| A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation M Hanke, N Adelhöfer, D Kottke, V Iacovella, A Sengupta, FR Kaule, ... Scientific data 3 (1), 1-15, 2016 | 120 | 2016 |
| Optimised probabilistic active learning (OPAL) for fast, non-myopic, cost-sensitive active classification G Krempl, D Kottke, V Lemaire Machine Learning 100, 449-476, 2015 | 56 | 2015 |
| Transfer learning for time series anomaly detection V Vercruyssen, W Meert, J Davis Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning …, 2017 | 45 | 2017 |
| Challenges of Reliable, Realistic and Comparable Active Learning Evaluation D Kottke, A Calma, D Huseljic, G Krempl, B Sick Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning …, 2017 | 38 | 2017 |
| Probabilistic active learning in datastreams D Kottke, G Krempl, M Spiliopoulou Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015 | 34 | 2015 |
| Multi-Class Probabilistic Active Learning D Kottke, G Krempl, D Lang, J Teschner, M Spiliopoulou Frontiers in Artificial Intelligence and Applications 285, 586-594 (ECAI), 2016 | 31 | 2016 |
| Separation of aleatoric and epistemic uncertainty in deterministic deep neural networks D Huseljic, B Sick, M Herde, D Kottke 2020 25th International Conference on Pattern Recognition (ICPR), 9172-9179, 2021 | 30 | 2021 |
| Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms A Calma, D Kottke, B Sick, S Tomforde 2nd IEEE International Workshops on Foundations and Applications of Self …, 2017 | 29 | 2017 |
| A comparative study on hyperparameter optimization for recommender systems P Matuszyk, RT Castillo, D Kottke, M Spiliopoulou Workshop on Recommender Systems and Big Data Analytics (RS-BDA'16).-2016.-Р …, 2016 | 29 | 2016 |
| Toward optimal probabilistic active learning using a Bayesian approach D Kottke, M Herde, C Sandrock, D Huseljic, G Krempl, B Sick Machine learning 110 (6), 1199-1231, 2021 | 28 | 2021 |
| Stream-based active learning for sliding windows under the influence of verification latency T Pham, D Kottke, G Krempl, B Sick Machine Learning, 1-26, 2022 | 25 | 2022 |
| Probabilistic active learning: Towards combining versatility, optimality and efficiency G Krempl, D Kottke, M Spiliopoulou Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014 | 20 | 2014 |
| Limitations of assessing active learning performance at runtime D Kottke, J Schellinger, D Huseljic, B Sick arXiv preprint arXiv:1901.10338, 2019 | 17 | 2019 |
| Probabilistic Active Learning for Active Class Selection D Kottke, G Krempl, M Stecklina, CS von Rekowski, T Sabsch, TP Minh, ... Future of Interactive Learning Machines Workshop @NIPS 2016, 2016 | 16* | 2016 |
| scikit-activeml: A library and toolbox for active learning algorithms D Kottke, M Herde, A Benz, P Mergard, A Roghman, C Sandrock, B Sick | 11 | 2021 |
| A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific Data, 3, 160092 M Hanke, N Adelhöfer, D Kottke, V Iacovella, A Sengupta, FR Kaule, ... | 10 | 2016 |
| Active sorting–an efficient training of a sorting robot with active learning techniques M Herde, D Kottke, A Calma, M Bieshaar, S Deist, B Sick 2018 international joint conference on neural networks (IJCNN), 1-8, 2018 | 9 | 2018 |
| Multi-annotator probabilistic active learning M Herde, D Kottke, D Huseljic, B Sick 2020 25th International Conference on Pattern Recognition (ICPR), 10281-10288, 2021 | 8 | 2021 |
| Active learning with realistic data-a case study A Calma, M Stolz, D Kottke, S Tomforde, B Sick 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 7 | 2018 |
| Probabilistic active learning: A short proposition G Krempl, D Kottke, M Spiliopoulou ECAI 2014, 1049-1050, 2014 | 7 | 2014 |
Bernhard SickProfessor of Intelligent Embedded Systems, University of Kassel在 uni-kassel.de 的電子郵件地址已通過驗證