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 ArticleDOI
Robust common spatial patterns based on Bhattacharyya distance and Gamma divergence
TL;DR: This paper integrates two additional divergence measures, namely Bhattacharyya distance and Gamma divergence, into the divergence-based CSP framework and evaluates their robustness using simulations and data set IVa from BCI Competition III.
Journal ArticleDOI
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective
Simon Letzgus,Patrick Wagner,Jonas Lederer,Wojciech Samek,Klaus-Robert Müller,Grégoire Montavon +5 more
TL;DR: This review clarifies the fundamental conceptual differences of XAI for regression and classification tasks, establishes novel theoretical insights and analysis for XAIR, provides demonstrations of XAIR on genuine practical regression problems, and discusses challenges remaining for the field.
Proceedings Article
Minimizing trust leaks for robust Sybil detection
TL;DR: This paper proposes transductive Sybil ranking (TSR), a robust extension to SybilRank and Integro that directly minimizes trust leaks and shows significant advantages over stateof-the-art competitors on a variety of attacking scenarios on artificially generated data and realworld datasets.
Journal ArticleDOI
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Alexander Binder,Shinichi Nakajima,Marius Kloft,Christina Müller,Wojciech Samek,Ulf Brefeld,Klaus-Robert Müller,Motoaki Kawanabe +7 more
TL;DR: A recently developed non-sparse MKL variant is applied to state-of-the-art concept recognition tasks from the application domain of computer vision and compared against its direct competitors, the sum-kernel SVM and sparse MKL.
Book ChapterDOI
Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
TL;DR: The symmetric GDML (sGDML) approach is able to faithfully reproduce global force fields at the accuracy high-level ab initio methods, thus enabling sample intensive tasks like molecular dynamics simulations at that level of accuracy.