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.
An introduction to kernel-based learning algorithms
20012.5k citationsK. Müller, Gunnar Rätsch et al.profile →
SchNet – A deep learning architecture for molecules and materials
20181.4k citationsKristof T. Schütt, Alexandre Tkatchenko et al.profile →
Quantum-chemical insights from deep tensor neural networks
2017967 citationsKristof T. Schütt, K. Müller et al.profile →
Soft Margins for AdaBoost
2001912 citationsGunnar Rätsch, Takashi Onoda et al.profile →
Input space versus feature space in kernel-based methods
1999823 citationsK. Müller, Gunnar Rätsch et al.profile →
The BCI competition III: validating alternative approaches to actual BCI problems
2006694 citationsBenjamin Blankertz, K. Müller et al.IEEE Transactions on Neural Systems and Rehabilitation Engineeringprofile →
Spatio-spectral filters for improving the classification of single trial EEG
2005503 citationsBenjamin Blankertz, Gabriel Curio et al.profile →
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
2004501 citationsGuido Dornhege, Benjamin Blankertz et al.profile →
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
2018305 citationsKristof T. Schütt, Alexandre Tkatchenko et al.Journal of Chemical Theory and Computationprofile →
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
2019303 citationsKristof T. Schütt, Alexandre Tkatchenko et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of K. 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 K. Müller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. Müller more than expected).
This network shows the impact of papers produced by K. 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 K. Müller. The network helps show where K. Müller may publish in the future.
Co-authorship network of co-authors of K. Müller
This figure shows the co-authorship network connecting the top 25 collaborators of K. Müller.
A scholar is included among the top collaborators of K. 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 K. Müller. K. Müller is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ruff, Lukas, et al.. (2021). Explainable Deep One-Class Classification. arXiv (Cornell University).9 indexed citations
5.
Alber, Maximilian, et al.. (2017). An Empirical Study on The Properties of Random Bases for Kernel Methods. neural information processing systems. 30. 2763–2774.
Montavon, Grégoire, K. Müller, & Marco Cuturi. (2016). Wasserstein Training of Restricted Boltzmann Machines. neural information processing systems. 29. 3718–3726.34 indexed citations
8.
Görnitz, Nico, Anne K. Porbadnigk, Alexander Binder, et al.. (2014). Learning and Evaluation in Presence of Non-i.i.d. Label Noise. Journal of Machine Learning Research. 33. 293–302.1 indexed citations
9.
Montavon, Grégoire, Mikio L. Braun, & K. Müller. (2012). Deep Boltzmann Machines as Feed-Forward Hierarchies. International Conference on Artificial Intelligence and Statistics. 22. 798–804.6 indexed citations
Blankertz, Benjamin, K. Müller, Dean J. Krusienski, et al.. (2006). The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 14(2). 153–159.694 indexed citations breakdown →
Pawelzik, Klaus, et al.. (1999). Hidden Markov mixtures of experts with an application to EEG recordings from sleep. Theory in Biosciences. 118. 246–260.16 indexed citations
14.
Onoda, Takashi, et al.. (1998). An Improvement of AdaBoost to Avoid Overfitting. International Conference on Neural Information Processing. 506–509.41 indexed citations
15.
Rätsch, Gunnar, Takashi Onoda, & K. Müller. (1998). Regularizing AdaBoost. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 11. 564–570.37 indexed citations
16.
Müller, K., Jens Kohlmorgen, & Klaus Pawelzik. (1995). Analysis of switching dynamics with competing neural networks. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 78(10). 1306–1315.26 indexed citations
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.