Klaus-Robert Müller
Klaus-Robert Müller
Google DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany
Verified email at
Cited by
Cited by
Nonlinear component analysis as a kernel eigenvalue problem
B Schölkopf, A Smola, KR Müller
Neural computation 10 (5), 1299-1319, 1998
Efficient backprop
Y LeCun, L Bottou, G Orr, KR Müller
Neural networks: Tricks of the trade 7700, 9-53, 2012
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
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
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
Methods for interpreting and understanding deep neural networks
G Montavon, W Samek, KR Müller
Digital Signal Processing 73, 1-15, 2018
Kernel principal component analysis
B Schölkopf, A Smola, KR Müller
Artificial Neural Networks—ICANN'97, 583-588, 1997
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
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
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
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
Soft margins for AdaBoost
G Rätsch, T Onoda, KR Müller
Machine learning 42, 287-320, 2001
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
Explaining nonlinear classification decisions with deep taylor decomposition
G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
Pattern recognition 65, 211-222, 2017
Quantum-chemical insights from deep tensor neural networks
KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko
Nature Communications 8, 13890, 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
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
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
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
How to explain individual classification decisions
D Baehrens, T Schroeter, S Harmeling, M Kawanabe, K Hansen, ...
Journal of Machine Learning Research 11 (Jun), 1803-1831, 2010
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