Klaus-Robert Müller

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Citations	156564	80620
h-index	163	116
i10-index	567	408

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Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerified email at tu-berlin.de
Grégoire MontavonGuest professor, Freie Universität BerlinVerified email at fu-berlin.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSCVerified email at uni.lu
Alexander BinderChair Computer Vision, OvGU, Magdeburg, GermanyVerified email at ovgu.de
Alex SmolaBoson AIVerified email at smola.org
Sebastian MikaVerified email at sebastianmika.info
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Kristof T. SchüttPfizer, Machine Learning ResearchVerified email at pfizer.com
Frank C. MeineckeDirector of Applied Machine Learning, TomTomVerified email at tomtom.com
Andreas ZiehePostdoc researcher, TU BerlinVerified email at cs.tu-berlin.de
Carmen VidaurreIkerbasque Research Associate Prof., BCBL & BIFOLD TU-BerlinVerified email at bcbl.eu
Huziel E. SaucedaAssistant Professor, Instituto de Física, Universidad Nacional Autonoma de MexicoVerified email at fisica.unam.mx
Matthias KrauledatRhine-Waal University of Applied SciencesVerified email at hochschule-rhein-waal.de
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Guido NolteDept. of Neurophysiology and Pathophysiology, UKE, HamburgVerified email at uke.de
Thomas WiegandProfessor, TU Berlin and Fraunhofer HHI, Berlin, GermanyVerified email at tu-berlin.de
Siamac FazliNazarbayev UniversityVerified email at nu.edu.kz

Klaus-Robert Müller

Google DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany

Verified email at tu-berlin.de

Machine learning artificial intelligence big data computational neuroscience


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998	11225	1998
Efficient backprop Y LeCun, L Bottou, G Orr, KR Müller Neural networks: Tricks of the trade 7700, 9-53, 2012	7725*	2012
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	5459	2015
An introduction to kernel-based learning algorithms KR Müller, S Mika, G Rätsch, K Tsuda, B Schölkopf IEEE Transactions on Neural Networks 12 (2), 181 - 201, 2001	5073*	2001
Fisher discriminant analysis with kernels S Mika, G Rätsch, J Weston, B Schölkopf, KR Müller Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE …, 1999	3862	1999
Kernel principal component analysis B Schölkopf, A Smola, KR Müller Artificial Neural Networks—ICANN'97, 583-588, 1997	3613	1997
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018	3183	2018
Fast and accurate modeling of molecular atomization energies with machine learning M Rupp, A Tkatchenko, KR Müller, OA Von Lilienfeld Physical review letters 108 (5), 058301, 2012	2582	2012
Optimizing spatial filters for robust EEG single-trial analysis B Blankertz, R Tomioka, S Lemm, M Kawanabe, KR Müller IEEE Signal processing magazine 25 (1), 41-56, 2008	2441	2008
Schnet–a deep learning architecture for molecules and materials KT Schütt, HE Sauceda, PJ Kindermans, A Tkatchenko, KR Müller The Journal of Chemical Physics 148 (24), 2018	2110	2018
Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models W Samek, T Wiegand, KR Müller ITU Journal: ICT Discoveries - The Impact of Artifcial Intelligence (AI) on …, 2017	2107*	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	1801	2020
Soft margins for AdaBoost G Rätsch, T Onoda, KR Müller Machine learning 42, 287-320, 2001	1775	2001
Explaining nonlinear classification decisions with deep taylor decomposition G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller Pattern recognition 65, 211-222, 2017	1766	2017
Input space versus feature space in kernel-based methods B Schölkopf, S Mika, CJC Burges, P Knirsch, KR Müller, G Rätsch, ... IEEE transactions on neural networks 10 (5), 1000-1017, 1999	1752	1999
Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8, 13890, 2017	1703	2017
Kernel PCA and De-noising in feature spaces. S Mika, B Schölkopf, AJ Smola, KR Müller, M Scholz, G Rätsch Advances of Neural Information Processing Systems (NIPS) 11, 536-542, 1998	1507	1998
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	1506	2017
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ... Advances in Neural Information Processing Systems (NIPS) 30, 992--1002, 2017	1493	2017
Predicting time series with support vector machines KR Müller, A Smola, G Rätsch, B Schölkopf, J Kohlmorgen, V Vapnik Artificial Neural Networks—ICANN'97, 999-1004, 1997	1487	1997

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