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
More filters
Journal ArticleDOI
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces
TL;DR: A simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier is suggested that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks.
Proceedings ArticleDOI
On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP
TL;DR: High-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components.
Proceedings Article
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt,Pieter-Jan Kindermans,Huziel E. Sauceda,Stefan Chmiela,Alexandre Tkatchenko,Klaus-Robert Müller +5 more
TL;DR: This work proposes to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid, and obtains a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.
Journal Article
A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation
TL;DR: A new efficient algorithm is presented for joint diagonalization of several matrices based on the Frobenius-norm formulation of the joint diagonalized problem, and addresses diagonalization with a general, non-orthogonal transformation.
Posted Content
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: An overview of applications of ML-FFs and the chemical insights that can be obtained from them is given, and a step-by-step guide for constructing and testing them from scratch is given.