| 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 | 403 | 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 | 313 | 2017 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019 | 263 | 2019 |
| 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 | 167 | 2019 |
| PatternNet and PatternLRP--Improving the interpretability of neural networks PJ Kindermans, KT Schütt, M Alber, KR Müller, S Dähne arXiv preprint arXiv:1705.05598 3, 2017 | 44 | 2017 |
| Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling A Stenzinger, M Alber, M Allgäuer, P Jurmeister, M Bockmayr, J Budczies, ... Seminars in cancer biology, 2021 | 20 | 2021 |
| 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 30, 2017 | 13 | 2017 |
| Distributed optimization of multi-class SVMs M Alber, J Zimmert, U Dogan, M Kloft PloS one 12 (6), e0178161, 2017 | 12 | 2017 |
| Explainable AI: Interpreting, Explaining and Visualizing Deep Learning PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne Springer, 2019 | 11 | 2019 |
| Backprop evolution M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le arXiv preprint arXiv:1808.02822, 2018 | 11 | 2018 |
| Learning how to explain neural networks: Patternnet and patternattribution (2017) PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne arXiv preprint arXiv:1705.05598, 2018 | 11 | 2018 |
| Software and application patterns for explanation methods M Alber Explainable AI: interpreting, explaining and visualizing deep learning, 399-433, 2019 | 9 | 2019 |
| Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images P Seegerer, A Binder, R Saitenmacher, M Bockmayr, M Alber, ... Artificial Intelligence and Machine Learning for Digital Pathology, 16-37, 2020 | 8 | 2020 |
| How to iNNvestigate neural networks' predictions! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... | 4 | 2018 |
| Balancing the composition of word embeddings across heterogenous data sets S Brandl, D Lassner, M Alber arXiv preprint arXiv:2001.04693, 2020 | 2 | 2020 |
| Efficient learning machines: From kernel methods to deep learning M Alber | 1 | 2019 |
| Masterarbeit: Big Data and Machine Learning: A Case Study with Bump Boost M Alber | 1 | 2015 |
| Efficient learning machines M Alber | | 2019 |
| Explanations can be manipulated and geometry is to blame Open Website AK Dombrowski, M Alber, CJ Anders, M Ackermann, KR Muller, P Kessel | | |
