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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Proceedings Article
Estimating vector fields using sparse basis field expansions
TL;DR: It is shown that significantly more precise and neurophysiologically more plausible location and shape estimates of cerebral current sources from EEG/MEG measurements become possible with the S-FLEX method when comparing to the state-of-the-art.
Journal ArticleDOI
Machine learning models for lipophilicity and their domain of applicability.
Timon Schroeter,Anton Schwaighofer,Sebastian Mika,Antonius Ter Laak,Detlev Suelzle,Ursula Ganzer,Nikolaus Heinrich,Klaus-Robert Müller +7 more
TL;DR: This study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma, and considers error bars for each method, and investigates how well they quantify the domain of applicability of each model.
Book ChapterDOI
Optimizing spectral filters for single trial EEG classification
TL;DR: This work proposes a novel spectral filter optimization algorithm for the single trial ElectroEncephaloGraphy (EEG) classification problem, and shows how a prior knowledge can drastically improve the classification or only be misleading.
Journal ArticleDOI
Decoding of top-down cognitive processing for SSVEP-controlled BMI
TL;DR: The present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.
Proceedings Article
Layer-wise analysis of deep networks with Gaussian kernels
TL;DR: This analysis uses Gaussian kernels to show empirically that deep networks build progressively better representations of the learning problem and that the best representations are obtained when the deep network discriminates only in the last layers.