| The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267-280, 2019 | 91 | 2019 |
| Learning how to explain neural networks: Patternnet and patternattribution PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne arXiv preprint arXiv:1705.05598, 2017 | 86 | 2017 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... Journal of Machine Learning Research 20 (93), 1-8, 2019 | 27 | 2019 |
| Patternnet and patternlrp–improving the interpretability of neural networks PJ Kindermans, KT Schütt, M Alber, KR Müller, S Dähne stat 1050, 16, 2017 | 26 | 2017 |
| Explanations can be manipulated and geometry is to blame AK Dombrowski, M Alber, CJ Anders, M Ackermann, KR Müller, P Kessel arXiv preprint arXiv:1906.07983, 2019 | 5 | 2019 |
| Backprop evolution M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le arXiv preprint arXiv:1808.02822, 2018 | 5 | 2018 |
| Distributed optimization of multi-class SVMs M Alber, J Zimmert, U Dogan, M Kloft PloS one 12 (6), e0178161, 2017 | 4 | 2017 |
| An empirical study on the properties of random bases for kernel methods M Alber, PJ Kindermans, K Schütt, KR Müller, F Sha Advances in Neural Information Processing Systems, 2763-2774, 2017 | 3 | 2017 |
| Software and application patterns for explanation methods M Alber arXiv preprint arXiv:1904.04734, 2019 | 1 | 2019 |
| How to iNNvestigate neural networks' predictions! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... | 1 | 2018 |
| Masterarbeit: Big Data and Machine Learning: A Case Study with Bump Boost M Alber | | 2015 |
| The (Un) reliability of Saliency Methods M Alber, KT Schütt, S Dähne, D Erhan, B Kim Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267, 0 | | |
