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Philipp Liznerski
Philipp Liznerski
PhD Student, RPTU Kaiserslautern-Landau
在 cs.uni-kl.de 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
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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
2272021
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
312022
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
72020
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
42023
Reimagining Anomalies: What If Anomalies Were Normal?
P Liznerski, S Varshneya, E Calikus, S Fellenz, M Kloft
arXiv preprint arXiv:2402.14469, 2024
22024
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
22021
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
12024
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
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