| 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 | 119* | 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 | 44 | 2021 |
| 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 | 18 | 2021 |
| A typology to explore and guide explanatory interactive machine learning F Friedrich, W Stammer, P Schramowski, K Kersting arXiv preprint arXiv:2203.03668, 2022 | 5 | 2022 |
| 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 | 5 | 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 | 3* | 2022 |
| Leveraging explanations in interactive machine learning: An overview S Teso, Ö Alkan, W Stammer, E Daly arXiv preprint arXiv:2207.14526, 2022 | 3 | 2022 |
| 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 | 2 | 2020 |
| Boosting Object Representation Learning via Motion and Object Continuity Q Delfosse, W Stammer, T Rothenbacher, D Vittal, K Kersting arXiv preprint arXiv:2211.09771, 2022 | | 2022 |
| Revision Transformers: Getting RiT of No-Nos F Friedrich, W Stammer, P Schramowski, K Kersting arXiv preprint arXiv:2210.10332, 2022 | | 2022 |
| NeurASP: Neural-Probabilistic Answer Set Programming A Skryagin, W Stammer, D Ochs, D Singh Dhami, K Kristian, NPA Set | | 2022 |
| Workshop on Interactive Machine Learning E Daly, O Alkan, S Teso, W Stammer AAAI Conference on Artificial Intelligence, 2022 | | 2022 |
| Explanations in Interactive Machine Learning S Teso, O Alkan, E Daly, W Stammer AAAI Conference on Artificial Intelligence, 2022 | | 2022 |
| Elementary Concept Reasoning (ECR) W Stammer, M Memmel, P Schramowski, K Kersting Technical University of Darmstadt, 2021 | | 2021 |
| CLEVR-Hans7 W Stammer, P Schramowski | | 2020 |
| Insights from Explainable Interactive Machine Learning in the Age of COVID-19 O Hinz, N Pfeuffer, W Stammer, P Schramowski, BM Abdel-Karim, ... | | |
