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Klaus-Robert Müller (1964-)

Physiker, Informatiker

Name	Klaus-Robert Müller
Time	1964- (Date of birth and death)
Place	Karlsruhe (Geburtsort) Deutschland
Gender	männlich
Academic degree	Dr.
Profession	Physiker Informatiker
Related	Technische Universität Berlin (Full Professor (Software Engineering and Theoretical Computer Science)) Universität Karlsruhe (TH) (Lehre) Deutsche Akademie der Naturforscher Leopoldina (Mitglied, Matrikelnummer 07476) (2012-) Maschinelles Lernen (Thema)
Source	Wikipedia: https://de.wikipedia.org/w/index.php?title=Klaus-Robert_M%C3%BCller&oldid=208138665 (Stand: 26.02.2020)
Record type	Person
GND-Identifier	1202459374
Record ID	1202459374
Involved in show all A Euclidean transformer for fast and stable machine learned force fields Erklärbare Künstliche Intelligenz in der Pathologie Erklärbare Künstliche Intelligenz in der Pathologie An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments Bispectral analysis of EEG time series data Debugging learning algorithms : understanding and correcting machine learning models Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Explainability and transparency in the realm of digital humanities: toward a historian XAI A geometrical perspective on explanations for deep neural networks Advances in hierarchical Bayesian learning with applications to neuroimaging Explainable machine learning in pathology - biomedical research and diagnostic application Machine learning for atomistic modeling: representations and thermal transport Machine learning for electronic structure Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings Polynomial-Time Constrained Message Passing for Exact MAP Inference on Discrete Models with Global Dependencies Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements Towards model-agnostic active learning in regression via identification of problem-intrinsic properties New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning Künstliche Intelligenz als Lösung des PathologInnenmangels? An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning Künstliche Intelligenz als Lösung des PathologInnenmangels? 2020 International brain–computer interface competition: A review Advancing computer vision algorithms to overcome challenges in computational pathology BIGDML - Towards accurate quantum machine learning force fields for materials DNA methylation-based classification of sinonasal tumors Explainable structured machine learning : insights into similarity, graph and transformer models Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks Higher order stationary subspace analysis Inverse design of 3d molecular structures with conditional generative neural networks Machine learning of molecular electronic properties in chemical compound space Materialization and reuse optimizations for machine learning workloads New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain Scrutinizing XAI using linear ground-truth data with suppressor variables SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects To pretrain or not? A systematic analysis of the benefits of pretraining in diabetic retinopathy Applications of machine learning in dynamic settings : from computational neuroscience to natural language processing Combining machine learning and computational chemistry for predictive insights into chemical systems Deep one-class learning: a deep learning approach to anomaly detection Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature Machine learning force fields Machine learning of solvent effects on molecular spectra and reactions Sensorimotor functional connectivity: A neurophysiological factor related to BCI performance A convolutional neural network for steady state visual evoked potential classification under ambulatory environment A Multi-Class Support Vector Machine Based on Scatter Criteria Autonomous robotic nanofabrication with reinforcement learning Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review Brain–computer interfacing under distraction: an evaluation study Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment Development of model observers for quantitative assessment of mammography image quality Immediate brain plasticity after one hour of brain–computer interface (BCI) Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields Quantum-Chemical Insights from Interpretable Atomistic Neural Networks Similarity encoder: A neural network architecture for learning similarity preserving embeddings Towards more efficient and performant computations in quantum chemistry with machine learning Unmasking Clever Hans predictors and assessing what machines really learn

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Katalog der Deutschen Nationalbibliothek

Klaus-Robert Müller (1964-)

A Euclidean transformer for fast and stable machine learned force fields

Erklärbare Künstliche Intelligenz in der Pathologie

Erklärbare Künstliche Intelligenz in der Pathologie

An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

Bispectral analysis of EEG time series data

Debugging learning algorithms : understanding and correcting machine learning models

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces

Explainability and transparency in the realm of digital humanities: toward a historian XAI

A geometrical perspective on explanations for deep neural networks

Advances in hierarchical Bayesian learning with applications to neuroimaging

Explainable machine learning in pathology - biomedical research and diagnostic application

Machine learning for atomistic modeling: representations and thermal transport

Machine learning for electronic structure

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings

Polynomial-Time Constrained Message Passing for Exact MAP Inference on Discrete Models with Global Dependencies

Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements

Towards model-agnostic active learning in regression via identification of problem-intrinsic properties

New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning

Künstliche Intelligenz als Lösung des PathologInnenmangels?

An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning

Künstliche Intelligenz als Lösung des PathologInnenmangels?

2020 International brain–computer interface competition: A review

Advancing computer vision algorithms to overcome challenges in computational pathology

BIGDML - Towards accurate quantum machine learning force fields for materials

DNA methylation-based classification of sinonasal tumors

Explainable structured machine learning : insights into similarity, graph and transformer models

Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data

High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks

Higher order stationary subspace analysis

Inverse design of 3d molecular structures with conditional generative neural networks

Machine learning of molecular electronic properties in chemical compound space

Materialization and reuse optimizations for machine learning workloads

New machine learning methods for modeling nonlinear interactions in neural data : towards separation and detection of genuine cross-frequency synchronized sources

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Scrutinizing XAI using linear ground-truth data with suppressor variables

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

To pretrain or not? A systematic analysis of the benefits of pretraining in diabetic retinopathy

Applications of machine learning in dynamic settings : from computational neuroscience to natural language processing

Combining machine learning and computational chemistry for predictive insights into chemical systems

Deep one-class learning: a deep learning approach to anomaly detection

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

Machine learning force fields

Machine learning of solvent effects on molecular spectra and reactions

Sensorimotor functional connectivity: A neurophysiological factor related to BCI performance

A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

A Multi-Class Support Vector Machine Based on Scatter Criteria

Autonomous robotic nanofabrication with reinforcement learning

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

Brain–computer interfacing under distraction: an evaluation study

Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment

Development of model observers for quantitative assessment of mammography image quality

Immediate brain plasticity after one hour of brain–computer interface (BCI)

Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual

ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines

Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Similarity encoder: A neural network architecture for learning similarity preserving embeddings

Towards more efficient and performant computations in quantum chemistry with machine learning

Unmasking Clever Hans predictors and assessing what machines really learn