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.
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Journal ArticleDOI
Kernel-based nonlinear blind source separation
TL;DR: In this paper, a kernel-based algorithm for nonlinear blind source separation (BSS) with temporal information is proposed. But this algorithm requires the data to be mapped to a high (possibly infinite)-dimensional kernel feature space.
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.
John A. Keith,Valentin Vassilev-Galindo,Bingqing Cheng,Stefan Chmiela,Michael Gastegger,Klaus-Robert Müller,Alexandre Tkatchenko +6 more
TL;DR: In this paper, the authors provide a review of the applications of computational chemistry and machine learning in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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A New Discriminative Kernel From Probabilistic Models
TL;DR: This work proposes a new discriminative TOP kernel derived from tangent vectors of posterior log-odds and develops a theoretical framework on feature extractors from probabilistic models and uses it for analyzing the TOP kernel.
Proceedings ArticleDOI
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
TL;DR: This paper extends the LRP framework for Layer-wise Relevance Propagation for Fisher vector classifiers and uses it as analysis tool to quantify the importance of context for classification, qualitatively compare DNNs against FV classifiers in terms of important image regions and detect potential flaws and biases in data.
Journal ArticleDOI
Analysis of Multimodal Neuroimaging Data
TL;DR: A comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.