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
Predicting pairwise relations with neural similarity encoders
TL;DR: Similarity Encoder (SimEc) as discussed by the authors is designed to simultaneously factorize a given target matrix while also learning the mapping to project the data points' feature vectors into a similarity preserving embedding space.
Journal ArticleDOI
The Plurality of Human Brain–Computer Interfacing [Scanning the Issue]
Gernot Müller-Putz,José del R. Millán,Gerwin Schalk,Gerwin Schalk,Gerwin Schalk,Klaus-Robert Müller +5 more
TL;DR: The articles in this special issue focus on brain-computer interfacing, and features important review articles as well as some important current examples of research in this area.
Posted Content
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Felix Sattler,Jackie Ma,Patrick Wagner,Patrick Wagner,David Neumann,Markus Wenzel,Ralf Schäfer,Wojciech Samek,Klaus-Robert Müller,Thomas Wiegand,Thomas Wiegand +10 more
TL;DR: This work proposes a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected to aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.
Proceedings Article
Heterogeneous Component Analysis
TL;DR: This work proposes a new machine learning tool, heterogeneous component analysis (HCA), for feature extraction in order to better understand the factors that underlie such complex structured heterogeneous data.
Journal ArticleDOI
Neural Networks for Computational Chemistry: Pitfalls and Recommendations
TL;DR: In this paper, a small guide intends to pinpoint some neural networks pitfalls, along with corresponding solutions to successfully realize function approximation tasks in physics, chemistry or other fields in computational physics and chemistry.