Wojciech Samek

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Citations	17195	16378
h-index	48	48
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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Sebastian Lapuschkin (né Bach)Head of Explainable AI, Fraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.de
Grégoire MontavonTechnische Universität BerlinVerified email at tu-berlin.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
Sebastian BosseHead of Interactive & Cognitive Systems, Fraunhofer HHI, GermanyVerified email at hhi.fraunhofer.de
Felix SattlerFraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.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
Nils StrodthoffProfessor for eHealth/AI4Health, Oldenburg University, GermanyVerified email at uol.de
Carmen VidaurreIkerbasque Research Associate, Tecnalia, NeurotechVerified email at tecnalia.com
Frederick KlauschenInstitute of Pathology, Charité Universitätsmedizin BerlinVerified email at charite.de
Sven DähneAmazonVerified email at amazon.com
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
Arno VillringerDirector, Max Planck SocietyVerified email at cbs.mpg.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerified email at tu-berlin.de
simon wiedemann

Wojciech Samek

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

Verified email at hhi.fraunhofer.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	2668	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018	1645	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	1062	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	982	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	801	2017
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	615	2018
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	593	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	589	2019
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	567	2020
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	318	2019
Towards explainable artificial intelligence W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 11700 …, 2019	306	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	296	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	292	2017
"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	277	2017
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	270*	2021
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	252	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	249	2016
Stationary common spatial patterns for brain–computer interfacing W Samek, C Vidaurre, KR Müller, M Kawanabe Journal of Neural Engineering 9 (2), 026013, 2012	241	2012
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	236	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	222	2021

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