Leo Schwinn
Dr. Leo Schwinn
Alumnus
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany
- E-Mail: leo.schwinn@fau.de
- Webseite: https://www.mad.tf.fau.de/person/leo-schwinn/
| 01/2023-11/2023 | Postdoc
Machine Learning and Data Analytics Lab, |
| 01/2022-01/2023 | Research Stay
Mila – Quebec AI Institute |
| 09/2019-11/2022 | PhD Candidate
Machine Learning and Data Analytics Lab, |
| 01/2019-03/2019 | Scientific Assistant
IT Security Infrastructures Lab (CS1), |
| 10/2017-08/2019 | M.Sc. in Medical Engineering, „Medical Image and Data Processing“
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition, Machine Learning, Deep Learning |
| 10/2014-10/2017 | B.Sc. in Medical Engineering, „Medical Imaging Systems“
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition |
2024
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Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
International Conference on Machine Learning 2024 DMLR Workshop: Data-centric Machine Learning Research (Wien, 26. Juli 2024 - 27. Juli 2024)
DOI: 10.48550/arXiv.2406.13283
BibTeX: Download - , , , , :
Sensory evaluation of axillary odour samples of younger and older adults by a trained panel
In: Flavour and Fragrance Journal 39 (2024), S. 3-9
ISSN: 0882-5734
DOI: 10.1002/ffj.3762
BibTeX: Download
2023
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Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
2023 International Joint Conference on Neural Networks (IJCNN) (Gold Coast, Australia, 18. Juni 2023 - 23. Juni 2023)
In: Proc. Intl. Joint Conf. Neural Netw. (IJCNN) 2023
DOI: 10.1109/IJCNN54540.2023.10191724
BibTeX: Download - , , , , :
Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach
In: Evolutionary Intelligence (2023)
ISSN: 1864-5909
DOI: 10.1007/s12065-023-00878-4
URL: https://link.springer.com/article/10.1007/s12065-023-00878-4
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Artificial intelligence trend analysis in German business and politics: a web mining approach
In: International Journal of Data Science and Analytics (2023), S. 1-22
ISSN: 2364-415X
DOI: 10.1007/s41060-023-00483-9
URL: https://link.springer.com/article/10.1007/s41060-023-00483-9
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Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study
In: Frontiers in Ecology and Evolution 11 (2023), Art.Nr.: 1107463
ISSN: 2296-701X
DOI: 10.3389/fevo.2023.1107463
BibTeX: Download - , , , :
FastAMI - A Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
AAAI 2023 (Washington, D.C., 7. Februar 2023 - 14. Februar 2023)
In: Proceedings of the AAAI Conference on Artificial Intelligence, 37(7) 2023
DOI: 10.1609/aaai.v37i7.26003
URL: https://ojs.aaai.org/index.php/AAAI/article/view/26003
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Exploring misclassifications of robust neural networks to enhance adversarial attacks
In: Applied Intelligence (2023)
ISSN: 0924-669X
DOI: 10.1007/s10489-023-04532-5
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2022
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xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning
In: Sensors 22 (2022), Art.Nr.: 8474
ISSN: 1424-8220
DOI: 10.3390/s22218474
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Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Proceedings of the 39th International Conference on Machine Learning (Baltimore, USA, 17. Juli 2022 - 23. Juli 2022)
In: PMLR (Hrsg.): 162 2022
Open Access: https://proceedings.mlr.press/v162/schwinn22a.html
URL: https://proceedings.mlr.press/v162/schwinn22a.html
BibTeX: Download
2021
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CLIP: Cheap Lipschitz Training of Neural Networks
International Conference on Scale Space and Variational Methods in Computer Vision
In: Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon (Hrsg.): SSVM 2021: Scale Space and Variational Methods in Computer Vision, Cham: 2021
DOI: 10.1007/978-3-030-75549-2_25
URL: https://arxiv.org/abs/2103.12531
BibTeX: Download - , , , , , , , , , , :
System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data
In: IEEE Access (2021)
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3106791
BibTeX: Download - , , , , , , , :
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Conference on Uncertainty in Artificial Intelligence (UAI) (Online, 27. Juli 2021 - 30. Juli 2021)
URL: https://arxiv.org/abs/2102.12196
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Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
International Joint Conference on Neural Networks (IJCNN) (Online, 18. Juli 2021 - 22. Juli 2021)
DOI: 10.1109/ijcnn52387.2021.9534190
BibTeX: Download - , , , , :
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
URL: https://arxiv.org/abs/2105.10304
BibTeX: Download
2020
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Time matters: Time-aware LSTMs for predictive business process monitoring
International Conference on Process Mining (Padua, 4. Oktober 2020 - 9. Oktober 2020)
In: Proceedings of the ICPM 2020 International Workshops 2020
DOI: 10.1007/978-3-030-72693-5_9
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2019
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Impact of Pathologies on Automatic Age Estimation
Fortschritte der Akustik - DAGA 2019 (DAGA 2019 - 45. Jahrestagung für Akustik) (Rostock, 18. März 2019 - 21. März 2019)
In: Deutsche Gesellschaft für Akustik e.V. (Hrsg.): Fortschritte der Akustik - DAGA 2019, Rostock: 2019
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2019/Schwinn19-IOP.pdf
BibTeX: Download
| Year | Name | Title |
| 2023 | Fabio Rosenthal | Adaptive Resolution Transformers for High-Resolution Image Processing (Master’s Thesis) |
| 2022 | Fabio Rosenthal | Unsupervised Modelling of Free-Viewing Human Scanptahs with Biologically Constrained RNNs (Master’s Project) |
| 2022 | Tim Löhr | AI Trend Detection in Healthcare by applying Topic Clustering and Sentiment Analysis using Podcast Data (Master’s Thesis, Co-Supervision) |
| 2022 | Andrey Kurzyukov | Modeling Mixed-Type Time Series Data for Machinery Health Prognostics (Master’s Thesis, Co-Supervision) |
| 2021 | Klaus Fischer | Unsupervised Adversarial Representation Learning (Master’s Project) |
| 2021 | Kenneth Mayer | Improving Human Gait Analysis by Super-Resolution and Generative Adversarial Networks (Master’s Project, Co-Supervision) |
| 2021 | Jonas Utz |
Unsupervised Modeling of Visual Attention (Master’s Project, Co-Supervision) |
| 2021 | Meike Biendl | Exploring Connections between Visual Attention and Adversarial Robustness (Master’s Thesis) |
| 2021 | Pavlo Beylin | Simultaneous Classification and Identification of Adversarial Attacks Through Multi-Task Learning (Master’s Thesis) |
| 2021 | Johannes Roider |
Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance (Master’s Thesis, Co-Supervision) |
| 2021 | Tassilo Elsberger | Analysis of Artificial Intelligence Trends in German Economics and Politics – A Data-Driven Approach (Master’s Thesis, Co-Supervision) |
| 2021 | Miaomiao Li | Adaptive Lipschitz Regularization of Neural Networks through Matrix Decomposition (Master’s Thesis) |
| 2021 | Long Do | Artificial Intelligence trend analysis using speech-to-text data from healthcare podcasts (Master’s Thesis, Co-Supervision) |
| 2020 | Pavlo Beylin | Development of a Modular Adversarial Attack Framework and Evaluation on the Sampled Nonlocal Gradient Descent Method (Master’s Project) |
| 2020 | Osman Demir | Anomaly Semantic Segmentation with Learnable Wavelet Filters (Master’s Thesis) |
| 2020 | Darko Boshkovski | Using speech recognition techniques for the application of online handwriting recognition (Bachelor’s Thesis, Co-Supervision) |