| 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 | 1519 | 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 | 589 | 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 | 489 | 2016 |
| 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), 1-8, 2019 | 250 | 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 | 199 | 2016 |
| Layer-wise relevance propagation for neural networks with local renormalization layers A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek International Conference on Artificial Neural Networks, 63-71, 2016 | 136 | 2016 |
| 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 | 120 | 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 | 111 | 2019 |
| The LRP toolbox for artificial neural networks S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek The Journal of Machine Learning Research 17 (1), 3938-3942, 2016 | 92 | 2016 |
| 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 | 85 | 2019 |
| 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 | 72 | 2016 |
| 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 | 67 | 2017 |
| 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), 1-13, 2019 | 54 | 2019 |
| Interpreting and explaining deep neural networks for classification of audio signals S Becker, M Ackermann, S Lapuschkin, KR Müller, W Samek arXiv preprint arXiv:1807.03418, 2018 | 40 | 2018 |
| Toward interpretable machine learning: Transparent deep neural networks and beyond W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller arXiv preprint arXiv:2003.07631, 2020 | 29 | 2020 |
| 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), 1-12, 2020 | 25 | 2020 |
| Interpretable human action recognition in compressed domain V Srinivasan, S Lapuschkin, C Hellge, KR Müller, W Samek 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 24 | 2017 |
| 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 | 16 | 2020 |
| Interpreting the predictions of complex ml models by layer-wise relevance propagation W Samek, G Montavon, A Binder, S Lapuschkin, KR Müller arXiv preprint arXiv:1611.08191, 2016 | 16 | 2016 |
| Controlling explanatory heatmap resolution and semantics via decomposition depth S Bach, A Binder, KR Müller, W Samek IEEE International Conference on Image Processing (ICIP), 2271-2275, 2016 | 15 | 2016 |
Wojciech SamekHead of AI Department and Explainable AI Group, Fraunhofer HHI, GermanyBestätigte E-Mail-Adresse bei hhi.fraunhofer.de
