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
Explaining nonlinear classification decisions with deep Taylor decomposition
TL;DR: A novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements by backpropagating the explanations from the output to the input layer is introduced.
Journal ArticleDOI
SchNet - A deep learning architecture for molecules and materials.
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Alexandre Tkatchenko,Klaus-Robert Müller +4 more
TL;DR: SchNet as mentioned in this paper is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, where the model learns chemically plausible embeddings of atom types across the periodic table.
Journal ArticleDOI
Single-Trial Analysis and Classification of ERP Components - a Tutorial
TL;DR: This tutorial proposes to use shrinkage estimators and shows that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification.
Proceedings Article
Kernel PCA and De-Noising in Feature Spaces
Sebastian Mika,Bernhard Schölkopf,Alexander J. Smola,Klaus-Robert Müller,Matthias Scholz,Gunnar Rätsch +5 more
TL;DR: This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre- images in data reconstruction and de-noising on toy examples as well as on real world data.
Book ChapterDOI
Predicting Time Series with Support Vector Machines
Klaus-Robert Müller,Alexander J. Smola,Gunnar Rätsch,Bernhard Schölkopf,Jens Kohlmorgen,Vladimir Vapnik +5 more
TL;DR: Two different cost functions for Support Vectors are made use: training with an e insensitive loss and Huber's robust loss function and how to choose the regularization parameters in these models are discussed.