| 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 | 65 | 2021 |
| Towards non-parametric drift detection via dynamic adapting window independence drift detection (dawidd) F Hinder, A Artelt, B Hammer International Conference on Machine Learning, 4249-4259, 2020 | 46 | 2020 |
| Deepview: Visualizing classification boundaries of deep neural networks as scatter plots using discriminative dimensionality reduction A Schulz, F Hinder, B Hammer arXiv preprint arXiv:1909.09154, 2019 | 39 | 2019 |
| Model-based explanations of concept drift F Hinder, V Vaquet, J Brinkrolf, B Hammer Neurocomputing 555, 126640, 2023 | 26 | 2023 |
| Suitability of different metric choices for concept drift detection F Hinder, V Vaquet, B Hammer International Symposium on Intelligent Data Analysis, 157-170, 2022 | 23 | 2022 |
| Evaluating metrics for bias in word embeddings S Schröder, A Schulz, P Kenneweg, R Feldhans, F Hinder, B Hammer arXiv preprint arXiv:2111.07864, 2021 | 15 | 2021 |
| Contrasting Explanation of Concept Drift F Hinder, A Artelt, V Vaquet, B Hammer 30th European Symposium on Artificial Neural Networks, Computational …, 2022 | 14 | 2022 |
| One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift F Hinder, V Vaquet, B Hammer Frontiers in Artificial Intelligence 7, 1330257, 2024 | 12 | 2024 |
| 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 | 12* | 2023 |
| Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information L Pfannschmidt, J Jakob, F Hinder, M Biehl, P Tino, B Hammer Neurocomputing 416, 266-279, 2020 | 11 | 2020 |
| On the Hardness and Necessity of Supervised Concept Drift Detection. F Hinder, V Vaquet, J Brinkrolf, B Hammer ICPRAM, 164-175, 2023 | 10 | 2023 |
| 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 | 10 | 2021 |
| Concept Drift Segmentation via Kolmogorov-Trees. F Hinder, B Hammer, M Verleysen ESANN, 2021 | 10 | 2021 |
| 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 | 9 | 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 | 9 | 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 | 9 | 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 | 8 | 2021 |
| Investigating the suitability of concept drift detection for detecting leakages in water distribution networks V Vaquet, F Hinder, B Hammer arXiv preprint arXiv:2401.01733, 2024 | 6 | 2024 |
| 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 | 6 | 2023 |
| Feature selection for concept drift detection F Hinder, B Hammer Verleysen, M., editor, European Symposium on Artificial Neural Networks …, 2023 | 6 | 2023 |
Barbara HammerProfessor, Bielefeld UniversityOverená e-mailová adresa na: techfak.uni-bielefeld.de
