| Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks L Schwinn, R Raab, A Nguyen, D Zanca, B Eskofier Applied Intelligence, 2023 | 67 | 2023 |
| 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 | 43 | 2021 |
| 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 | 40 | 2021 |
| 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 NeurIPS, 2024 | 22 | 2024 |
| Adversarial Attacks and Defenses in Large Language Models: Old and New Threats L Schwinn, D Dobre, S Günnemann, G Gidel NeurIPS 2023, ICBINB Workshop, 103-117, 2023 | 21 | 2023 |
| 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, ... ICML, 19434-19449, 2022 | 15 | 2022 |
| Efficient adversarial training in llms with continuous attacks S Xhonneux, A Sordoni, S Günnemann, G Gidel, L Schwinn NeurIPS (Spotlight), 2024 | 14 | 2024 |
| 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 (Oral), 854-864, 2021 | 14 | 2021 |
| 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 | 12 | 2021 |
| 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 | 10 | 2021 |
| 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 | 9 | 2022 |
| Raising the bar for certified adversarial robustness with diffusion models T Altstidl, D Dobre, B Eskofier, G Gidel, L Schwinn NeurIPS, 2024 | 8 | 2024 |
| Towards Rapid and Robust Adversarial Training with One-Step Attacks L Schwinn, R Raab, B Eskofier arXiv preprint arXiv:2002.10097, 2020 | 8* | 2020 |
| Assessing robustness via score-based adversarial image generation M Kollovieh, L Gosch, Y Scholten, M Lienen, L Schwinn, S Günnemann Transactions on Machine Learning Research (TMLR), 2024 | 4 | 2024 |
| 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 17 (4), 2145-2166, 2024 | 4 | 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 | 4 | 2023 |
| 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 | 3 | 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 | 3 | 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 | 3 | 2023 |
| xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning J Link, L Schwinn, F Pulsmeyer, T Kautz, BM Eskofier Sensors 22 (21), 8474, 2022 | 3 | 2022 |
Bjoern M. EskofierMaD Lab, FAU Erlangen-Nürnberg & TDH Group, Helmholtz MunichEmail verificata su fau.de
