Lukas Tuggener

Projects

• Master3D – 3D-Master for a Digitized Manufacturing Platform / Team member / Project ongoing
• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

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