| 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 | 604 | 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 | 372 | 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 | 355 | 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 | 270 | 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 | 45 | 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 84, 129-143, 2022 | 42 | 2022 |
| Backprop evolution M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le arXiv preprint arXiv:1808.02822, 2018 | 17 | 2018 |
| 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 | 17 | 2017 |
| Distributed optimization of multi-class SVMs M Alber, J Zimmert, U Dogan, M Kloft PloS one 12 (6), e0178161, 2017 | 14 | 2017 |
| Software and application patterns for explanation methods M Alber Explainable AI: interpreting, explaining and visualizing deep learning, 399-433, 2019 | 13 | 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: State-of …, 2020 | 12 | 2020 |
| 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 | 12 | 2018 |
| How to iNNvestigate neural networks' predictions! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... | 4 | 2018 |
| Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law D Kim, M Alber, MW Kwok, J MitroviĆ, C Ramirez-Atencia, JÚARÍ PÉrez, ... GRUR International 71 (4), 295-321, 2022 | 3 | 2022 |
| Ten Assumptions About Artificial Intelligence That Can Mislead Patent Law Analysis D Kim, M Alber, MW Kwok, J Mitrovic, C Ramirez-Atencia, ... Max Planck Institute for Innovation & Competition Research Paper, 2021 | 3 | 2021 |
| Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights L Schweizer, P Seegerer, H Kim, R Saitenmacher, A Muench, L Barnick, ... Neuropathology and Applied Neurobiology 49 (1), e12866, 2023 | 2 | 2023 |
| Deep learning assisted diagnosis of onychomycosis on whole-slide images P Jansen, A Creosteanu, V Matyas, A Dilling, A Pina, A Saggini, ... Journal of Fungi 8 (9), 912, 2022 | 2 | 2022 |
| 68MO Generalization of a deep learning model for HER2 status predictions on H&E-stained whole slide images derived from 3 neoadjuvant clinical studies M Hägele, KR Müller, C Denkert, A Schneeweiss, BV Sinn, M Untch, ... Annals of Oncology 33, S572-S573, 2022 | 1 | 2022 |
| Balancing the composition of word embeddings across heterogenous data sets S Brandl, D Lassner, M Alber arXiv preprint arXiv:2001.04693, 2020 | 1 | 2020 |
| Efficient learning machines: From kernel methods to deep learning M Alber Dissertation, Berlin, Technische Universität Berlin, 2019, 2019 | 1 | 2019 |
