| 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 |
| Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018 | 999 | 2018 |
| 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 | 492 | 2016 |
| Assessment and validation of machine learning methods for predicting molecular atomization energies K Hansen, G Montavon, F Biegler, S Fazli, M Rupp, M Scheffler, ... Journal of Chemical Theory and Computation 9 (8), 3404-3419, 2013 | 448 | 2013 |
| Machine learning of molecular electronic properties in chemical compound space G Montavon, M Rupp, V Gobre, A Vazquez-Mayagoitia, K Hansen, ... New Journal of Physics 15 (9), 095003, 2013 | 440 | 2013 |
| Neural networks: tricks of the trade G Montavon, G Orr, KR Müller springer, 2012 | 373* | 2012 |
| 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 |
| " What is relevant in a text document?": An interpretable machine learning approach L Arras, F Horn, G Montavon, KR Müller, W Samek PloS one 12 (8), e0181142, 2017 | 187 | 2017 |
| Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017 | 186 | 2017 |
| Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019 | 170 | 2019 |
| 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 |
| Learning Invariant Representations of Molecules for Atomization Energy Prediction G Montavon, K Hansen, S Fazli, M Rupp, F Biegler, A Ziehe, ... Advances in Neural Information Processing Systems 25, 449-457, 2012 | 136* | 2012 |
| 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 | 130 | 2016 |
| Kernel Analysis of Deep Networks G Montavon, ML Braun, KR Müller Journal of Machine Learning Research 12, 2563-2581, 2011 | 114 | 2011 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019 | 111 | 2019 |
| Deep Boltzmann machines and the centering trick G Montavon, KR Müller Neural networks: tricks of the trade, 621-637, 2012 | 99 | 2012 |
| Wasserstein training of restricted Boltzmann machines G Montavon, KR Müller, M Cuturi Proceedings of the 30th International Conference on Neural Information …, 2016 | 93* | 2016 |
| 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 |
