Wojciech Samek

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	Toutes	Depuis 2016
Citations	8960	8473
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indice i10	89	80

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Coauteurs

Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Adresse e-mail validée de tu-berlin.de
Alexander BinderAssociate Professor, University of Oslo (UiO)Adresse e-mail validée de ifi.uio.no
Grégoire MontavonTechnische Universität BerlinAdresse e-mail validée de tu-berlin.de
Sebastian Lapuschkin (né Bach)Head of Explainable AI Group, Fraunhofer Heinrich Hertz InstituteAdresse e-mail validée de hhi.fraunhofer.de
Thomas WiegandProfessor, TU Berlin and Fraunhofer HHI, Berlin, GermanyAdresse e-mail validée de tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalAdresse e-mail validée de atr.jp
Sebastian BosseHead of Interactive & Cognitive Systems, Fraunhofer HHI, GermanyAdresse e-mail validée de hhi.fraunhofer.de
simon wiedemannM. Sc.Adresse e-mail validée de wiedemann.es
Vignesh SrinivasanFraunhofer HHIAdresse e-mail validée de hhi.fraunhofer.de
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinAdresse e-mail validée de tu-berlin.de
Felix SattlerFraunhofer Heinrich Hertz InstituteAdresse e-mail validée de hhi.fraunhofer.de
Leila ArrasResearch Associate, Fraunhofer HHI, GermanyAdresse e-mail validée de hhi.fraunhofer.de
Carmen VidaurreBerlin University of Technology, Universidad Pública de NavarraAdresse e-mail validée de unavarra.es
Frederick KlauschenInstitute of Pathology, Charité Universitätsmedizin BerlinAdresse e-mail validée de charite.de
Nils StrodthoffHead of Applied Machine Learning Group, Fraunhofer HHI, GermanyAdresse e-mail validée de hhi.fraunhofer.de
Sven DähneAmazonAdresse e-mail validée de amazon.com
Felix BiessmannEinstein Center Digital Future, Beuth UniversityAdresse e-mail validée de beuth-hochschule.de
Peter EisertProfessor Visual Computing, Humboldt University Berlin, Fraunhofer HHIAdresse e-mail validée de informatik.hu-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupAdresse e-mail validée de tu-berlin.de
Arno VillringerDirector, Max Planck SocietyAdresse e-mail validée de cbs.mpg.de

Wojciech Samek

Head of AI Department and Explainable AI Group, Fraunhofer HHI, Germany

Adresse e-mail validée de hhi.fraunhofer.de - Page d'accueil

Machine Learning Interpretability Deep Learning Explainable AI Federated Learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	1502	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018	988	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	586	2017
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	536	2018
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	488	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	365	2018
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	247	2019
Stationary common spatial patterns for brain–computer interfacing W Samek, C Vidaurre, KR Müller, M Kawanabe Journal of Neural Engineering 9 (2), 026013, 2012	218	2012
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	199	2016
"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	186	2017
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	183	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	164	2020
Explainable AI: Interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedali, LK Hansen, KR Müller Springer Nature, 2019	163	2019
Divergence-based framework for common spatial patterns algorithms W Samek, M Kawanabe, KR Müller IEEE Reviews in Biomedical Engineering 7, 50-72, 2014	159	2014
Transferring Subspaces Between Subjects in Brain-Computer Interfacing W Samek, FC Meinecke, KR Müller IEEE Transactions on Biomedical Engineering 60 (8), 2289-2298, 2013	144	2013
A deep neural network for image quality assessment S Bosse, D Maniry, T Wiegand, W Samek 23th IEEE International Conference on Image Processing (ICIP), 3773-3777, 2016	137	2016
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	134	2016
Analyzing classifiers: Fisher vectors and deep neural networks S Lapuschkin, A Binder, G Montavon, KR Muller, W Samek IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2912-2920, 2016	119	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	110	2019
The LRP toolbox for artificial neural networks S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek Journal of Machine Learning Research 17 (1), 3938-3942, 2016	92	2016

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