Wojciech Samek

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerifierad e-postadress på tu-berlin.de
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteVerifierad e-postadress på hhi.fraunhofer.de
Grégoire MontavonGuest professor, Freie Universität BerlinVerifierad e-postadress på fu-berlin.de
Alexander BinderChair Computer Vision, OvGU, Magdeburg, GermanyVerifierad e-postadress på ovgu.de
Thomas WiegandProfessor, TU Berlin and Fraunhofer HHI, Berlin, GermanyVerifierad e-postadress på tu-berlin.de
Sebastian BosseHead of Interactive & Cognitive Systems, Fraunhofer HHI, GermanyVerifierad e-postadress på hhi.fraunhofer.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerifierad e-postadress på atr.jp
Leila ArrasResearch Associate, Fraunhofer HHI, BIFOLD, Berlin, GermanyVerifierad e-postadress på hhi.fraunhofer.de
Nils StrodthoffProfessor for eHealth/AI4Health, Oldenburg University, GermanyVerifierad e-postadress på uol.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Verifierad e-postadress på lmu.de
Vignesh SrinivasanFraunhofer HHIVerifierad e-postadress på hhi.fraunhofer.de
Shinichi NakajimaBIFOLD, Technische Universität BerlinVerifierad e-postadress på tu-berlin.de
Carmen VidaurreIkerbasque Research Associate Prof., BCBL & BIFOLD TU-BerlinVerifierad e-postadress på bcbl.eu
Sven DähneAmazonVerifierad e-postadress på amazon.com
Peter EisertProfessor Visual Computing, Humboldt University Berlin, Fraunhofer HHIVerifierad e-postadress på informatik.hu-berlin.de
Arno VillringerDirector, Max Planck SocietyVerifierad e-postadress på cbs.mpg.de
Felix BiessmannEinstein Center Digital Future, Berlin University of Applied SciencesVerifierad e-postadress på bht-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerifierad e-postadress på tu-berlin.de

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Wojciech Samek

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

Verifierad e-postadress på tu-berlin.de - Startsida

Deep Learning Interpretability Explainable AI Trustworthy AI Federated Learning


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	5197	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018	3067	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	1917	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	1693	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	1687	2020
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	1447	2017
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	1373	2019
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	1256	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, 1096, 2019	1245	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	1198	2018
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	1109	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	1017	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	936	2019
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	852	2020
Towards explainable artificial intelligence W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 11700 …, 2019	773	2019
Artificial Intelligence in Dentistry: Chances and Challenges F Schwendicke, W Samek, J Krois Journal of Dental Research 99 (7), 769-774, 2020	766	2020
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	569	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	470	2017
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	445	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	437	2019

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