| Explainable deep one-class classification. P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller Proceedings of the International Conference on Learning Representations (ICLR), 2021 | 256 | 2021 |
| Exposing outlier exposure: What can be learned from few, one, and zero outlier images P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft Transactions on Machine Learning Research (TMLR), 2022 | 36 | 2022 |
| Explainable deep one-class classification. arXiv 2020. P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller arXiv preprint arXiv:2007.01760, 2020 | 8 | 2020 |
| Deep anomaly detection on Tennessee Eastman process data F Hartung, BJ Franks, T Michels, D Wagner, P Liznerski, S Reithermann, ... Chemie Ingenieur Technik 95 (7), 1077-1082, 2023 | 7 | 2023 |
| Reimagining Anomalies: What If Anomalies Were Normal? P Liznerski, S Varshneya, E Calikus, S Fellenz, M Kloft arXiv preprint arXiv:2402.14469, 2024 | 4 | 2024 |
| Deep Learning zur Unterstützung der Arbeitsplanung: Ein Konzept zur Ermittlung von Vorgangsfolgen durch künstliche neuronale Netze M Hussong, M Glatt, P Rüdiger-Flore, S Varshneya, P Liznerski, M Kloft, ... Zeitschrift für wirtschaftlichen Fabrikbetrieb 116 (10), 648-651, 2021 | 2 | 2021 |
| Interpretable Tensor Fusion S Varshneya, A Ledent, P Liznerski, A Balinskyy, P Mehta, W Mustafa, ... Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2024 | 1 | 2024 |
| AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics J Dippel, N Prenißl, J Hense, P Liznerski, T Winterhoff, S Schallenberg, ... arXiv preprint arXiv:2406.14866, 2024 | | 2024 |
| Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks W Mustafa, P Liznerski, A Ledent, D Wagner, P Wang, M Kloft International Conference on Artificial Intelligence and Statistics, 4528-4536, 2024 | | 2024 |
| Non-vacuous PAC-Bayes bounds for Models under Adversarial Corruptions W Mustafa, P Liznerski, D Wagner, P Wang, M Kloft PAC-Bayes Meets Interactive Learning Workshop at ICML 2023, 2023 | | 2023 |
| Deep Learning zur Prozessüberwachung in der additiven Fertigung: Ein Konzept zur Vorhersage der Materialporosität von additiv gefertigten Bauteilen durch Deep Learning L Yi, S Ehmsen, M Cassani, M Glatt, S Varshneya, P Liznerski, M Kloft, ... Zeitschrift für wirtschaftlichen Fabrikbetrieb 115 (11), 810-813, 2020 | | 2020 |
