K
Klaus-Robert Müller
Researcher at Technical University of Berlin
Publications - 799
Citations - 98394
Klaus-Robert Müller is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 129, co-authored 764 publications receiving 79391 citations. Previous affiliations of Klaus-Robert Müller include Korea University & University of Tokyo.
Papers
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Inlier-based ICA with an application to superimposed images
TL;DR: A new independent component analysis method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images and is robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers.
Proceedings Article
Regression for sets of polynomial equations
Franz J. Király,Paul von Bünau,Jan Saputra Müller,Duncan A. J. Blythe,Frank C. Meinecke,Klaus-Robert Müller +5 more
TL;DR: In this article, the authors propose a method called ideal regression for approximating an arbitrary system of polynomial equations by a system of a particular type using techniques from approximate computational algebraic geometry.
Journal ArticleDOI
2020 International brain–computer interface competition: A review
Ji-Hoon Jeong,Jeong-Hyun Cho,Young Eun Lee,Seo-Hyun Lee,Gi Hwan Shin,Young-Seok Kweon,José del R. Millán,Klaus-Robert Müller,Seong-Whan Lee +8 more
TL;DR: Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.
The IDIAP Brain-Computer Interface: An Asynchronous Multiclass Approach
Guido Dornhege,José del R. Millán,Thilo Hinterberger,Dennis J. McFarland,Klaus-Robert Müller +4 more
TL;DR: An overview of the work on a self-pace asynchronous BCI that responds every 0.5 seconds, a statistical Gaussian classifier tries to recognize three different mental tasks; it may also respond unknown for uncertain samples as the classifier has incorporated statistical rejection criteria.
Posted Content
Entropy-Constrained Training of Deep Neural Networks
TL;DR: In this paper, the authors propose a general framework for neural network compression motivated by the minimum description length (MDL) principle and derive an expression for the entropy of a neural network.