| 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 doi: 10.1371/journal.pone.0130140, 2015 | 5327 | 2015 |
| Deep one-class classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International Conference on Machine Learning, 4393-4402, 2018 | 2648 | 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 | 1725 | 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, 2017 | 1473 | 2017 |
| 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 | 1284 | 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 | 967 | 2019 |
| Deep Semi-Supervised Anomaly Detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... arXiv preprint arXiv:1906.02694, 2019 | 756 | 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 | 582 | 2016 |
| The SHOGUN machine learning toolbox S Sonnenburg, G Rätsch, S Henschel, C Widmer, J Behr, A Zien, F Bona, ... The Journal of Machine Learning Research 11, 1799-1802, 2010 | 415 | 2010 |
| 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, 107899, 2021 | 267 | 2021 |
| 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 | 258 | 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, 2016 | 208 | 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 | 198 | 2020 |
| Understanding and Comparing Deep Neural Networks for Age and Gender Classification S Lapuschkin, A Binder, KR Müller, W Samek of the IEEE Conference on Computer Vision and Pattern Recognition, 1629-1638, 2017 | 184 | 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 | 181 | 2016 |
| Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning F Klauschen, KR Müller, A Binder, M Bockmayr, M Hägele, P Seegerer, ... Seminars in cancer biology 52, 151-157, 2018 | 173 | 2018 |
| 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 | 171 | 2020 |
| Morphological and molecular breast cancer profiling through explainable machine learning A Binder, M Bockmayr, M Hägele, S Wienert, D Heim, K Hellweg, M Ishii, ... Nature Machine Intelligence 3 (4), 355-366, 2021 | 154 | 2021 |
| DeepClue: Visual Interpretation of Text-based Deep Stock Prediction L Shi, Z Teng, L Wang, Y Zhang, A Binder IEEE Transactions on Knowledge and Data Engineering, 2018 | 122 | 2018 |
| Explanation-guided training for cross-domain few-shot classification J Sun, S Lapuschkin, W Samek, Y Zhao, NM Cheung, A Binder 2020 25th International Conference on Pattern Recognition (ICPR), 7609-7616, 2021 | 114 | 2021 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & 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
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteПодтвержден адрес электронной почты в домене hhi.fraunhofer.de
Grégoire MontavonGuest professor, Freie Universität BerlinПодтвержден адрес электронной почты в домене fu-berlin.de
Marius KloftProfessor, RPTU Kaiserslautern-LandauПодтвержден адрес электронной почты в домене cs.uni-kl.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Подтвержден адрес электронной почты в домене lmu.de
Nico GoernitzFreelancer | CTO | ex-postdoc@TU-BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Lukas RuffAignosticsПодтвержден адрес электронной почты в домене aignostics.com
Yuval EloviciHead of Cyber@BGU, Director of Telekom Innovation Laboratories at BGU, Ben Gurion UniversityПодтвержден адрес электронной почты в домене bgu.ac.il
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundПодтвержден адрес электронной почты в домене cs.tu-dortmund.de
Robert A. VandermeulenIndependent ResearcherПодтвержден адрес электронной почты в домене umich.edu
Penny ChongIBM Research, SingaporeПодтвержден адрес электронной почты в домене ibm.com
Shinichi NakajimaBIFOLD, Technische Universität BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Shoaib Ahmed SiddiquiUniversity of CambridgeПодтвержден адрес электронной почты в домене cam.ac.uk
