| Making deep neural networks right for the right scientific reasons by interacting with their explanations P Schramowski, W Stammer, S Teso, A Brugger, F Herbert, X Shao, ... Nature Machine Intelligence 2 (8), 476-486, 2020 | 214 | 2020 |
| Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations W Stammer, P Schramowski, K Kersting Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 87 | 2021 |
| Leveraging explanations in interactive machine learning: An overview S Teso, Ö Alkan, W Stammer, E Daly Frontiers in Artificial Intelligence 6, 1066049, 2023 | 37 | 2023 |
| Right for better reasons: Training differentiable models by constraining their influence functions X Shao, A Skryagin, W Stammer, P Schramowski, K Kersting Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9533-9540, 2021 | 34 | 2021 |
| A typology for exploring the mitigation of shortcut behaviour F Friedrich, W Stammer, P Schramowski, K Kersting Nature Machine Intelligence 5 (3), 319-330, 2023 | 22* | 2023 |
| Interactive disentanglement: Learning concepts by interacting with their prototype representations W Stammer, M Memmel, P Schramowski, K Kersting Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 21 | 2022 |
| Neural-probabilistic answer set programming A Skryagin, W Stammer, D Ochs, DS Dhami, K Kersting Proceedings of the International Conference on Principles of Knowledge …, 2022 | 19* | 2022 |
| Explanatory Interactive Machine Learning: Establishing an Action Design Research Process for Machine Learning Projects N Pfeuffer, L Baum, W Stammer, BM Abdel-Karim, P Schramowski, ... Business & Information Systems Engineering 65 (6), 677-701, 2023 | 11 | 2023 |
| Learning to intervene on concept bottlenecks D Steinmann, W Stammer, F Friedrich, K Kersting arXiv preprint arXiv:2308.13453, 2023 | 6 | 2023 |
| Boosting object representation learning via motion and object continuity Q Delfosse, W Stammer, T Rothenbächer, D Vittal, K Kersting Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 3 | 2023 |
| Machine learning assisted pattern matching: Insight into oxide electronic device performance by phase determination in 4D-STEM datasets A Zintler, R Eilhardt, S Wang, M Krajnak, P Schramowski, W Stammer, ... Microscopy and Microanalysis 26 (S2), 1908-1909, 2020 | 3 | 2020 |
| Interpretable concept bottlenecks to align reinforcement learning agents Q Delfosse, S Sztwiertnia, W Stammer, M Rothermel, K Kersting arXiv preprint arXiv:2401.05821, 2024 | 2 | 2024 |
| Revision Transformers: Instructing Language Models to Change their Values F Friedrich, W Stammer, P Schramowski, K Kersting arXiv preprint arXiv:2210.10332, 2022 | 2* | 2022 |
| Learning by Self-Explaining W Stammer, F Friedrich, D Steinmann, H Shindo, K Kersting arXiv preprint arXiv:2309.08395, 2023 | 1 | 2023 |
| Pix2Code: Learning to Compose Neural Visual Concepts as Programs A Wüst, W Stammer, Q Delfosse, DS Dhami, K Kersting arXiv preprint arXiv:2402.08280, 2024 | | 2024 |
| Where is the Truth? The Risk of Getting Confounded in a Continual World FP Busch, R Kamath, R Mitchell, W Stammer, K Kersting, M Mundt arXiv preprint arXiv:2402.06434, 2024 | | 2024 |
| V-LoL: A Diagnostic Dataset for Visual Logical Learning L Helff, W Stammer, H Shindo, DS Dhami, K Kersting arXiv preprint arXiv:2306.07743, 2023 | | 2023 |
| NeurASP: Neural-Probabilistic Answer Set Programming A Skryagin, W Stammer, D Ochs, D Singh Dhami, K Kristian, NPA Set | | 2022 |
| P30-Characterization of grapevine resistance to downy mildew using hyperspectral imaging in SWIR spectral range. R Höfle, W Stammer, K Kersting, R Töpfer, H Katja Julius-Kühn-Archiv, 2022 | | 2022 |
| Workshop on Interactive Machine Learning E Daly, O Alkan, S Teso, W Stammer AAAI Conference on Artificial Intelligence, 2022 | | 2022 |
