Leo Schwinn

Leo Schwinn, M. Sc.

Researcher & PhD Candidate

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)

Room 01.016
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany

since 09/2019	Researcher and PhD Candidate Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
01/2019-03/2019	Scientific Assistant IT Security Infrastructures Lab (CS1), Department of Computer Science, Friedrich-Alexander-Universität (FAU)
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 Master’s Thesis: “Learning Feature Extraction for End-to-End Speaker Recognition with Deep Neural Networks”
10/2014-10/2017	B.Sc. in Medical Engineering, “Medical Imaging Systems” Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition Bachelor’s Thesis: “Unterscheidung von Sprachpathologien durch Sprechererkennungsverfahren”

Bungert L., Raab R., Roith T., Schwinn L., Tenbrinck D.:
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 (ed.): 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
Schwinn L., Nguyen A., Raab R., Zanca D., Eskofier B., Tenbrinck D., Burger M.:
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
International Joint Conference on Neural Networks (IJCNN) (Online, 18. July 2021 - 22. July 2021)
BibTeX: Download
Schwinn L., Nguyen AT., Raab R., Bungert L., Tenbrinck D., Zanca D., Burger M., Eskofier B.:
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Conference on Uncertainty in Artificial Intelligence (UAI) (Online, 27. July 2021 - 30. July 2021)
URL: https://arxiv.org/abs/2102.12196
BibTeX: Download
Schwinn L., Raab R., Nguyen A., Zanca D., Eskofier B.:
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
URL: https://arxiv.org/abs/2105.10304
BibTeX: Download

Nguyen A., Chatterjee S., Weinzierl S., Schwinn L., Matzner M., Eskofier B.:
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
International Conference on Process Mining (Padua, 4. October 2020 - 9. October 2020)
In: Proceedings of the 1st International Workshop on Leveraging Machine Learning in Process Mining 2020
BibTeX: Download

Year	Name	Type	Title
2021	Klaus Fischer	Master Project	Unsupervised Adversarial Representation Learning
2021	Kenneth Mayer	Master Project	Improving Human Gait Analysis by Super-Resolution and Generative Adversarial Networks
2021	Meike Biendl	Master Thesis	Exploring Connections between Visual Attention and Adversarial Robustness
2021	Pavlo Beylin	Master Thesis	Simultaneous Classification and Identification of Adversarial Attacks Through Multi-Task Learning
2021	Johannes Roider	Master Thesis	Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance
2021	Tassilo Elsberger	Master Thesis	Analysis of Artificial Intelligence Trends in German Economics and Politics – A Data-Driven Approach
2021	Miaomiao Li	Master Thesis	Adaptive Lipschitz Regularization of Neural Networks through Matrix Decomposition
2021	Long Do	Master Thesis	Artificial Intelligence trend analysis using speech-to-text data from healthcare podcasts
2020	Pavlo Beylin	Master Project	Development of a Modular Adversarial Attack Framework and Evaluation on the Sampled Nonlocal Gradient Descent Method
2020	Osman Demir	Master Thesis	group:Anomaly Semantic Segmentation with Learnable Wavelet Filters
2020	Darko Boshkovski	Bachelor Thesis	group:Using speech recognition techniques for the application of online handwriting recognition