| Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond A Hedström, L Weber, D Krakowczyk, D Bareeva, F Motzkus, W Samek, ... Journal of Machine Learning Research 24 (34), 1-11, 2023 | 240 | 2023 |
| Finding and removing Clever Hans: Using explanation methods to debug and improve deep models CJ Anders, L Weber, D Neumann, W Samek, KR Müller, S Lapuschkin Information Fusion 77, 261-295, 2022 | 138 | 2022 |
| Beyond explaining: Opportunities and challenges of XAI-based model improvement L Weber, S Lapuschkin, A Binder, W Samek Information Fusion 92, 154-176, 2023 | 120 | 2023 |
| Understanding integrated gradients with smoothtaylor for deep neural network attribution GSW Goh, S Lapuschkin, L Weber, W Samek, A Binder 2020 25th International Conference on Pattern Recognition (ICPR), 4949-4956, 2021 | 40 | 2021 |
| Shortcomings of top-down randomization-based sanity checks for evaluations of deep neural network explanations A Binder, L Weber, S Lapuschkin, G Montavon, KR Müller, W Samek Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 21 | 2023 |
| Explain to not forget: Defending against catastrophic forgetting with xai S Ede, S Baghdadlian, L Weber, A Nguyen, D Zanca, W Samek, ... International Cross-Domain Conference for Machine Learning and Knowledge …, 2022 | 10 | 2022 |
| Measurably stronger explanation reliability via model canonization F Motzkus, L Weber, S Lapuschkin 2022 IEEE International Conference on Image Processing (ICIP), 516-520, 2022 | 9 | 2022 |
| PatClArC: Using pattern concept activation vectors for noise-robust model debugging F Pahde, L Weber, CJ Anders, W Samek, S Lapuschkin arXiv preprint arXiv:2202.03482, 2022 | 9 | 2022 |
| Sanity checks revisited: An exploration to repair the model parameter randomisation test A Hedström, L Weber, S Lapuschkin, M Höhne arXiv preprint arXiv:2401.06465, 2024 | 8 | 2024 |
| Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence F Pahde, M Dreyer, L Weber, M Weckbecker, CJ Anders, T Wiegand, ... arXiv preprint arXiv:2202.03482, 2022 | 5 | 2022 |
| A fresh look at sanity checks for saliency maps A Hedström, L Weber, S Lapuschkin, M Höhne World Conference on Explainable Artificial Intelligence, 403-420, 2024 | 4 | 2024 |
| Towards a more refined training process for neural networks: Applying layer-wise relevance propagation to understand and improve classification performance on imbalanced datasets L Weber Technische Universität Berlin, 2020 | 4 | 2020 |
| Layer-wise Feedback Propagation L Weber, J Berend, A Binder, T Wiegand, W Samek, S Lapuschkin arXiv preprint arXiv:2308.12053, 2023 | 1 | 2023 |
| Supplement to: Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations A Binder, L Weber, S Lapuschkin | | |
