| Deep one-class classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International conference on machine learning, 4393-4402, 2018 | 2773 | 2018 |
| Deep semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... arXiv preprint arXiv:1906.02694, 2019 | 788 | 2019 |
| Toward supervised anomaly detection N Görnitz, MM Kloft, K Rieck, U Brefeld Journal of Artificial Intelligence Research (JAIR), 2013 | 552 | 2013 |
| Active learning for network intrusion detection N Görnitz, M Kloft, K Rieck, U Brefeld Proceedings of the 2nd ACM workshop on Security and artificial intelligence …, 2009 | 104 | 2009 |
| Hidden markov anomaly detection N Görnitz, M Braun, M Kloft International Conference on Machine Learning, 2015 | 86 | 2015 |
| Support Vector Data Descriptions and -Means Clustering: One Class? N Görnitz, LA Lima, KR Müller, M Kloft, S Nakajima IEEE transactions on neural networks and learning systems 29 (9), 3994-4006, 2017 | 52 | 2017 |
| Feature importance measure for non-linear learning algorithms MMC Vidovic, N Görnitz, KR Müller, M Kloft arXiv preprint arXiv:1611.07567, 2016 | 47 | 2016 |
| DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies B Mieth, A Rozier, JA Rodriguez, MMC Höhne, N Görnitz, KR Müller NAR genomics and bioinformatics 3 (3), lqab065, 2021 | 46 | 2021 |
| Active and semi-supervised data domain description N Görnitz, M Kloft, U Brefeld Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009 | 44 | 2009 |
| Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data B Mieth, JRF Hockley, N Görnitz, MMC Vidovic, KR Müller, A Gutteridge, ... Scientific reports 9 (1), 20353, 2019 | 40 | 2019 |
| Hierarchical multitask structured output learning for large-scale sequence segmentation N Görnitz, C Widmer, G Zeller, A Kahles, G Rätsch, S Sonnenburg Advances in Neural Information Processing Systems 24, 2011 | 40 | 2011 |
| Learning and evaluation in presence of non-iid label noise N Görnitz, A Porbadnigk, A Binder, C Sannelli, M Braun, KR Müller, ... Artificial Intelligence and Statistics, 293-302, 2014 | 35 | 2014 |
| Efficient Algorithms for Exact Inference in Sequence Labeling SVMs A Bauer, N Goernitz, F Biegler, KR Mueller, M Kloft IEEE Transactions on Neural Networks and Learning (TNNLS), 2013 | 26 | 2013 |
| Porosity estimation by semi-supervised learning with sparsely available labeled samples LA Lima, N Görnitz, LE Varella, M Vellasco, KR Müller, S Nakajima Computers & Geosciences 106, 33-48, 2017 | 24 | 2017 |
| Efficient training of graph-regularized multitask SVMs C Widmer, M Kloft, N Görnitz, G Rätsch Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012 | 23 | 2012 |
| 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 | 22 | 2019 |
| Extracting latent brain states—Towards true labels in cognitive neuroscience experiments AK Porbadnigk, N Görnitz, C Sannelli, A Binder, M Braun, M Kloft, ... NeuroImage 120, 225-253, 2015 | 22 | 2015 |
| Ensembles of Lasso screening rules S Lee, N Görnitz, EP Xing, D Heckerman, C Lippert IEEE transactions on pattern analysis and machine intelligence 40 (12), 2841 …, 2017 | 20 | 2017 |
| An off-the-shelf approach to authorship attribution JA Nasir, N Görnitz, U Brefeld Proceedings of COLING 2014, the 25th International Conference on …, 2014 | 19 | 2014 |
| Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis VT Sreedharan, SJ Schultheiss, G Jean, A Kahles, R Bohnert, P Drewe, ... Bioinformatics 30 (9), 1300-1301, 2014 | 19 | 2014 |
Marius KloftProfessor, RPTU Kaiserslautern-LandauVerified email at cs.uni-kl.de
