Leo Schwinn
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Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
L Schwinn, R Raab, A Nguyen, D Zanca, B Eskofier
Applied Intelligence, 2023
CLIP: Cheap Lipschitz training of neural networks
L Bungert, R Raab, T Roith, L Schwinn, D Tenbrinck
International Conference on Scale Space and Variational Methods in Computer …, 2021
Time matters: Time-aware lstms for predictive business process monitoring
A Nguyen, S Chatterjee, S Weinzierl, L Schwinn, M Matzner, B Eskofier
Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy …, 2021
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
L Schwinn, A Nguyen, R Raab, L Bungert, D Tenbrinck, D Zanca, ...
Uncertainty in Artificial Intelligence, 854-864, 2021
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
L Schwinn, L Bungert, A Nguyen, R Raab, F Pulsmeyer, D Precup, ...
Proceedings of the 39th International Conference on Machine Learning, 19434 …, 2022
System design for a data-driven and explainable customer sentiment monitor using IoT and enterprise data
A Nguyen, S Foerstel, T Kittler, A Kurzyukov, L Schwinn, D Zanca, T Hipp, ...
IEEE Access 9, 117140-117152, 2021
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
L Schwinn, D Dobre, S Günnemann, G Gidel
NeurIPS 2023, ICBINB Workshop, 2023
Towards Rapid and Robust Adversarial Training with One-Step Attacks
L Schwinn, R Raab, B Eskofier
ArXiv, 2020
Behind the Machine’s Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
L Schwinn, D Precup, B Eskofier, D Zanca
Transactions on Machine Learning Research (TMLR), 2022
Dynamically sampled nonlocal gradients for stronger adversarial attacks
L Schwinn, A Nguyen, R Raab, D Zanca, BM Eskofier, D Tenbrinck, ...
2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021
Raising the bar for certified adversarial robustness with diffusion models
T Altstidl, D Dobre, B Eskofier, G Gidel, L Schwinn
arXiv preprint arXiv:2305.10388, 2023
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space
L Schwinn, D Dobre, S Xhonneux, G Gidel, S Gunnemann
arXiv preprint arXiv:2402.09063, 2024
Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study
T Haertl, D Owsienko, L Schwinn, C Hirsch, BM Eskofier, R Lang, S Wirtz, ...
Frontiers in Ecology and Evolution 11, 1107463, 2023
Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach
P Dumbach, L Schwinn, T Löhr, PL Do, BM Eskofier
Evolutionary Intelligence, 1-22, 2023
Just a matter of scale? reevaluating scale equivariance in convolutional neural networks
T Altstidl, A Nguyen, L Schwinn, F Köferl, C Mutschler, B Eskofier, ...
2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023
Conformance checking for a medical training process using petri net simulation and sequence alignment
A Nguyen, W Zhang, L Schwinn, B Eskofier
arXiv preprint arXiv:2010.11719, 2020
Efficient adversarial training in llms with continuous attacks
S Xhonneux, A Sordoni, S Günnemann, G Gidel, L Schwinn
arXiv preprint arXiv:2405.15589, 2024
Artificial intelligence trend analysis in German business and politics: a web mining approach
P Dumbach, L Schwinn, T Löhr, T Elsberger, BM Eskofier
International Journal of Data Science and Analytics, 1-22, 2023
Can you smell my stress? Influence of stress chemosignals on empathy and emotion recognition in depressed individuals and healthy controls
A Wunder, J Ludwig, T Haertl, S Arnhardt, L Schwinn, DC Chellapandian, ...
Physiology & behavior 270, 114309, 2023
FastAMI--a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
K Klede, L Schwinn, D Zanca, B Eskofier
AAAI Conference on Artificial Intelligence 37 (7), 8317-8324, 2023
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