| Deep One-Class Classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International Conference on Machine Learning, 4390-4399, 2018 | 3417 | 2018 |
| A unifying review of deep and shallow anomaly detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE 109 (5), 756-795, 2021 | 1403 | 2021 |
| Deep semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... International Conference on Learning Representations, 2019 | 970 | 2019 |
| Explainable deep one-class classification P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller International Conference on Learning Representations, 2020 | 349 | 2020 |
| Image anomaly detection with generative adversarial networks L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft Joint european conference on machine learning and knowledge discovery in …, 2018 | 337 | 2018 |
| Rethinking assumptions in deep anomaly detection L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning, 2021 | 132 | 2021 |
| Human alignment of neural network representations L Muttenthaler, L Linhardt, J Dippel, RA Vandermeulen, S Kornblith SVRHM 2022 Workshop@ NeurIPS, 2022 | 126 | 2022 |
| Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ... The journal of physical chemistry letters 11 (3), 981-985, 2020 | 119 | 2020 |
| Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text L Ruff, Y Zemlyanskiy, R Vandermeulen, T Schnake, M Kloft Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 106 | 2019 |
| 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, 2022 | 65 | 2022 |
| Improving neural network representations using human similarity judgments L Muttenthaler, L Linhardt, J Dippel, RA Vandermeulen, K Hermann, ... Advances in neural information processing systems 36, 50978-51007, 2023 | 61 | 2023 |
| Transfer-based semantic anomaly detection L Deecke, L Ruff, RA Vandermeulen, H Bilen International Conference on Machine Learning, 2546-2558, 2021 | 51 | 2021 |
| VICE: Variational Inference for Concept Embeddings L Muttenthaler, CY Zheng, P McClure, RA Vandermeulen, MN Hebart, ... Advances in NeurIPS, 2022 | 33* | 2022 |
| Consistency of robust kernel density estimators R Vandermeulen, C Scott Conference on Learning Theory, 568-591, 2013 | 26 | 2013 |
| Deep support vector data description for unsupervised and semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Gornitz, A Binder, E Muller, M Kloft Proceedings of the ICML 2019 Workshop on Uncertainty and Robustness in Deep …, 2019 | 24 | 2019 |
| An Operator Theoretic Approach to Nonparametric Mixture Models RA Vandermeulen, CD Scott Annals of Statistics 47 (5), 2704-2733, 2019 | 23 | 2019 |
| Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations A Ritchie, RA Vandermeulen, C Scott Advances in Neural Information Processing Systems 33, 2020 | 15 | 2020 |
| Beyond smoothness: Incorporating low-rank analysis into nonparametric density estimation RA Vandermeulen, A Ledent Advances in Neural Information Processing Systems 34, 12180-12193, 2021 | 14 | 2021 |
| Anomaly detection with generative adversarial networks, 2018 L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft URL https://openreview. net/forum, 2018 | 14 | 2018 |
| Robust kernel density estimation by scaling and projection in hilbert space RA Vandermeulen, CD Scott Advances in Neural Information Processing Systems 27, 2014 | 13 | 2014 |
Marius KloftProfessor, RPTU Kaiserslautern-LandauVerified email at cs.uni-kl.de
