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Klaus-Robert Müller
Max Planck Society
610Publications
82H-index
35.5kCitations
Publications 610
Newest
#1Sebastian Lapuschkin (Heinrich Hertz Institute)H-Index: 9
#2Stephan Wäldchen (Technical University of Berlin)H-Index: 1
Last.Klaus-Robert Müller (Technical University of Berlin)H-Index: 82
view all 6 authors...
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...
#1Carmen Vidaurre (Technical University of Berlin)H-Index: 21
#2Guido Nolte (UHH: University of Hamburg)H-Index: 24
Last.Vadim V. Nikulin (HSE: National Research University – Higher School of Economics)H-Index: 29
view all 8 authors...
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...
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...
#1Felix SattlerH-Index: 2
Last.Wojciech SamekH-Index: 5
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Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularity, it has been observed that Federated Learning yields suboptimal results if the local clients' data distributions diverge. To address this issue, we present Clustered Federated Learning (CFL), a novel Federated Multi-Task Learning (FMTL) framework, which exploits geometric properties of the FL loss surface, to group t...
#1Alexander von Lühmann (BU: Boston University)
#2Zois Boukouvalas (UMD: University of Maryland, College Park)H-Index: 3
Last.Tülay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 52
view all 4 authors...
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...
#1Huziel E. SaucedaH-Index: 7
#2Stefan ChmielaH-Index: 5
Last.Alexandre TkatchenkoH-Index: 47
view all 5 authors...
Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields by discussing the hierarchical pathway from generating the dataset of reference calculations to the construction of the machine learning model, and the validation of the physics generated by the model. We will use the symmetrized gradient-domain machine learning (sGDML) framework due to its ability to reconstruct comp...
#1Philipp Jurmeister (Charité)
#2Michael Bockmayr (Humboldt University of Berlin)H-Index: 2
Last.Keno Bressem (Humboldt University of Berlin)
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Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We de...
#1Sebastian Bosse (Heinrich Hertz Institute)H-Index: 10
#2Sören Becker (Heinrich Hertz Institute)H-Index: 1
Last.Thomas Wiegand (Technical University of Berlin)H-Index: 60
view all 5 authors...
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...
#1Stefan Chmiela (Technical University of Berlin)H-Index: 5
#2Huziel E. SaucedaH-Index: 7
Last.Alexandre Tkatchenko (University of Luxembourg)H-Index: 47
view all 5 authors...
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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