Klaus-Robert Müller

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Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowPatvirtintas el. paštas tu-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupPatvirtintas el. paštas tu-berlin.de
Grégoire MontavonGuest professor, Freie Universität BerlinPatvirtintas el. paštas fu-berlin.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHPatvirtintas el. paštas tuebingen.mpg.de
Gunnar RätschProfessor, ETH ZürichPatvirtintas el. paštas inf.ethz.ch
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSCPatvirtintas el. paštas uni.lu
Alexander BinderChair Computer Vision, OvGU, Magdeburg, GermanyPatvirtintas el. paštas ovgu.de
Alex SmolaBoson AIPatvirtintas el. paštas smola.org
Sebastian MikaPatvirtintas el. paštas sebastianmika.info
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalPatvirtintas el. paštas atr.jp
Kristof T. SchüttPfizer, Machine Learning ResearchPatvirtintas el. paštas pfizer.com
Frank C. MeineckeDirector of Applied Machine Learning, TomTomPatvirtintas el. paštas tomtom.com
Andreas ZiehePostdoc researcher, TU BerlinPatvirtintas el. paštas cs.tu-berlin.de
Huziel E. SaucedaAssistant Professor, Instituto de Física, Universidad Nacional Autonoma de MexicoPatvirtintas el. paštas fisica.unam.mx
Carmen VidaurreIkerbasque Research Associate Prof., BCBL & BIFOLD TU-BerlinPatvirtintas el. paštas bcbl.eu
Matthias KrauledatRhine-Waal University of Applied SciencesPatvirtintas el. paštas hochschule-rhein-waal.de
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andPatvirtintas el. paštas donders.ru.nl
Guido NolteDept. of Neurophysiology and Pathophysiology, UKE, HamburgPatvirtintas el. paštas uke.de
Thomas WiegandProfessor, TU Berlin and Fraunhofer HHI, Berlin, GermanyPatvirtintas el. paštas tu-berlin.de
Siamac FazliNazarbayev UniversityPatvirtintas el. paštas nu.edu.kz

Stebėti

Klaus-Robert Müller

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

Patvirtintas el. paštas tu-berlin.de

Machine learning artificial intelligence big data computational neuroscience


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998	11234	1998
Efficient backprop Y LeCun, L Bottou, G Orr, KR Müller Neural networks: Tricks of the trade 7700, 9-53, 2012	7743*	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	5491	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	5075*	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	3864	1999
Kernel principal component analysis B Schölkopf, A Smola, KR Müller Artificial Neural Networks—ICANN'97, 583-588, 1997	3622	1997
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15, 2018	3194	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	2591	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	2443	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	2129	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	2121*	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	1810	2020
Soft margins for AdaBoost G Rätsch, T Onoda, KR Müller Machine learning 42, 287-320, 2001	1776	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	1771	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	1712	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	1511	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	1508	1998
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	1504	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	1488	1997

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