| Explaining deep neural networks and beyond: A review of methods and applications W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller Proceedings of the IEEE 109 (3), 247-278, 2021 | 152 | 2021 |
| Explanations can be manipulated and geometry is to blame AK Dombrowski, M Alber, C Anders, M Ackermann, KR Müller, P Kessel Advances in Neural Information Processing Systems 32, 2019 | 141 | 2019 |
| Toward interpretable machine learning: Transparent deep neural networks and beyond W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller | 81 | 2020 |
| Estimation of thermodynamic observables in lattice field theories with deep generative models KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... Physical review letters 126 (3), 032001, 2021 | 35 | 2021 |
| Fairwashing explanations with off-manifold detergent CJ Anders, P Pasliev, AK Dombrowski, KR Müller, P Kessel Thirty-seventh International Conference on Machine Learning, 2020 | 34 | 2020 |
| Understanding patch-based learning of video data by explaining predictions CJ Anders, G Montavon, W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 297-309, 2019 | 23* | 2019 |
| 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 | 15 | 2022 |
| Towards robust explanations for deep neural networks AK Dombrowski, CJ Anders, KR Müller, P Kessel Pattern Recognition 121, 108194, 2022 | 11 | 2022 |
| Software for Dataset-wide XAI: From local explanations to global insights with Zennit, CoRelAy, and ViRelAy CJ Anders, D Neumann, W Samek, KR Müller, S Lapuschkin arXiv preprint arXiv:2106.13200, 2021 | 11 | 2021 |
| Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans' ed CJ Anders, T Marinč, D Neumann, W Samek, KR Müller, S Lapuschkin | 11 | 2019 |
| Toward interpretable machine learning: Transparent deep neural networks and beyond. arXiv 2020 W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller arXiv preprint arXiv:2003.07631, 2003 | 8 | 2003 |
| Toward interpretable machine learning: Transparent deep neural networks and beyond. arXiv W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller | 5 | 2020 |
| 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 | 1 | 2022 |
| Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse KA Nicoli, C Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... arXiv preprint arXiv:2111.11303, 2021 | | 2021 |
| Explanations can be manipulated and geometry is to blame Open Website AK Dombrowski, M Alber, CJ Anders, M Ackermann, KR Muller, P Kessel | | |
| Towards robust explanations for deep neural networks Open Website AK Dombrowski, CJ Anders, KR Muller, P Kessel | | |
| REGULAR PAPERS ISSUE C She, C Sun, Z Gu, Y Li, C Yang, HV Poor, B Vucetic, W Samek, ... | | |
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
