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
3D High-Efficiency Video Coding for Multi-View Video and Depth Data
Klaus-Robert Müller,Heiko Schwarz,Detlev Marpe,Christian Bartnik,Sebastian Bosse,Heribert Brust,Tobias Hinz,Haricharan Lakshman,Philipp Merkle,F. H. Rhee,Gerhard Tech,Martin Winken,Thomas Wiegand +12 more
TL;DR: This paper describes an extension of the high efficiency video coding (HEVC) standard for coding of multi-view video and depth data, and develops and integrated a novel encoder control that guarantees that high quality intermediate views can be generated based on the decoded data.
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Machine Learning Force Fields
Oliver T. Unke,Stefan Chmiela,Huziel E. Sauceda,Michael Gastegger,Igor Poltavsky,Kristof T. Schütt,Alexandre Tkatchenko,Klaus-Robert Müller +7 more
TL;DR: In this article, the authors present an overview of applications of ML-based force fields and the chemical insights that can be obtained from them, and a step-by-step guide for constructing and testing them from scratch is given.
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The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
Benjamin Blankertz,Benjamin Blankertz,Michael Tangermann,Carmen Vidaurre,Siamac Fazli,Claudia Sannelli,Stefan Haufe,Cecilia Maeder,Lenny Ramsey,Lenny Ramsey,Irene Sturm,Gabriel Curio,Klaus-Robert Müller +12 more
TL;DR: Examples of novel BCI applications which provide evidence for the promising potential of BCI technology for non-medical uses are presented and distinct methodological improvements required to bring non- medical applications ofBCI technology to a diversity of layperson target groups are discussed.
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Asymptotic statistical theory of overtraining and cross-validation
Shun-ichi Amari,Noboru Murata,Klaus-Robert Müller,Klaus-Robert Müller,Michael Finke,H.H. Yang +5 more
TL;DR: A statistical theory for overtraining is proposed and it is shown that the asymptotic gain in the generalization error is small if the authors perform early stopping, even if they have access to the optimal stopping time.
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By-passing the Kohn-Sham equations with machine learning
TL;DR: In this paper, the density potential and energy density maps for test systems and various molecules are learned via examples, bypassing the need to solve the Kohn-Sham equations.