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Klaus-Robert Müller
Technical University of Berlin
676Publications
92H-index
42.3kCitations
Publications 630
Newest
#1Alexander von Lühmann (BU: Boston University)H-Index: 4
#2Xinge Li (BU: Boston University)
Last.Meryem A. Yücel (BU: Boston University)H-Index: 14
view all 5 authors...
Abstract For the robust estimation of evoked brain activity from functional Near Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals...
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Feb 7, 2020 in AAAI (National Conference on Artificial Intelligence)
#1Vignesh SrinivasanH-Index: 4
#2Klaus-Robert Müller (Technical University of Berlin)H-Index: 92
Last.Shinichi Nakajima (Technical University of Berlin)H-Index: 14
view all 4 authors...
We develop a combined machine learning (ML) and quantum mechanics approach that enables data-efficient reconstruction of flexible molecular force fields from high-level ab initio calculations, through the consideration of fundamental physical constraints. We discuss how such constraints are recovered and incorporated into ML models. Specifically, we use conservation of energy - a fundamental property of closed classical and quantum mechanical systems -- to derive an efficient gradient-domain mac...
#1Kristof T. Sch "utt (Technical University of Berlin)H-Index: 12
#2Michael Gastegger (Technical University of Berlin)H-Index: 4
Last.Reinhard J. Maurer (Warw.: University of Warwick)H-Index: 12
view all 5 authors...
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction...
7 CitationsSource
#1Sebastian Lapuschkin (Heinrich Hertz Institute)H-Index: 10
#2Stephan Wäldchen (Technical University of Berlin)H-Index: 1
Last.Klaus-Robert Müller (Technical University of Berlin)H-Index: 92
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...
37 CitationsSource
#1Bettina Mieth (Technical University of Berlin)H-Index: 1
#2James R. F. Hockley (University of Cambridge)
Last.Daniel Ziemek (Pfizer)H-Index: 12
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In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. T...
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Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the huge set of all potentially stable molecules. Recent advances in combining quantum mechanical (QM) calculations with machine learning (ML) provide powerful tools for exploring wide swaths of CCS. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and a...
#1Carmen Vidaurre (Technical University of Berlin)H-Index: 23
#2Guido Nolte (UHH: University of Hamburg)H-Index: 20
Last.Vadim V. Nikulin (HSE: National Research University – Higher School of Economics)H-Index: 31
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...
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#1Armin W. Thomas (Technical University of Berlin)H-Index: 2
#2Klaus-Robert Müller (Technical University of Berlin)H-Index: 92
Last.Wojciech Samek (Heinrich Hertz Institute)H-Index: 9
view all 3 authors...
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...
1 CitationsSource
#1Alexander von Lühmann (BU: Boston University)H-Index: 4
#2Zois Boukouvalas (UMD: University of Maryland, College Park)H-Index: 5
Last.Tulay Adali (UMBC: University of Maryland, Baltimore County)H-Index: 56
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...
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