| Evaluating robustness of counterfactual explanations A Artelt, V Vaquet, R Velioglu, F Hinder, J Brinkrolf, M Schilling, ... 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-09, 2021 | 42 | 2021 |
| Suitability of different metric choices for concept drift detection F Hinder, V Vaquet, B Hammer International Symposium on Intelligent Data Analysis, 157-170, 2022 | 18 | 2022 |
| Contrasting Explanation of Concept Drift. F Hinder, A Artelt, V Vaquet, B Hammer ESANN, 2022 | 9 | 2022 |
| Balanced sam-knn: Online learning with heterogeneous drift and imbalanced data V Vaquet, B Hammer Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020 | 8 | 2020 |
| Model-based explanations of concept drift F Hinder, V Vaquet, J Brinkrolf, B Hammer Neurocomputing 555, 126640, 2023 | 7 | 2023 |
| Investigating intensity and transversal drift in hyperspectral imaging data V Vaquet, P Menz, U Seiffert, B Hammer Neurocomputing 505, 68-79, 2022 | 7 | 2022 |
| Fast non-parametric conditional density estimation using moment trees F Hinder, V Vaquet, J Brinkrolf, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2021 | 7 | 2021 |
| Contrastive explanations for explaining model adaptations A Artelt, F Hinder, V Vaquet, R Feldhans, B Hammer International Work-Conference on Artificial Neural Networks, 101-112, 2021 | 7 | 2021 |
| On the Hardness and Necessity of Supervised Concept Drift Detection. F Hinder, V Vaquet, J Brinkrolf, B Hammer ICPRAM, 164-175, 2023 | 6 | 2023 |
| Taking care of our drinking water: dealing with sensor faults in water distribution networks V Vaquet, A Artelt, J Brinkrolf, B Hammer International Conference on Artificial Neural Networks, 682-693, 2022 | 6 | 2022 |
| A shape-based method for concept drift detection and signal denoising F Hinder, J Brinkrolf, V Vaquet, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08, 2021 | 5 | 2021 |
| Contrasting explanations for understanding and regularizing model adaptations A Artelt, F Hinder, V Vaquet, R Feldhans, B Hammer Neural Processing Letters 55 (5), 5273-5297, 2023 | 4 | 2023 |
| On the change of decision boundary and loss in learning with concept drift F Hinder, V Vaquet, J Brinkrolf, B Hammer International Symposium on Intelligent Data Analysis, 182-194, 2023 | 4 | 2023 |
| Localization of concept drift: Identifying the drifting datapoints F Hinder, V Vaquet, J Brinkrolf, A Artelt, B Hammer 2022 International Joint Conference on Neural Networks (IJCNN), 1-9, 2022 | 4 | 2022 |
| One or Two Things We know about Concept Drift--A Survey on Monitoring Evolving Environments F Hinder, V Vaquet, B Hammer arXiv preprint arXiv:2310.15826, 2023 | 3 | 2023 |
| Online learning on non-stationary data streams for image recognition using deep embeddings V Vaquet, F Hinder, J Vaquet, J Brinkrolf, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2021 | 2 | 2021 |
| Combining self-labeling and demand based active learning for non-stationary data streams V Vaquet, F Hinder, J Brinkrolf, B Hammer arXiv preprint arXiv:2302.04141, 2023 | 1 | 2023 |
| On the change of decision boundaries and loss in learning with concept drift F Hinder, V Vaquet, J Brinkrolf, B Hammer arXiv preprint arXiv:2212.01223, 2022 | 1 | 2022 |
| Federated learning vector quantization for dealing with drift between nodes V Vaquet, F Hinder, J Brinkrolf, P Menz, U Seiffert, B Hammer Bruges, 2022 | 1 | 2022 |
| A remark on concept drift for dependent data F Hinder, V Vaquet, B Hammer International Symposium on Intelligent Data Analysis, 77-89, 2024 | | 2024 |
Barbara HammerProfessor, Bielefeld University在 techfak.uni-bielefeld.de 的电子邮件经过验证
