| 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 | 5859 | 2015 |
| Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018 | 3354 | 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 | 1841 | 2017 |
| Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019 | 1605 | 2019 |
| 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 | 1598 | 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), 2021 | 1584 | 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 | 1441 | 2019 |
| 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, 1-40, 2021 | 1247 | 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, 193-209, 2019 | 1158 | 2019 |
| Neural networks-tricks of the trade second edition G Montavon, G Orr, KR Müller Springer, DOI 10, 978-3, 2012 | 1015* | 2012 |
| 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 | 844 | 2013 |
| 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 | 738 | 2013 |
| 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 | 648 | 2016 |
| Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017 | 495 | 2017 |
| 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 | 480 | 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 | 412 | 2017 |
| Higher-order explanations of graph neural networks via relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 7581 …, 2022 | 363* | 2022 |
| 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 | 286 | 2016 |
| 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, 913-922, 2016 | 218 | 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 | 218 | 2012 |
Klaus-Robert MüllerTU Berlin & Korea University & Google DeepMind & Max Planck Institute for Informatics, GermanyПодтвержден адрес электронной почты в домене tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowПодтвержден адрес электронной почты в домене tu-berlin.de
Alexander BinderProfessor, ScaDS.AI and Faculty of Math and Computer Science, Uni Leipzig, GermanyПодтвержден адрес электронной почты в домене uni-leipzig.de
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteПодтвержден адрес электронной почты в домене hhi.fraunhofer.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Подтвержден адрес электронной почты в домене lmu.de
Lukas RuffAignosticsПодтвержден адрес электронной почты в домене aignostics.com
Oliver EberleTU BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Leila ArrasResearch Associate, Fraunhofer HHI, BIFOLD, Berlin, GermanyПодтвержден адрес электронной почты в домене hhi.fraunhofer.de
Christopher J. AndersCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoПодтвержден адрес электронной почты в домене riken.jp
Thomas SchnakeTechnical University of BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Shinichi NakajimaBIFOLD, Technische Universität BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Подтвержден адрес электронной почты в домене list.lu
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinПодтвержден адрес электронной почты в домене utoronto.ca
Ping XiongTechnical University BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Pattarawat ChormaiTechnische Universität Berlin, Zuse School ELIZA, and Max Planck School of CognitionПодтвержден адрес электронной почты в домене tu-berlin.de
Marco CuturiAppleПодтвержден адрес электронной почты в домене apple.com
Léo AndéolInstitut de Mathématiques de Toulouse / SNCFПодтвержден адрес электронной почты в домене math.univ-toulouse.fr
