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
Robustly estimating the flow direction of information in complex physical systems.
Guido Nolte,Andreas Ziehe,Vadim V. Nikulin,Alois Schlögl,Nicole C. Krämer,Tom Brismar,Klaus-Robert Müller +6 more
TL;DR: A new measure (phase-slope index) to estimate the direction of information flux in multivariate time series is proposed that is insensitive to mixtures of independent sources, gives meaningful results even if the phase spectrum is not linear, and properly weights contributions from different frequencies.
Proceedings Article
Classifying Single Trial EEG: Towards Brain Computer Interfacing
TL;DR: This work detects upcoming finger movements in a natural keyboard typing condition and predicts their laterality in a pseudo-online simulation, and compares discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable regularization properties for dealing with high noise cases.
Journal ArticleDOI
Machine Learning of Molecular Electronic Properties in Chemical Compound Space
Grégoire Montavon,Matthias Rupp,Vivekanand V. Gobre,Álvaro Vázquez-Mayagoitia,Katja Hansen,Alexandre Tkatchenko,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,O. Anatole von Lilienfeld +9 more
TL;DR: In this article, a deep multi-task artificial neural network is used to predict multiple electronic ground and excited-state properties, such as atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies.
Proceedings ArticleDOI
Multi-View Video Plus Depth Representation and Coding
TL;DR: The impact on image quality of rendered arbitrary intermediate views is investigated and analyzed in a second part, comparing compressed multi-view video plus depth data at different bit rates with the uncompressed original.
Journal ArticleDOI
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
TL;DR: A deep neural network-based approach to image quality assessment (IQA) that allows for joint learning of local quality and local weights in an unified framework and shows a high ability to generalize between different databases, indicating a high robustness of the learned features.