Klaus‐Robert Müller

74.0k total citations · 26 hit papers
385 papers, 41.6k citations indexed

About

Klaus‐Robert Müller is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Klaus‐Robert Müller has authored 385 papers receiving a total of 41.6k indexed citations (citations by other indexed papers that have themselves been cited), including 166 papers in Cognitive Neuroscience, 110 papers in Artificial Intelligence and 85 papers in Signal Processing. Recurrent topics in Klaus‐Robert Müller's work include EEG and Brain-Computer Interfaces (141 papers), Neural dynamics and brain function (94 papers) and Blind Source Separation Techniques (69 papers). Klaus‐Robert Müller is often cited by papers focused on EEG and Brain-Computer Interfaces (141 papers), Neural dynamics and brain function (94 papers) and Blind Source Separation Techniques (69 papers). Klaus‐Robert Müller collaborates with scholars based in Germany, South Korea and United States. Klaus‐Robert Müller's co-authors include Bernhard Schölkopf, Wojciech Samek, Alexander J. Smola, Grégoire Montavon, Benjamin Blankertz, Alexandre Tkatchenko, Alexander Binder, Gabriel Curio, Matthias Rupp and Steven Lemm and has published in prestigious journals such as Chemical Reviews, Physical Review Letters and Nucleic Acids Research.

In The Last Decade

Klaus‐Robert Müller

376 papers receiving 40.2k citations

Hit Papers

Nonlinear Component Analysis as a Kernel Eigenvalue Problem 1998 2026 2007 2016 1998 2015 2012 2017 2008 1000 2.0k 3.0k 4.0k 5.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Nonlinear Component Analysis as a Kernel Eigenvalue Problem
    1998 5.3k citations Klaus‐Robert Müller et al. profile →
  2. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
    2015 2.5k citations Grégoire Montavon, Frederick Klauschen et al. PLoS ONE profile →
  3. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
    2012 1.6k citations Alexandre Tkatchenko, Klaus‐Robert Müller et al. profile →
  4. Methods for interpreting and understanding deep neural networks
    2017 1.5k citations Grégoire Montavon, Wojciech Samek et al. profile →
  5. Optimizing Spatial filters for Robust EEG Single-Trial Analysis
    2008 1.5k citations Benjamin Blankertz, Klaus‐Robert Müller et al. profile →
  6. Efficient BackProp
    1998 951 citations Klaus‐Robert Müller et al. profile →
  7. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
    2019 876 citations Wojciech Samek, Grégoire Montavon et al. profile →
  8. Single-trial analysis and classification of ERP components — A tutorial
    2010 845 citations Benjamin Blankertz, Klaus‐Robert Müller et al. profile →
  9. Review of the BCI Competition IV
    2012 829 citations Michael Tangermann, Klaus‐Robert Müller et al. profile →
  10. Machine learning of accurate energy-conserving molecular force fields
    2017 810 citations Stefan Chmiela, Alexandre Tkatchenko et al. Science Advances profile →
  11. Explaining nonlinear classification decisions with deep Taylor decomposition
    2016 788 citations Grégoire Montavon, Wojciech Samek et al. Pattern Recognition profile →
  12. The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects
    2007 704 citations Benjamin Blankertz, Guido Dornhege et al. profile →
  13. Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
    2021 702 citations Wojciech Samek, Grégoire Montavon et al. profile →
  14. Neural Networks: Tricks of the Trade
    1998 647 citations Klaus‐Robert Müller et al. profile →
  15. Evaluating the Visualization of What a Deep Neural Network Has Learned
    2016 620 citations Wojciech Samek, Grégoire Montavon et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  16. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
    2015 585 citations Klaus‐Robert Müller, Alexandre Tkatchenko et al. The Journal of Physical Chemistry Letters profile →
  17. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
    2021 546 citations Valentín Vassilev-Galindo, Stefan Chmiela et al. profile →
  18. Kernel PCA and De-Noising in Feature Spaces
    1998 535 citations Klaus‐Robert Müller et al. profile →
  19. Neurophysiological predictor of SMR-based BCI performance
    2010 521 citations Benjamin Blankertz, Claudia Sannelli et al. profile →
  20. Covariate Shift Adaptation by Importance Weighted Cross Validation
    2007 448 citations Matthias Krauledat, Klaus‐Robert Müller et al. profile →
  21. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
    2013 443 citations Grégoire Montavon, Alexandre Tkatchenko et al. Journal of Chemical Theory and Computation profile →
  22. Finding Density Functionals with Machine Learning
    2012 414 citations Klaus‐Robert Müller et al. profile →
  23. Quantum chemical accuracy from density functional approximations via machine learning
    2020 264 citations Klaus‐Robert Müller et al. Nature Communications profile →
  24. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
    2021 215 citations Oliver T. Unke, Stefan Chmiela et al. Nature Communications profile →
  25. Accurate global machine learning force fields for molecules with hundreds of atoms
    2023 112 citations Stefan Chmiela, Valentín Vassilev-Galindo et al. Science Advances profile →
  26. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
    2024 67 citations Oliver T. Unke, Michael Gastegger et al. Science Advances profile →

Peers

Klaus‐Robert Müller
Comparison fields: 5 of 230
  • Cognitive Neuroscience 14.4k
  • Artificial Intelligence 11.0k
  • Computer Vision and Pattern Recognition 6.3k
  • Materials Chemistry 6.0k
  • Signal Processing 5.2k
Replace Demis Hassabis with:
Demis Hassabis United Kingdom
J. J. Hopfield United States
K. Müller Germany
Bernhard Schölkopf Germany
David E. Rumelhart United States
H. Sebastian Seung United States
Corinna Cortes United States
Jürgen Kurths Germany
Terrence J. Sejnowski United States
Chih‐Jen Lin Taiwan
Demis Hassabis United Kingdom View profile →
Citations per field, relative to Klaus‐Robert Müller
Klaus‐Robert Müller · 1×
Citations per year, relative to Klaus‐Robert Müller
Klaus‐Robert Müller · 1×

Countries citing papers authored by Klaus‐Robert Müller

Since Specialization
Citations

This map shows the geographic impact of Klaus‐Robert Müller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Klaus‐Robert Müller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Klaus‐Robert Müller more than expected).

Fields of papers citing papers by Klaus‐Robert Müller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Klaus‐Robert Müller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Klaus‐Robert Müller. The network helps show where Klaus‐Robert Müller may publish in the future.

Co-authorship network of co-authors of Klaus‐Robert Müller

This figure shows the co-authorship network connecting the top 25 collaborators of Klaus‐Robert Müller. A scholar is included among the top collaborators of Klaus‐Robert Müller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Klaus‐Robert Müller. Klaus‐Robert Müller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence
2023 ·Journal of Chemical Theory and Computation ·(unknown), Klaus‐Robert Müller, Stefan Chmiela
1
2
Accurate global machine learning force fields for molecules with hundreds of atoms breakdown →
2023 ·Science Advances ·Stefan Chmiela, Valentín Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus‐Robert Müller
112
3
Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation
2023 ·PLoS Computational Biology ·Kai J. Miller, Klaus‐Robert Müller, (unknown), Harvey Huang, Nicholas M. Gregg, Gregory A. Worrell, Dora Hermes
11
4
Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights
2022 ·Neuropathology and Applied Neurobiology ·Leonille Schweizer, Philipp Seegerer, (unknown), (unknown), (unknown), (unknown), Anja Osterloh, Carsten Dittmayer, (unknown), Debora Pehl, Annekathrin Reinhardt, Klemens Ruprecht, Werner Stenzel, Annika K. Wefers, Patrick N. Harter, Ulrich Schüller, Frank L. Heppner, Maximilian Alber, Klaus‐Robert Müller, Frederick Klauschen
4
5
From Clustering to Cluster Explanations via Neural Networks
2022 ·IEEE Transactions on Neural Networks and Learning Systems ·(unknown), (unknown), Lukas Ruff, Grégoire Montavon, Wojciech Samek, Klaus‐Robert Müller
44
6
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
2022 ·The Journal of Physical Chemistry Letters ·(unknown), Klaus‐Robert Müller, Stefan Chmiela
11
7
BIGDML—Towards accurate quantum machine learning force fields for materials
2022 ·Nature Communications ·Huziel E. Sauceda, (unknown), Stefan Chmiela, Lauro Oliver Paz‐Borbón, Klaus‐Robert Müller, Alexandre Tkatchenko
54
8
DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies
2021 ·NAR Genomics and Bioinformatics ·(unknown), (unknown), Juan Antonio Rodríguez, Marina Höhne, Nico Görnitz, Klaus‐Robert Müller
31
9
On the Byzantine Robustness of Clustered Federated Learning
2020 ·Fraunhofer-Publica (Fraunhofer-Gesellschaft) ·Felix Sattler, Klaus‐Robert Müller, Thomas Wiegand, Wojciech Samek
104
10
Towards explaining anomalies: A deep Taylor decomposition of one-class models
2020 ·Pattern Recognition ·(unknown), Klaus‐Robert Müller, Grégoire Montavon
63
11
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
2020 ·The Journal of Chemical Physics ·Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Klaus‐Robert Müller, Alexandre Tkatchenko
34
12
A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity
2019 ·PLoS ONE ·Claudia Sannelli, Carmen Vidaurre, Klaus‐Robert Müller, Benjamin Blankertz
72
13
Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces
2019 ·Bulletin of the American Physical Society ·Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus‐Robert Müller, Alexandre Tkatchenko
1
14
Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases
2019 ·Science Translational Medicine ·Philipp Jurmeister, Michael Bockmayr, Philipp Seegerer, (unknown), Denise Treue, Grégoire Montavon, Claudia Vollbrecht, Alexander Arnold, Daniel Teichmann, Keno K. Bressem, Ulrich Schüller, Maximilian von Laffert, Klaus‐Robert Müller, David Capper, Frederick Klauschen
78
15
The P300 BCI: on its Way to End-Users?
2017 ·Andrea Kübler, Klaus‐Robert Müller, Cuntai Guan
0
16
On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP
2015 ·PubMed ·Irene Winkler, Stefan Debener, Klaus‐Robert Müller, Michael Tangermann
274
17
The Berlin Brain-Computer Interface presents the novel mental typewriter Hex-o-Spell
2006 ·Maynooth University ePrints and eTheses Archive (Maynooth University) ·Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Michael Schröder, John Williamson, Roderick Murray‐Smith, Klaus‐Robert Müller
74
18
New Inference Concepts for Analysing Complex Data
2005 ·Oberwolfach Reports ·Jianqing Fan, Klaus‐Robert Müller, Vladimir Spokoiny
0
19
The Berlin Brain-Computer Interface: Report from the Feedback Sessions
2005 ·Fraunhofer-Publica (Fraunhofer-Gesellschaft) ·Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus‐Robert Müller, Gabriel Curio
10
20
Preface
1987 ·Annual Review of Physical Chemistry ·Frank Noé, Alexandre Tkatchenko, Klaus‐Robert Müller, Cecilia Clementi, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), 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0

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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