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Klaus-Robert Müller
Max Planck Society
634Publications
82H-index
35.4kCitations
Publications 634
Newest
Published on Mar 11, 2019in Nature Communications 11.88
Sebastian Lapuschkin7
Estimated H-index: 7
(Heinrich Hertz Institute),
Stephan Wäldchen1
Estimated H-index: 1
(Technical University of Berlin)
+ 3 AuthorsKlaus-Robert Müller82
Estimated H-index: 82
(Technical University of Berlin)
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be obli...
Published on Jul 1, 2019in NeuroImage 5.81
Carmen Vidaurre21
Estimated H-index: 21
(Technical University of Berlin),
Guido Nolte24
Estimated H-index: 24
(UHH: University of Hamburg)
+ 5 AuthorsVadim V. Nikulin28
Estimated H-index: 28
(HSE: National Research University – Higher School of Economics)
Abstract Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hypersca...
Published on Jun 1, 2019in NeuroImage 5.81
Alexander von Lühmann (BU: Boston University), Zois Boukouvalas3
Estimated H-index: 3
(UMD: University of Maryland, College Park)
+ 1 AuthorsTülay Adali52
Estimated H-index: 52
(UMBC: University of Maryland, Baltimore County)
Abstract In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statisti...
Published on Jun 1, 2019in NeuroImage 5.81
Carmen Vidaurre21
Estimated H-index: 21
(Technical University of Berlin),
A. Ramos Murguialday1
Estimated H-index: 1
(University of Tübingen)
+ 3 AuthorsVadim V. Nikulin28
Estimated H-index: 28
(Charité)
Abstract An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants ...
Miriam Hägele2
Estimated H-index: 2
,
Philipp Seegerer2
Estimated H-index: 2
+ -3 AuthorsAlexander Binder11
Estimated H-index: 11
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, explanation methods have emerged, which are so far still rarely used in medicine. This work shows their application to generate heatmaps that allow to resolve common challenges encountered in deep learning-based digital histopathology analyses. Th...
Published in arXiv: Learning
Danny Panknin2
Estimated H-index: 2
(Technical University of Berlin),
Shinichi Nakajima13
Estimated H-index: 13
+ -3 AuthorsKlaus-Robert Müller82
Estimated H-index: 82
Real world data often exhibit inhomogeneity, e.g., skewed distribution, non-uniform complexity of the target function and uneven noise level over the input space. In this paper, we cope with inhomogeneity by explicitly estimating the locally optimal kernel bandwidth as a function. Specifically, we propose Spatially Adaptive Bandwidth Estimation in Regression (SABER), which employs the mixture of experts consisting of multinomial kernel logistic regression as a gate and Gaussian process regressio...
Published on Dec 1, 2018in Digital Signal Processing 2.79
Sebastian Bosse10
Estimated H-index: 10
(Heinrich Hertz Institute),
Sören Becker1
Estimated H-index: 1
(Heinrich Hertz Institute)
+ 2 AuthorsThomas Wiegand59
Estimated H-index: 59
(Technical University of Berlin)
Abstract The PSNR and MSE are the computationally simplest and thus most widely used measures for image quality, although they correlate only poorly with perceived visual quality. More accurate quality models that rely on processing on both the reference and distorted image are potentially difficult to integrate in time-critical communication systems where computational complexity is disadvantageous. This paper derives the concept of distortion sensitivity as a property of the reference image th...
Published on Jul 2, 2019in arXiv: Image and Video Processing
Armin W. Thomas1
Estimated H-index: 1
,
Klaus-Robert Müller82
Estimated H-index: 82
,
Wojciech Samek5
Estimated H-index: 5
The application of deep learning (DL) models to the decoding of cognitive states from whole-brain functional Magnetic Resonance Imaging (fMRI) data is often hindered by the small sample size and high dimensionality of these datasets. Especially, in clinical settings, where patient data are scarce. In this work, we demonstrate that transfer learning represents a solution to this problem. Particularly, we show that a DL model, which has been previously trained on a large openly available fMRI data...
Published on Feb 1, 2019in Computer Physics Communications 3.31
Stefan Chmiela4
Estimated H-index: 4
(Technical University of Berlin),
Huziel E. Sauceda7
Estimated H-index: 7
+ 2 AuthorsAlexandre Tkatchenko47
Estimated H-index: 47
(University of Luxembourg)
Abstract We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowl...
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