| 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 | 5384 | 2015 |
| Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018 | 3160 | 2018 |
| Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models W Samek, T Wiegand, KR Müller ITU Journal: ICT Discoveries 1 (1), 39-48, 2018 | 2034 | 2018 |
| 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 | 1760 | 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 | 1743 | 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, 2017 | 1485 | 2017 |
| Explainable AI: Interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedali, LK Hansen, KR Müller Lecture Notes in Computer Science, Springer 11700, 1-439, 2019 | 1421 | 2019 |
| 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 | 1350 | 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, 1096, 2019 | 1306 | 2019 |
| Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment S Bosse, D Maniry, KR Müller, T Wiegand, W Samek IEEE Transactions on Image Processing 27 (1), 206-219, 2018 | 1240 | 2018 |
| Clustered federated learning: Model-agnostic distributed multi-task optimization under privacy constraints F Sattler, KR Müller, W Samek IEEE Transactions on Neural Networks and Learning Systems 32 (8), 3710-3722, 2021 | 1199 | 2021 |
| A unifying review of deep and shallow anomaly detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE 109 (5), 756-795, 2021 | 1087 | 2021 |
| Layer-Wise Relevance Propagation: An Overview G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 11700 …, 2019 | 982 | 2019 |
| PTB-XL, a large publicly available electrocardiography dataset P Wagner, N Strodthoff, RD Bousseljot, D Kreiseler, FI Lunze, W Samek, ... Scientific Data 7 (1), 1-15, 2020 | 942 | 2020 |
| Artificial Intelligence in Dentistry: Chances and Challenges F Schwendicke, W Samek, J Krois Journal of Dental Research 99 (7), 769-774, 2020 | 843 | 2020 |
| Towards explainable artificial intelligence W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 11700 …, 2019 | 806 | 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, LNCS 9887, 63-71, 2016 | 584 | 2016 |
| Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek EMNLP'17 Workshop on Computational Approaches to Subjectivity, Sentiment …, 2017 | 477 | 2017 |
| 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 | 449 | 2016 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... Journal of Machine Learning Research 20 (93), 1-8, 2019 | 445 | 2019 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany在 tu-berlin.de 的电子邮件经过验证