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.
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The Berlin Brain-Computer Interface: Report from the Feedback Sessions
TL;DR: The Berlin Brain-Computer Interface (BBCI) uses well established motor competences in control paradigms and a machine learning approach to extract subject-specific discriminability from high-dimensional features and its adaptivity which respects the enormous inter-subject variability.
Book ChapterDOI
Identifying individual facial expressions by deconstructing a neural network
TL;DR: In this paper, the authors focus on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks and apply transfer learning with two base models to avoid overfitting.
Book ChapterDOI
Convex Cost Functions for Support Vector Regression
TL;DR: The concept of Support Vector Regression is extended and it is shown how the resulting convex constrained optimization problems can be efficiently solved by a Primal-Dual Interior Point path following method.
Journal ArticleDOI
Leaf-inspired homeostatic cellulose biosensors.
Ji-Yong Kim,Yong Ju Yun,Joshua Jeong,C.-Yoon Kim,Klaus-Robert Müller,Klaus-Robert Müller,Seong-Whan Lee +6 more
TL;DR: In this article, a mesoporous cellulose membrane transforms into homeostatic material with properties that include high ion conductivity, excellent flexibility and stability, appropriate adhesion force, and selfhealing effects when swollen in a saline solution.
Journal ArticleDOI
2009 Special Issue: Improving BCI performance by task-related trial pruning
TL;DR: A novel method is presented which aims to detect defected trials taking into account the intended task by use of Relevant Dimensionality Estimation (RDE), a new machine learning method for denoising in feature space and effectively "cleans" the training data and thus allows better BCI classification.