Wojciech Samek

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Citations	24017	22646
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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Grégoire MontavonFreie Universität BerlinVerified email at fu-berlin.de
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.de
Alexander BinderAssociate Professor, Singapore Institute of Technology (SIT) and University of Oslo (UiO)Verified email at singaporetech.edu.sg
Thomas WiegandProfessor, TU Berlin and Fraunhofer HHI, Berlin, GermanyVerified email at tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Felix SattlerFraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.de
Sebastian BosseHead of Interactive & Cognitive Systems, Fraunhofer HHI, GermanyVerified email at hhi.fraunhofer.de
Nils StrodthoffProfessor for eHealth/AI4Health, Oldenburg University, GermanyVerified email at uol.de
Vignesh SrinivasanFraunhofer HHIVerified email at hhi.fraunhofer.de
Leila ArrasResearch Associate, Fraunhofer HHI, GermanyVerified email at hhi.fraunhofer.de
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Verified email at lmu.de
Carmen VidaurreIkerbasque Research Associate, Tecnalia, NeurotechVerified email at tecnalia.com
Sven DähneAmazonVerified email at amazon.com
Arno VillringerDirector, Max Planck SocietyVerified email at cbs.mpg.de
Felix BiessmannEinstein Center Digital Future, Berlin University of Applied SciencesVerified email at bht-berlin.de
Peter EisertProfessor Visual Computing, Humboldt University Berlin, Fraunhofer HHIVerified email at informatik.hu-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerified email at tu-berlin.de

Wojciech Samek

Professor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD Fellow

Verified email at tu-berlin.de - Homepage

Machine Learning Interpretability Deep Learning Explainable AI Federated Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	3529	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018	2192	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	1371	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	1252	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	1048	2017
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	962	2020
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	873	2019
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	839	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	837	2018
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	558*	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	520	2019
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	479	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	471	2021
Towards explainable artificial intelligence W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 11700 …, 2019	426	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	366	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	363	2017
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	339	2016
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	337	2020
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	320	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	319	2017

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