2024.
Real world music object recognition.
Transactions of the International Society for Music Information Retrieval.
7(1), pp. 1-14.
Available from: https://doi.org/10.5334/tismir.157
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Tuggener, Lukas; Schmidhuber, Jürgen; Stadelmann, Thilo,
2022.
Is it enough to optimize CNN architectures on ImageNet?.
Frontiers in Computer Science.
4(1041703).
Available from: https://doi.org/10.3389/fcomp.2022.1041703
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Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo,
2020.
Design patterns for resource-constrained automated deep-learning methods.
AI.
1(4), pp. 510-538.
Available from: https://doi.org/10.3390/ai1040031
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Tuggener, Lukas; Sager, Pascal; Taoudi-Benchekroun, Yassine; Grewe, Benjamin F.; Stadelmann, Thilo,
2024.
So you want your private LLM at home? : a survey and benchmark of methods for efficient GPTs [paper].
In:
11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-30279
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Emberger, Raphael; Boss, Jens Michael; Baumann, Daniel; Seric, Marko; Huo, Shufan; Tuggener, Lukas; Keller, Emanuela; Stadelmann, Thilo,
2023.
Video object detection for privacy-preserving patient monitoring in intensive care [paper].
In:
2023 10th IEEE Swiss Conference on Data Science (SDS).
10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023.
IEEE.
pp. 85-88.
Available from: https://doi.org/10.1109/SDS57534.2023.00019
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Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [paper].
In:
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Springer.
Available from: https://doi.org/10.21256/zhaw-22061
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Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo,
2021.
The DeepScoresV2 dataset and benchmark for music object detection [paper].
In:
2020 25th International Conference on Pattern Recognition (ICPR).
25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021.
IEEE.
pp. 9188-9195.
Available from: https://doi.org/10.1109/ICPR48806.2021.9412290
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Tuggener, Lukas; Amirian, Mohammadreza; Rombach, Katharina; Lörwald, Stefan; Varlet, Anastasia; Westermann, Christian; Stadelmann, Thilo,
2019.
Automated machine learning in practice : state of the art and recent results [paper].
In:
2019 6th Swiss Conference on Data Science (SDS).
6th Swiss Conference on Data Science (SDS), Bern, 14. Juni 2019.
IEEE.
pp. 31-36.
Available from: https://doi.org/10.1109/SDS.2019.00-11
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Elezi, Ismail; Tuggener, Lukas; Pelillo, Marcello; Stadelmann, Thilo,
2018.
DeepScores and Deep Watershed Detection : current state and open issues [paper].
In:
Proceedings of the 1st International Workshop on Reading Music Systems.
1st International Workshop on Reading Music Systems at ISMIR 2018, Paris, France, 20 September 2018.
Paris:
Society for Music Information Retrieval.
pp. 13-14.
Available from: https://doi.org/10.21256/zhaw-4777
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Stadelmann, Thilo; Amirian, Mohammadreza; Arabaci, Ismail; Arnold, Marek; Duivesteijn, Gilbert François; Elezi, Ismail; Geiger, Melanie; Lörwald, Stefan; Meier, Benjamin Bruno; Rombach, Katharina; Tuggener, Lukas,
2018.
Deep learning in the wild [paper].
In:
Artificial Neural Networks in Pattern Recognition.
8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.
Springer.
pp. 17-38.
Lecture Notes in Computer Science ; 11081.
Available from: https://doi.org/10.1007/978-3-319-99978-4_2
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Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Stadelmann, Thilo,
2018.
Deep watershed detector for music object recognition [paper].
In:
Proceedings of the 19th International Society for Music Information Retrieval Conference.
19th International Society for Music Information Retrieval Conference, Paris, 23-27 September 2018.
Paris:
Society for Music Information Retrieval.
Available from: https://doi.org/10.21256/zhaw-3760
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Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Pelillo, Marcello; Stadelmann, Thilo,
2018.
DeepScores : a dataset for segmentation, detection and classification of tiny objects [paper].
In:
2018 24th International Conference on Pattern Recognition (ICPR).
24th International Conference on Pattern Recognition (ICPR 2018), Beijing, China, 20-28 August 2018.
IEEE.
pp. 1-6.
Available from: https://doi.org/10.1109/ICPR.2018.8545307
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Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo,
2019.
Efficient deep CNNs for cross-modal automated computer vision under time and space constraints [paper].
In:
ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-18357