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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Book ChapterDOI
A note on the Berlin Brain-Computer Interface
Klaus-Robert Müller,Matthias Krauledat,G. Dornhege,G. Dornhege,Stefan Jähnichen,Stefan Jähnichen,Gabriel Curio,Gabriel Curio,Benjamin Blankertz,Benjamin Blankertz +9 more
TL;DR: A particular focus is placed on linear classification methods which can be applied in the BCI context and an overview on the Berlin-Brain Computer Interface (BBCI) is provided.
Book ChapterDOI
Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches
TL;DR: Chmiela et al. as mentioned in this paper developed a combined machine learning and quantum mechanics approach that enables data-efficient reconstruction of flexible molecular force fields from high-level ab initio calculations, through the consideration of fundamental physical constraints.
Journal IssueDOI
Securing IMS against novel threats
TL;DR: This work proposes an architecture for an autonomous and self-sufficient monitoring and protection system for devices and infrastructure inspired by network intrusion detection techniques, and proposes a signature-less detection of abnormal events and zero-day attacks.
Book ChapterDOI
Using Rest Class and Control Paradigms for Brain Computer Interfacing
TL;DR: The effectiveness of introducing an intermediary state between state probabilities and interface command, driven by a dynamic control law, is investigated and the strategies used by 2 subjects to achieve idle state BCI control are outlined.
Posted Content
Exploring text datasets by visualizing relevant words.
TL;DR: This paper compares three methods for extracting relevant words from a collection of texts and demonstrates the usefulness of the resulting word clouds by providing an overview of the classes contained in a dataset of scientific publications as well as by discovering trending topics from recent New York Times article snippets.