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
More filters
Journal ArticleDOI
The need for novel informatics tools for integrating and planning research in molecular and cellular cognition
TL;DR: The development of semiautomated graphical and interactive tools to help neuroscientists and other biologists, including those working in molecular and cellular cognition, to track, map, and weight causal evidence in research papers are proposed.
Journal ArticleDOI
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
TL;DR: In this paper, the authors compare the ability of machine learning force fields for reconstructing dynamic and thermodynamic observables to MM-FFs in order to gain a qualitative understanding of the differences between the two approaches.
Graz-Brain-Computer Interface: State of Research
Guido Dornhege,José del R. Millán,Thilo Hinterberger,Dennis J. McFarland,Klaus-Robert Müller +4 more
Journal ArticleDOI
SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor
Marina M.-C. Vidovic,Nico Görnitz,Klaus-Robert Müller,Klaus-Robert Müller,Gunnar Rätsch,Marius Kloft +5 more
TL;DR: A new machine-learning methodology is presented, entitled motifPOIM, to extract the truly relevant motifs—regardless of their length and complexity—underlying the predictions of a trained SVM model, which considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem.
Journal ArticleDOI
Accurate Maximum-Margin Training for Parsing With Context-Free Grammars
TL;DR: This paper derives an extension of the well-known Cocke-Kasami-Younger algorithm used for parsing with probabilistic context-free grammars for the case of loss-augmented inference enabling an effective training in the cutting-plane approach.