| Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998 | 11580 | 1998 |
| Efficient backprop Y LeCun, L Bottou, G Orr, KR Müller Neural networks: Tricks of the trade 7700, 9-53, 2012 | 8213* | 2012 |
| On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek PloS one 10 (7), e0130140, 2015 | 6404 | 2015 |
| An introduction to kernel-based learning algorithms KR Müller, S Mika, G Rätsch, K Tsuda, B Schölkopf IEEE Transactions on Neural Networks 12 (2), 181 - 201, 2001 | 5146* | 2001 |
| Fisher discriminant analysis with kernels S Mika, G Rätsch, J Weston, B Schölkopf, KR Müller Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE …, 1999 | 4051 | 1999 |
| Kernel principal component analysis B Schölkopf, A Smola, KR Müller Artificial Neural Networks—ICANN'97, 583-588, 1997 | 3827 | 1997 |
| Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018 | 3640 | 2018 |
| Fast and accurate modeling of molecular atomization energies with machine learning M Rupp, A Tkatchenko, KR Müller, OA Von Lilienfeld Physical review letters 108 (5), 058301, 2012 | 2917 | 2012 |
| Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models W Samek, T Wiegand, KR Müller ITU Journal: ICT Discoveries - The Impact of Artifcial Intelligence (AI) on …, 2017 | 2696 | 2017 |
| Schnet–a deep learning architecture for molecules and materials KT Schütt, HE Sauceda, PJ Kindermans, A Tkatchenko, KR Müller The Journal of Chemical Physics 148 (24), 241722, 2018 | 2600 | 2018 |
| Optimizing spatial filters for robust EEG single-trial analysis B Blankertz, R Tomioka, S Lemm, M Kawanabe, KR Müller IEEE Signal processing magazine 25 (1), 41-56, 2008 | 2594 | 2008 |
| Robust and communication-efficient federated learning from non-iid data F Sattler, S Wiedemann, KR Müller, W Samek IEEE transactions on neural networks and learning systems 31 (9), 3400--3413, 2020 | 2119 | 2020 |
| Explaining nonlinear classification decisions with deep taylor decomposition G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller Pattern recognition 65, 211-222, 2017 | 1947 | 2017 |
| Schnet: A continuous-filter convolutional neural network for modeling quantum interactions KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ... Advances in Neural Information Processing Systems (NIPS) 30, 992--1002, 2017 | 1919 | 2017 |
| Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8, 13890, 2017 | 1910 | 2017 |
| 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 | 1878 | 2021 |
| Soft margins for AdaBoost G Rätsch, T Onoda, KR Müller Machine learning 42 (3), 287-320, 2001 | 1801 | 2001 |
| Input space versus feature space in kernel-based methods B Schölkopf, S Mika, CJC Burges, P Knirsch, KR Müller, G Rätsch, ... IEEE transactions on neural networks 10 (5), 1000-1017, 1999 | 1766 | 1999 |
| Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019 | 1749 | 2019 |
| Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... Chemical Reviews 121 (16), 10142–10186, 2021 | 1722 | 2021 |
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
