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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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Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions

Kristof T. Schütt, +4 more
- 24 Jun 2019 - 
TL;DR: In this paper, a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived.
Posted ContentDOI

DeepCOMBI: Explainable artificial intelligence for the analysis and discovery in genome-wide association studies

Bettina Mieth, +7 more
- 08 Nov 2020 - 
TL;DR: The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 WTCCC study and it is shown to outperform ordinary raw p-value thresholding as well as other baseline methods.
Journal ArticleDOI

On robust parameter estimation in brain–computer interfacing

Wojciech Samek, +5 more
- 23 Nov 2017 - 
TL;DR: The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters, and a novel hierarchical view on robustness is introduced which naturally comprises different types of outlierness occurring in structured data.
Posted ContentDOI

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Ali Hashemi, +7 more
- 10 Aug 2020 - 
TL;DR: This paper proposes a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio settings and shows neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.
Proceedings Article

v-Arc: Ensemble Learning in the Presence of Outliers

Gunnar Rätsch, +5 more
TL;DR: A new boosting algorithm is proposed which allows for the possibility of a pre-specified fraction of points to lie in the margin area, even on the wrong side of the decision boundary.