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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Journal ArticleDOI
Editorial: Recent advances in brain-machine interfaces
TL;DR: This special issue is publishing multidisciplinary studies on BMI/BCI research, incorporating papers from a broad range of disciplines: invasive and noninvasive BMIs/BCIs, techniques for decoding brain-derived signals, neuroethics and applications of neuro-technology.
Journal ArticleDOI
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Huziel E. Sauceda,Huziel E. Sauceda,Valentin Vassilev-Galindo,Stefan Chmiela,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
TL;DR: In this paper, the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.
BCI2000: A General-Purpose Software Platform for BCI
Guido Dornhege,José del R. Millán,Thilo Hinterberger,Dennis J. McFarland,Klaus-Robert Müller +4 more
TL;DR: By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments and is currently being used in a variety of studies by many research groups.
Book ChapterDOI
Importance-Weighted cross-validation for covariate shift
TL;DR: This paper proposes a new method called importance-weighted cross-validation, which is still unbiased even under the covariate shift, and successfully tested on toy data and furthermore demonstrated in the brain-computer interface, where strong non-stationarity effects can be seen between calibration and feedback sessions.
Posted Content
The Clever Hans Effect in Anomaly Detection.
TL;DR: An explainable AI (XAI) procedure that can highlight the relevant features used by popular anomaly detection models of different type and points at a possible way out of the Clever Hans dilemma by allowing multiple anomaly models to mutually cancel their individual structural weaknesses to jointly produce a better and more trustworthy anomaly detector.