| 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 | 5469 | 2015 |
| Explaining nonlinear classification decisions with deep taylor decomposition G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller Pattern recognition 65, 211-222, 2017 | 1765 | 2017 |
| Evaluating the visualization of what a deep neural network has learned W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller IEEE transactions on neural networks and learning systems 28 (11), 2660-2673, 2016 | 1500 | 2016 |
| 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 | 1401 | 2021 |
| Unmasking Clever Hans predictors and assessing what machines really learn S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, KR Müller Nature communications 10 (1), 1096, 2019 | 1336 | 2019 |
| Layer-wise relevance propagation: an overview G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller Explainable AI: interpreting, explaining and visualizing deep learning, 193-209, 2019 | 1018 | 2019 |
| Layer-wise relevance propagation for neural networks with local renormalization layers A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016 | 598 | 2016 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... The Journal of Machine Learning Research 20 (93), 1-8, 2019 | 459 | 2019 |
| Interpretable deep neural networks for single-trial EEG classification I Sturm, S Lapuschkin, W Samek, KR Müller Journal of neuroscience methods 274, 141-145, 2016 | 455 | 2016 |
| Pruning by explaining: A novel criterion for deep neural network pruning SK Yeom, P Seegerer, S Lapuschkin, A Binder, S Wiedemann, KR Müller, ... Pattern Recognition 115, 107899, 2021 | 295 | 2021 |
| AudioMNIST: exploring explainable artificial intelligence for audio analysis on a simple benchmark S Becker, J Vielhaben, M Ackermann, KR Müller, S Lapuschkin, W Samek Journal of the Franklin Institute 361 (1), 418-428, 2024 | 282* | 2024 |
| Analyzing classifiers: Fisher vectors and deep neural networks S Lapuschkin, A Binder, G Montavon, KR Muller, W Samek Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 277 | 2016 |
| Explaining the unique nature of individual gait patterns with deep learning F Horst, S Lapuschkin, W Samek, KR Müller, WI Schöllhorn Scientific reports 9 (1), 2391, 2019 | 272 | 2019 |
| Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond A Hedström, L Weber, D Krakowczyk, D Bareeva, F Motzkus, W Samek, ... Journal of Machine Learning Research 24 (34), 1-11, 2023 | 252 | 2023 |
| Layer-wise relevance propagation for deep neural network architectures A Binder, S Bach, G Montavon, KR Müller, W Samek Information Science and Applications (ICISA) 2016, LNEE 6679, 913-922, 2016 | 209 | 2016 |
| Towards best practice in explaining neural network decisions with LRP M Kohlbrenner, A Bauer, S Nakajima, A Binder, W Samek, S Lapuschkin 2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020 | 206 | 2020 |
| Understanding and comparing deep neural networks for age and gender classification S Lapuschkin, A Binder, KR Muller, W Samek Proceedings of the IEEE international conference on computer vision …, 2017 | 187 | 2017 |
| The LRP toolbox for artificial neural networks S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek Journal of Machine Learning Research 17 (114), 1-5, 2016 | 182 | 2016 |
| Resolving challenges in deep learning-based analyses of histopathological images using explanation methods M Hägele, P Seegerer, S Lapuschkin, M Bockmayr, W Samek, ... Scientific reports 10 (1), 6423, 2020 | 179 | 2020 |
| From Attribution Maps to Human-understandable Explanations through Concept Relevance Propagation R Achtibat, M Dreyer, I Eisenbraun, S Bosse, T Wiegand, W Samek, ... Nature Machine Intelligence 5 (9), 1006-1019, 2023 | 174 | 2023 |
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowBestätigte E-Mail-Adresse bei tu-berlin.de
