K. Müller

发表

Efficient BackProp

Klaus-Robert Müller, Yann LeCun, Léon Bottou, 2012, Neural Networks: Tricks of the Trade.

Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1998, Neural Computation.

Input space versus feature space in kernel-based methods

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, IEEE Trans. Neural Networks.

Fast and accurate modeling of molecular atomization energies with machine learning.

K. Müller, M. Rupp, A. Tkatchenko, 2011, Physical review letters.

Blind decomposition of multimodal evoked responses and DC fields

E. Oja, K. Müller, R. Vigário, 2003 .

Asymptotic statistical theory of overtraining and cross-validation

Klaus-Robert Müller, Shun-ichi Amari, Noboru Murata, 1997, IEEE Trans. Neural Networks.

Adaptive On-line Learning in Changing Environments

Andreas Ziehe, Klaus-Robert Müller, Shun-ichi Amari, 1996, NIPS.

LARGE SCALE SIMULATIONS FOR LEARNING CURVES

S. Amari, K. Müller, N. Murata, 2022 .

Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

K. Müller, Daniel Bartz, K. Hatrick, 2013, PloS one.

How to Explain Individual Classification Decisions pdf

Motoaki Kawanabe, Klaus-Robert Müller, Stefan Harmeling, 2009, J. Mach. Learn. Res..

Finding stationary subspaces in multivariate time series.

K. Müller, P. von Bünau, F. Meinecke, 2009, Physical review letters.

Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models

K. Müller, S. Mika, Anton Schwaighofer, 2007, ChemMedChem.

LRP Toolbox for Artificial Neural Networks 1.2.0 – Manual

K. Müller, Alexander Binder, G. Montavon, 2016 .

Generalization Error Estimation under Covariate Shift

K. Müller, Masashi Sugiyama, 2005 .

Deep Boltzmann Machines and the Centering Trick

Klaus-Robert Müller, Grégoire Montavon, K. Müller, 2012, Neural Networks: Tricks of the Trade.

Predicting Time Series with Support Vector Machines

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1997, ICANN.

Advances in neural information processing systems : proceedings of the ... conference

Thomas G. Dietterich, Richard P. Lippmann, Klaus-Robert Müller, 1989 .

Covariate Shift Adaptation by Importance Weighted Cross Validation

Klaus-Robert Müller, Masashi Sugiyama, Matthias Krauledat, 2007, J. Mach. Learn. Res..

The connection between regularization operators and support vector kernels

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1998, Neural Networks.

General cost functions for support vector regression.

B. Schölkopf, K. Müller, Alex Smola, 1998 .

Classification on proximity data with LP-machines

R. C. Williamson, B. Schölkopf, K. Müller, 1999 .

On Taxonomies for Multi-class Image Categorization

Motoaki Kawanabe, Alexander Binder, Klaus-Robert Müller, 2012, International Journal of Computer Vision.

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

TDSEP — an efficient algorithm for blind separation using time structure

Andreas Ziehe, Klaus-Robert Müller, K. Müller, 1998 .

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

K. Müller, A. Tkatchenko, O. A. von Lilienfeld, 2015, The journal of physical chemistry letters.

Asymptotically Optimal Choice of ε-Loss for Support Vector Machines

B. Schölkopf, K. Müller, Alex Smola, 1998 .

Layer-wise analysis of deep networks with Gaussian kernels

Klaus-Robert Müller, Grégoire Montavon, Mikio L. Braun, 2010, NIPS.

Kernel Principal Component Analysis

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1997, ICANN.

Learning how to explain neural networks: PatternNet and PatternAttribution

Klaus-Robert Müller, Dumitru Erhan, Been Kim, 2017, ICLR.

Soft Margins for AdaBoost

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 2001, Machine Learning.

Kernel Analysis of Deep Networks

Klaus-Robert Müller, Grégoire Montavon, Mikio L. Braun, 2011, J. Mach. Learn. Res..

Kernel PCA and De-Noising in Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998, NIPS.

The IDIAP Brain-Computer Interface: An Asynchronous Multiclass Approach

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

BCI2000: A General-Purpose Software Platform for BCI

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond

Febo Cincotti, José del R. Millán, Donatella Mattia, 2015, Proceedings of the IEEE.

Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes

K. Müller, B. Blankertz, S. Fazli, 2007, PloS one.

Single-trial analysis of the neural correlates of speech quality perception

K. Müller, G. Curio, R. Schleicher, 2013, Journal of neural engineering.

Optimal dyadic decision trees

Klaus-Robert Müller, Gilles Blanchard, Christin Schäfer, 2007, Machine Learning.

Efficient and Accurate Lp-Norm Multiple Kernel Learning

Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg, 2009, NIPS.

Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing

Motoaki Kawanabe, Klaus-Robert Müller, Benjamin Blankertz, 2007, NIPS.

Spatio-Spectral Filters for Robust Classification of Single Trial EEG

K. Müller, G. Curio, S. Lemm, 2022 .

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2015, PloS one.

Machine learning models for lipophilicity and their domain of applicability.

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, Molecular pharmaceutics.

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

Klaus-Robert Müller, Sebastian Mika, Thomas Steger-Hartmann, 2009, J. Chem. Inf. Model..

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, J. Comput. Aided Mol. Des..

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, J. Chem. Inf. Model..

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Wojciech Samek, Lars Kai Hansen, Andrea Vedaldi, 2019, Explainable AI.

Towards exact molecular dynamics simulations with machine-learned force fields

K. Müller, A. Tkatchenko, Stefan Chmiela, 2018, Nature Communications.

Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective?

Klaus-Robert Müller, Shun-ichi Amari, Noboru Murata, 1995, NIPS.

On Relevant Dimensions in Kernel Feature Spaces

Joachim M. Buhmann, Klaus-Robert Müller, Mikio L. Braun, 2008, J. Mach. Learn. Res..

Predicting BCI performance to study BCI illiteracy

K. Müller, G. Curio, T. Dickhaus, 2009, BMC Neuroscience.

A Numerical Study on Learning Curves in Stochastic Multilayer Feedforward Networks

Klaus Schulten, Klaus-Robert Müller, Shun-ichi Amari, 1996, Neural Computation.

A Probabilistic Approach to Classifying Metabolic Stability

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2008, J. Chem. Inf. Model..

Artifact Reduction in Magnetoneurography Based on Time-Delayed Second Order Correlations

Andreas Ziehe, Klaus-Robert Müller, Gabriel Curio, 1998 .

Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2004, Neural Computation.

Asymptotically optimal choice of varepsilon-loss for support vector machines.

Alexander J. Smola, Tom Ziemke, Mikael Bodén, 1998 .

Model Selection Under Covariate Shift

Klaus-Robert Müller, Masashi Sugiyama, K. Müller, 2005, ICANN.

Nonlinear blind source separation using kernel feature spaces

Terrence J. Sejnowski, Lee, Tzyy-Ping Jung, 2001 .

Separation of post-nonlinear mixtures using ACE and temporal decorrelation

T. Sejnowski, K. Müller, S. Makeig, 2001 .

Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2004, IEEE Transactions on Biomedical Engineering.

Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics

Klaus-Robert Müller, Klaus Pawelzik, Jens Kohlmorgen, 1996, Neural Computation.

On-line learning in changing environments with applications in supervised and unsupervised learning

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2002, Neural Networks.

The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials

Klaus-Robert Müller, Christa Neuper, Gert Pfurtscheller, 2004, IEEE Transactions on Biomedical Engineering.

Effiicient BackProp

Yann LeCun, Klaus-Robert Müller, Genevieve B. Orr, 1996, Neural Networks: Tricks of the Trade.

Quantum-chemical insights from deep tensor neural networks

Alexandre Tkatchenko, Farhad Arbabzadah, Kristof T. Schütt, 2016, Nature Communications.

Kernel PCA pattern reconstruction via approximate pre-images.

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998 .

Kernel Feature Spaces and Nonlinear Blind Souce Separation

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2001, NIPS.

A New Discriminative Kernel from Probabilistic Models

Gunnar Rätsch, Motoaki Kawanabe, Klaus-Robert Müller, 2001, Neural Computation.

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Kristof T. Schütt, K. Müller, A. Tkatchenko, 2019, Nature Communications.

A comparison of ICA-based artifact reduction methods for MEG

K. Müller, A. Ziehe, T. Sander, 2000 .

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2006, PLoS Comput. Biol..

Pyff – A Pythonic Framework for Feedback Applications and Stimulus Presentation in Neuroscience

Klaus-Robert Müller, John Williamson, Benjamin Blankertz, 2010, Front. Neurosci..

Comparison of Granger Causality and Phase Slope Index

Andreas Ziehe, Klaus-Robert Müller, Nicole Krämer, 2008, NIPS Causality: Objectives and Assessment.

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2016, Journal of neural engineering.

Using Rest Class and Control Paradigms for Brain Computer Interfacing

Klaus-Robert Müller, Benjamin Blankertz, Siamac Fazli, 2009, IWANN.

Language Detection For Short Text Messages In Social Media pdf

Klaus-Robert Müller, Ivana Balazevic, Mikio L. Braun, 2016, ArXiv.

A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy

Klaus-Robert Müller, Alexander von Lühmann, Tülay Adali, 2019, NeuroImage.

The Stationary Subspace Analysis Toolbox

Franz J. Király, Klaus-Robert Müller, Frank C. Meinecke, 2011, J. Mach. Learn. Res..

Localizing and Estimating Causal Relations of Interacting Brain Rhythms

Klaus-Robert Müller, Guido Nolte, K. Müller, 2010, Front. Hum. Neurosci..

Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

Klaus-Robert Müller, Aapo Hyvärinen, Jukka-Pekka Kauppi, 2015, PloS one.

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

Klaus-Robert Müller, Seong-Whan Lee, No-Sang Kwak, 2017, PloS one.

Effect of higher frequency on the classification of steady-state visual evoked potentials

K. Müller, Seong-Whan Lee, Sven Dähne, 2016, Journal of neural engineering.

Regularizing AdaBoost

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 1998, NIPS.

Stationary common spatial patterns for brain–computer interfacing

Motoaki Kawanabe, Klaus-Robert Müller, Wojciech Samek, 2012, Journal of neural engineering.

Validity of Time Reversal for Testing Granger Causality

Stefan Haufe, Klaus-Robert Müller, Daniel Bartz, 2015, IEEE Transactions on Signal Processing.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

Klaus-Robert Müller, Siamac Fazli, Grégoire Montavon, 2013, Journal of chemical theory and computation.

Ruppet al.Reply

K. Müller, M. Rupp, A. Tkatchenko, 2012 .

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles pdf

Masaru Ishii, Alexander Binder, Klaus-Robert Müller, 2018, ArXiv.

Electrocorticogram as a Brain-Computer Interface Signal Source

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2008, Journal of Neuroscience Methods.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Klaus-Robert Müller, Michael Tangermann, Stefan Debener, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

BioSig: An Open-Source Software Library for BCI Research

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Invariant Feature Extraction and Classification in Kernel Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, NIPS.

General Signal Processing and Machine Learning Tools for BCI Analysis

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Kernels, Pre-images and Optimization

Klaus-Robert Müller, Sebastian Mika, John C. Snyder, 2013, Empirical Inference.

Orbital-free bond breaking via machine learning.

John C. Snyder, K. Müller, M. Rupp, 2013, The Journal of chemical physics.

Logistic Regression for Single Trial EEG Classification

Kazuyuki Aihara, Klaus-Robert Müller, Ryota Tomioka, 2006, NIPS.

Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms

Gunnar Rätsch, Klaus-Robert Müller, Marius Kloft, 2015, ECML/PKDD.

Identifying Individual Facial Expressions by Deconstructing a Neural Network

Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, 2016, GCPR.

Classifying 'Drug-likeness' with Kernel-Based Learning Methods

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2005, J. Chem. Inf. Model..

Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines

Klaus-Robert Müller, Christin Schäfer, Igor Kotenko, 2004 .

Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces

Klaus-Robert Müller, Wojciech Samek, Siamac Fazli, 2015, Proceedings of the IEEE.

Common Spatial Pattern Patches-an Optimized Filter Ensemble for Adaptive BCIs

K. Müller, B. Blankertz, C. Vidaurre, 2011 .

Machine learning of accurate energy-conserving molecular force fields

Klaus-Robert Müller, Alexandre Tkatchenko, Igor Poltavsky, 2016, Science Advances.

On-line learning in switching and drifting environments with application to blind source separation

Andreas Ziehe, Klaus-Robert Müller, Shun-ichi Amari, 1999 .

Learning Invariant Representations of Molecules for Atomization Energy Prediction

Andreas Ziehe, Klaus-Robert Müller, Siamac Fazli, 2012, NIPS.

Brain-computer interfacing in discriminative and stationary subspaces

Motoaki Kawanabe, Klaus-Robert Müller, Wojciech Samek, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions

K. Müller, Seong-Whan Lee, Dong-Ok Won, 2020, Science Robotics.

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces.

Klaus-Robert Müller, Alexandre Tkatchenko, Igor Poltavsky, 2019, The Journal of chemical physics.

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

Klaus-Robert Müller, Alexandre Tkatchenko, Wiktor Pronobis, 2018, Journal of chemical theory and computation.

Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers

Klaus-Robert Müller, Grégoire Montavon, Daniel Heim, 2018, Genome Medicine.

Explaining the Decisions of Convolutional and Recurrent Neural Networks

K. Müller, G. Montavon, W. Samek, 2021 .

Estimating Noise and Dimensionality in BCI Data Sets : Towards Illiteracy Comprehension

Klaus-Robert Müller, Michael Tangermann, Claudia Sannelli, 2008 .

Analyzing and Validating Neural Networks Predictions

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2016 .

Psychological predictors of SMR-BCI performance

K. Müller, T. Dickhaus, A. Kübler, 2012, Biological Psychology.

Neural Networks for Computational Chemistry: Pitfalls and Recommendations

K. Müller, G. Montavon, 2013 .

Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Wojciech Samek, Klaus-Robert Müller, Felix Sattler, 2019, IEEE Transactions on Neural Networks and Learning Systems.

1 Efficient BackProp

L. Bottou, Yann LeCun, G. Orr, 2012 .

Robustly estimating the flow direction of information in complex physical systems.

K. Müller, A. Schlögl, A. Ziehe, 2007, Physical review letters.

Defense Against Adversarial Attacks by Langevin Dynamics

K. Müller, W. Samek, Shinichi Nakajima, 2019 .

Quantum chemical accuracy from density functional approximations via machine learning

K. Müller, K. Burke, M. Tuckerman, 2020, Nature Communications.

The Berlin Brain-Computer Interface ( BBCI ) towards a new communication channel for online control of multimedia applications and computer games

K. Müller, G. Curio, B. Blankertz, 2003 .

How to iNNvestigate neural network’s predictions!

Kristof T. Schütt, K. Müller, Pieter-Jan Kindermans, 2018 .

Interaction Potentials in Molecules and Non-Local Information in Chemical Space

Klaus-Robert Müller, Alexandre Tkatchenko, Franziska Biegler, 2014 .

The need for novel informatics tools for integrating and planning research in molecular and cellular cognition

Klaus-Robert Müller, Alcino J. Silva, Alcino J Silva, 2015, Learning & memory.

A novel mechanism for evoked responses in the human brain

Steven Lemm, Gabriel Curio, Guido Nolte, 2007, The European journal of neuroscience.

Assessing brain effective connectivity from EEG – a simulation study

K. Müller, G. Nolte, V. Nikulin, 2012 .

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol

Seong-Whan Lee, Klaus-Robert Müller, Seul-Ki Yeom, 2017, PloS one.

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery

B. Blankertz, N. Birbaumer, K. Müller, 2013, Clinical Neurophysiology.

Asymptotic Properties of the Fisher Kernel

Motoaki Kawanabe, Klaus-Robert Müller, Shotaro Akaho, 2004, Neural Computation.

From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ

Klaus-Robert Müller, Gisbert Schneider, Matthias Rupp, 2010, ChemMedChem.

PREDICTION OF FINANCIAL DATA WITH HIDDEN MARKOV MIXTURES OF EXPERTS

Klaus-Robert Müller, Steven Lemm, Klaus Pawelzik, 2000 .

A new algorithm of non-Gaussian component analysis with radial kernel functions

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2007 .

A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2019 .

BRAIN-COMPUTER INTERFACES TRANSLATE BRAIN SIGNALS INTO CONTROL COMMANDS.

Klaus-Robert Müller, Benjamin Blankertz, K. Müller, 2006 .

Improving Network Models and Algorithmic Tricks: Preface

Klaus-Robert Müller, Genevieve B. Orr, G. Orr, 1996, Neural Networks: Tricks of the Trade.

Learning from label proportions in brain-computer interfaces

Klaus-Robert Müller, Michael Tangermann, Konstantin Schmid, 2017 .

Modeling Molecular Spectra with Interpretable Atomistic Neural Networks

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2019 .

Regularization Techniques to Improve Generalization: Preface

Klaus-Robert Müller, Genevieve B. Orr, G. Orr, 1996, Neural Networks.

Scrutinizing XAI using linear ground-truth data with suppressor variables pdf

Stefan Haufe, Klaus-Robert Müller, Rick Wilming, 2021, Machine Learning.

Dynamics of supervised learning with restricted training sets and noisy teachers

Klaus-Robert Müller, Sara A. Solla, T. K. Leen, 2000, NIPS 2000.

Faculty Opinions recommendation of EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy.

Klaus-Robert Müller, K. Müller, 2019, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Towards robust machine learning methods for the analysis of brain data

K. Müller, 2018, 2018 6th International Conference on Brain-Computer Interface (BCI).

From measurement to machine learning: Towards analysing cognition

K. Müller, 2017, Balkan Conference in Informatics.

Detecting resting-state networks using scalable multi-subject spatial canonical correlation analysis

Daniel S. Margulies, Julia M. Huntenburg, Janis Reinelt, 2016 .

Optimizing Visual Vocabularies for Image Classification

K. Müller, H. Burkhard, W. Wojcikiewicz, 2009 .

Directional Variance Adjustment: a novel covariance estimator for high dimensional portfolio optimization pdf

Klaus-Robert Müller, Steven Lemm, Daniel Bartz, 2011, arXiv.org.

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning pdf

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2019, Pattern Recognit..

Speeding Learning

Klaus-Robert Müller, K. Müller, 2012, Neural Networks: Tricks of the Trade.

Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop

Klaus-Robert Müller, Genevieve Orr, G. Orr, 1998, NIPS 1998.

Speeding Learning: Preface

Klaus-Robert Müller, Genevieve B. Orr, G. Orr, 1996, Neural Networks.

Faculty Opinions recommendation of VAMPnets for deep learning of molecular kinetics.

Klaus-Robert Müller, K. Müller, 2018, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

Improving Classification Performance of a brain-Computer Interface System based on Rapid Serial Visual Presentation by Shifting stimuli

Klaus-Robert Müller, Seong-Whan Lee, Han-Jeong Hwang, 2017, GBCIC.

Das Berliner Brain-Computer Interface

Klaus-Robert Müller, K. Müller, 2006, Künstliche Intell..

Advances in Cognitive Neural Prosthesis: Recognition of Neural Data with an Information-Theoretic Objective

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Proceedings of the 28th conference on Pattern Recognition

Klaus-Robert Müller, Katrin Franke, Bertram Nickolay, 2006 .

Machine Learning Force Fields

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2020, Chemical reviews.

Statistics meets Machine Learning

Klaus-Robert Müller, Lutz Dümbgen, Fadoua Balabdaoui, 2021 .

BCI Applications for the General Population

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2012 .

Graz-Brain-Computer Interface: State of Research

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Tricks for Time Series: Preface

Klaus-Robert Müller, Genevieve B. Orr, G. Orr, 1996, Neural Networks: Tricks of the Trade.

Proceedings of the 12th International Conference on Neural Information Processing Systems

Klaus-Robert Müller, Sara A. Solla, Sara A. Solla, 1999 .

Toward Brain-Computer Interfacing (Neural Information Processing)

Terrence J. Sejnowski, José del R. Millán, Klaus-Robert Müller, 2007 .

A Minimax Approach to Optimize Spatial Filters for EEG Single-Trial Classification

M. Kawanabe, K. Müller, B. Blankertz, 2009 .

BCI Systems and Approaches

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2021, Proceedings of the IEEE.

THE BERLIN BRAIN-COMPUTER INTERFACE FOR RAPID RESPONSE

K. Müller, G. Curio, M. Krauledat, 2004 .

Non-separable spatiotemporal deconvolutions improve decoding of neural activity from fMRI signals

N. Logothetis, K. Müller, F. Meinecke, 2011 .

Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance

K. Müller, C. Vidaurre, V. Nikulin, 2020, Frontiers in Neuroscience.

Objective quality assessment of stereoscopic images with vertical disparity using EEG

Sebastian Bosse, Klaus-Robert Müller, Thomas Wiegand, 2017, Journal of neural engineering.

Algebraic Representation of Probability Distributions

Duncan A. J. Blythe, K. Müller, P. V. Bünau, 2011 .

Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectr

Stephen D. Mayhew, C M Michel, S Gonzalez Andino, 2013, Clinical EEG and Neuroscience.

True Zero-Training Brain-Computer Interfacing – An Online Study

Klaus-Robert Müller, Benjamin Schrauwen, Michael Tangermann, 2014, PloS one.

Automatic Identification of Types of Alterations in Historical Manuscripts pdf

Anne Baillot, David Lassner, Sergej Dogadov, 2020, ArXiv.

Higher order stationary subspace analysis

M. Kawanabe, K. Müller, P. V. Bünau, 2016 .

Robust Ensemble Learning for Data Mining

B. Schölkopf, K. Müller, Alex Smola, 2000, PAKDD.

Kernel Principal Component Analysis

Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, 1997, International Conference on Artificial Neural Networks.

Joint Hierarchical Bayesian Learning of Full-structure Noise for Brain Source Imaging

K. Müller, S. Nagarajan, S. Haufe, 2020 .

Autonomous robotic nanofabrication with reinforcement learning

Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz, 2020, Science Advances.

Scanning the issue: The Plurality of Human BrainYComputer Interfacing

José del R. Millán, Gernot R. Müller-Putz, Klaus-Robert Müller, 2015 .

Wasserstein Stationary Subspace Analysis

Shinichi Nakajima, Klaus-Robert Müller, Wojciech Samek, 2018, IEEE Journal of Selected Topics in Signal Processing.

Towards the Interpretability of Deep Learning Models for Human Neuroimaging

Simon M. Hofmann, K. Müller, S. Lapuschkin, 2021 .

Learning Feature Hierarchies with Centered Deep Boltzmann Machines

K. Müller, G. Montavon, 2012, ArXiv.

Toward noninvasive brain-computer interfaces

Klaus-Robert Müller, Benjamin Blankertz, K. Müller, 2006, IEEE Signal Process. Mag..

Hidden Markov mixtures of experts with an application to EEG recordings from sleep

Klaus Pawelzik, Jens Kohlmorgen, Klaus-Robert Müller, 1999 .

Explorative data analysis for changes in neural activity

Klaus-Robert Müller, Duncan A. J. Blythe, Frank C Meinecke, 2013, Journal of neural engineering.

The Influence of Preceding Movements on Motor Cortical Activity in Finger-Tapping

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2004 .

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

Klaus-Robert Müller, Michael Gastegger, Oliver T. Unke, 2021, Nature Communications.

Decoding of top-down cognitive processing for SSVEP-controlled BMI

Klaus-Robert Müller, Sven Dähne, Byoung-Kyong Min, 2016, Scientific Reports.

Invasive BCI Approaches

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

Klaus-Robert Müller, Grégoire Montavon, Michael Bockmayr, 2019, Science Translational Medicine.

Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system

Yoon Gi Chung, K. Müller, H. Bülthoff, 2015, Front. Hum. Neurosci..

An asymptotical Analysis and Improvement of AdaBoost in the binary classification case

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 2000 .

IPAM Program on Machine Learning & Many-Particle Systems-Recent Progress and Open Problems

Farnaz Heidar-Zadeh, Jerzy Hoja, Marina Meila, 2017 .

2020 International brain–computer interface competition: A review

J. Millán, K. Müller, Ji-Hoon Jeong, 2022, Frontiers in Human Neuroscience.

Learning Feature Hierarchies with Centered Deep Boltzmann Machines

K. Müller, G. Montavon, 2012, ArXiv.

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

Simon M. Hofmann, K. Müller, S. Lapuschkin, 2021, NeuroImage.

Explainable ai – preface

Wojciech Samek, Lars Kai Hansen, Andrea Vedaldi, 2019 .

Accurate and Data-Efficient Machine Learning Force Fields for Periodic Systems

Klaus-Robert Müller, Alexandre Tkatchenko, Stefan Chmiela, 2020 .

Pyff – A Pythonic Framework for Feedback Applications and Stimulus Presentation in Neuroscience

Benjamin Blankertz, Klaus-Robert Müller, Matthias S. Treder, 2010, Front. Neurosci..

Analysing ICA component by injection noise

Noboru Murata, Stefan Harmeling, Frank C. Meinecke, 2003 .

Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation

K. Müller, K. Miller, D. Hermes, 2022, bioRxiv.

Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models

K. Müller, S. Nagarajan, S. Haufe, 2021, bioRxiv.

Algebraic Geometric Comparison of Probability Distributions

Duncan A. J. Blythe, K. Müller, P. V. Bünau, 2011, J. Mach. Learn. Res..

Tutorial on multimodal neuroimaging for brain-computer interfacing

K. Müller, Seong-Whan Lee, B. Blankertz, 2013, Balkan Conference in Informatics.

Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models

K. Müller, S. Nagarajan, S. Haufe, 2021, bioRxiv.

Efficient prediction of 3 D electron densities using machine learning

K. Müller, K. Burke, M. Tuckerman, 2018 .

Brain state differences between calibration and application periods influence BCI classification accuracy

Gabriel Curio, Benjamin Blankertz, Matthias Krauledat, 2006 .

Decoding cognitive brain states

Stefan Haufe, Gabriel Curio, Benjamin Blankertz, 2013, 2013 International Winter Workshop on Brain-Computer Interface (BCI).

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

Stefan Haufe, Gabriel Curio, Benjamin Blankertz, 2016, Front. Neurosci..

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, 2016, IEEE Transactions on Image Processing.

Inlier‐based ICA with an application to superimposed images

Klaus-Robert Müller, Stefan Harmeling, Frank C. Meinecke, 2005, Int. J. Imaging Syst. Technol..

Neurophysiological predictor of SMR-based BCI performance

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2010, NeuroImage.

A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization

Klaus-Robert Müller, Steven Lemm, Gabriel Curio, 2009, PLoS Comput. Biol..

The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2008, IEEE Transactions on Biomedical Engineering.

Forecasting Industrial Aging Processes with Machine Learning Methods

K. Müller, M. Bogojeski, F. Horn, 2020, Comput. Chem. Eng..

Unmasking Clever Hans predictors and assessing what machines really learn

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2019, Nature Communications.

Machine Learning Force Fields

Michael Gastegger, Klaus-Robert Muller, Alexandre Tkatchenko, 2020, Chemical reviews.

Robust Ensemble Learning

Gunnar Rätsch, Bernhard Schölkopf, Dale Schuurmans, 2000 .

Asymptotic Bayesian generalization error when training and test distributions are different

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2007, ICML '07.

The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces

Klaus-Robert Müller, Masashi Sugiyama, K. Müller, 2003, J. Mach. Learn. Res..

Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors

Klaus-Robert Müller, Masashi Sugiyama, Koji Tsuda, 2002, IEEE Trans. Neural Networks.

Introduction to machine learning for brain imaging

Klaus-Robert Müller, Steven Lemm, Benjamin Blankertz, 2011, NeuroImage.

Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

C. Neuper, D. Mattia, R. Murray-Smith, 2010, Front. Neurosci..

Extracting latent brain states — Towards true labels in cognitive neuroscience experiments

Alexander Binder, Marius Kloft, Klaus-Robert Müller, 2015, NeuroImage.

Kernel-Based Nonlinear Blind Source Separation

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2003, Neural Computation.

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Stefan Haufe, Klaus-Robert Müller, Wojciech Samek, 2015, Proceedings of the IEEE.

A regularized discriminative framework for EEG analysis with application to brain–computer interface

Klaus-Robert Müller, Ryota Tomioka, K. Müller, 2010, NeuroImage.

An introduction to kernel-based learning algorithms

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2001, IEEE Trans. Neural Networks.

Leaf-inspired homeostatic cellulose biosensors

K. Müller, Seong-Whan Lee, C-Yoon Kim, 2021, Science Advances.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

Tjeerd W. Boonstra, Arno Villringer, Guido Nolte, 2019, NeuroImage.

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

Klaus-Robert Müller, Jaeyoung Shin, Han-Jeong Hwang, 2018, Sensors.

Descriptor : Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

Klaus-Robert Müller, Jaeyoung Shin, Han-Jeong Hwang, 2018 .

Open Access Dataset for EEG+NIRS Single-Trial Classification

Klaus-Robert Müller, Jichai Jeong, Benjamin Blankertz, 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Evaluation of a Compact Hybrid Brain-Computer Interface System

Klaus-Robert Müller, Jaeyoung Shin, Han-Jeong Hwang, 2017, BioMed research international.

Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans

Andreas Ziehe, Klaus-Robert Müller, Gabriel Curio, 2000, IEEE Transactions on Biomedical Engineering.

Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals pdf

Li Li, Klaus-Robert Müller, Kevin Vu, 2015, ArXiv.

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

K. Müller, A. Tkatchenko, Stefan Chmiela, 2020, Nature communications.

Finding brain oscillations with power dependencies in neuroimaging data

Stefan Haufe, Klaus-Robert Müller, Vadim V. Nikulin, 2014, NeuroImage.

Robustifying EEG data analysis by removing outliers

Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, 2009 .

Stopping conditions for exact computation of leave-one-out error in support vector machines

Klaus-Robert Müller, Pavel Laskov, Vojtech Franc, 2008, ICML '08.

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Barrier Boosting

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2000, COLT.

A Mathematical Programming Approach to the Kernel Fisher Algorithm

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2000, NIPS.

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2000, German Conference on Bioinformatics.

A kernel-based statistical analysis of the residual error in video coding

Detlev Marpe, Thomas Wiegand, Klaus-Robert Müller, 2015, 2015 International Conference on Systems, Signals and Image Processing (IWSSIP).

Kernel Methods for Quantum Chemistry

Klaus-Robert Müller, Wiktor Pronobis, K. Müller, 2020 .

Understanding Machine-learned Density Functionals pdf

Li Li, Klaus-Robert Müller, John C. Snyder, 2014, ArXiv.

Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]

Sergios Theodoridis, Klaus-Robert Müller, Kenji Fukumizu, 2013, IEEE Signal Process. Mag..

Special Issue on Advances in Kernel-Based Learning for Signal Processing

Sergios Theodoridis, Klaus-Robert Müller, Kenji Fukumizu, 2013 .

Optimizing transition states via kernel-based machine learning.

Klaus-Robert Müller, Matthias Rupp, Graeme Henkelman, 2012, The Journal of chemical physics.

Convex Cost Functions for Support Vector Regression

Bernhard Schölkopf, Alexander J. Smola, Tom Ziemke, 1998 .

From outliers to prototypes: Ordering data

Klaus-Robert Müller, David M. J. Tax, Stefan Harmeling, 2006, Neurocomputing.

Evaluating the Visualization of What a Deep Neural Network Has Learned

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Machine learning methods of the Berlin brain-computer interface

Wojciech Samek, Klaus-Robert Müller, Carmen Vidaurre, 2015 .

Feature Extraction for Change-Point Detection Using Stationary Subspace Analysis

Klaus-Robert Müller, Frank C. Meinecke, Paul von Bünau, 2011, IEEE Transactions on Neural Networks and Learning Systems.

Single-trial analysis and classification of ERP components — A tutorial

Stefan Haufe, Klaus-Robert Müller, Steven Lemm, 2011, NeuroImage.

In Search of Non-Gaussian Components of a High-Dimensional Distribution

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, J. Mach. Learn. Res..

A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, ICA.

Estimating Functions for Blind Separation When Sources Have Variance Dependencies

Motoaki Kawanabe, Klaus-Robert Müller, M. Kawanabe, 2004, J. Mach. Learn. Res..

A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation

Andreas Ziehe, Klaus-Robert Müller, Guido Nolte, 2004, J. Mach. Learn. Res..

Injecting noise for analysing the stability of ICA components

Klaus-Robert Müller, Stefan Harmeling, Frank C. Meinecke, 2004, Signal Process..

Blind Source Separation Techniques for Decomposing Event-Related Brain Signals

Andreas Ziehe, Klaus-Robert Müller, Ricardo Vigário, 2004, Int. J. Bifurc. Chaos.

Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2003, J. Mach. Learn. Res..

Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation

Andreas Ziehe, A. Cichocki, S. Makino, 2003 .

A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2002, IEEE Transactions on Biomedical Engineering.

Estimating the Reliability of ICA Projections

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2001, NIPS.

Incremental Support Vector Learning: Analysis, Implementation and Applications

Klaus-Robert Müller, Pavel Laskov, Stefan Krüger, 2006, J. Mach. Learn. Res..

Machine learning for molecular simulation

Frank Noé, Cecilia Clementi, Klaus-Robert Müller, 2019, Annual review of physical chemistry.

SchNetPack: A Deep Learning Toolbox For Atomistic Systems.

K-R Müller, Kristof T. Schütt, M Gastegger, 2018, Journal of chemical theory and computation.

SchNet - A deep learning architecture for molecules and materials.

K-R Müller, Kristof T. Schütt, K T Schütt, 2017, The Journal of chemical physics.

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Stefan Haufe, Klaus-Robert Müller, Gabriel Curio, 2010, Front. Neurosci..

The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2007, NeuroImage.

Minimizing Trust Leaks for Robust Sybil Detection

Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz, 2017, ICML.

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data

Klaus-Robert Müller, Nico Görnitz, Daniel Ziemek, 2019, Scientific Reports.

DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies

Klaus-Robert Müller, Nico Görnitz, Alexandre Rozier, 2020, bioRxiv.

Nanotechnology in the Environmental Health Sciences

B. Schölkopf, K. Müller, S. Sonnenburg, 2005, Environmental Health Perspectives.

Wasserstein Training of Restricted Boltzmann Machines

Klaus-Robert Müller, Grégoire Montavon, Marco Cuturi, 2016, NIPS.

Science Journals — AAAS

K. Müller, Seong-Whan Lee, Dong-Ok Won, 2020 .

Artificial Intelligence and Pathology: from Principles to Practice and Future Applications in Histomorphology and Molecular Profiling.

K. Müller, F. Tacke, M. Alber, 2021, Seminars in cancer biology.

Interpretable deep neural networks for single-trial EEG classification

Klaus-Robert Müller, Wojciech Samek, Sebastian Bach, 2016, Journal of Neuroscience Methods.

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Klaus-Robert Müller, Alexandre Tkatchenko, Pieter-Jan Kindermans, 2017, NIPS.

Finding Density Functionals with Machine Learning

Klaus-Robert Müller, John C. Snyder, Matthias Rupp, 2011, Physical review letters.

PatternNet and PatternLRP - Improving the interpretability of neural networks pdf

Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans, 2017, ArXiv.

Deep Semi-Supervised Anomaly Detection pdf

Alexander Binder, Emmanuel Müller, Marius Kloft, 2019, ICLR.

BIGDML—Towards accurate quantum machine learning force fields for materials pdf

Klaus-Robert Müller, Alexandre Tkatchenko, Stefan Chmiela, 2021, Nature Communications.

Methods for interpreting and understanding deep neural networks

Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon, 2017, Digit. Signal Process..

Explaining nonlinear classification decisions with deep Taylor decomposition

Alexander Binder, Wojciech Samek, Sebastian Lapuschkin, 2015, Pattern Recognit..

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Christopher J. Anders, K. Müller, G. Montavon, 2021, Proceedings of the IEEE.

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Alexander Binder, Wojciech Samek, Sebastian Lapuschkin, 2016, ICANN.

Evaluating Recurrent Neural Network Explanations pdf

Klaus-Robert Müller, Wojciech Samek, Leila Arras, 2019, BlackboxNLP@ACL.

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Klaus-Robert Müller, Wojciech Samek, Leila Arras, 2017, WASSA@EMNLP.

"What is relevant in a text document?": An interpretable machine learning approach

Klaus-Robert Müller, Wojciech Samek, Franziska Horn, 2016, PloS one.

Explaining Predictions of Non-Linear Classifiers in NLP pdf

Klaus-Robert Müller, Wojciech Samek, Franziska Horn, 2016, Rep4NLP@ACL.

The LRP Toolbox for Artificial Neural Networks

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2016, J. Mach. Learn. Res..

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks pdf

Shinichi Nakajima, Klaus-Robert Müller, Grégoire Montavon, 2020, ArXiv.

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond pdf

Wojciech Samek, Sebastian Lapuschkin, Klaus-Robert Müller, 2020, ArXiv.

Towards Explainable Artificial Intelligence

Klaus-Robert Müller, Wojciech Samek, K. Müller, 2019, Explainable AI.

Approximate Tree Kernels

Klaus-Robert Müller, Ulf Brefeld, Konrad Rieck, 2010, J. Mach. Learn. Res..

Securing IMS against novel threats

Klaus-Robert Müller, Konrad Rieck, Stefan Wahl, 2009, Bell Labs Technical Journal.

A Self-learning System for Detection of Anomalous SIP Messages

Klaus-Robert Müller, Konrad Rieck, Stefan Wahl, 2008, IPTComm.

Machine Learning for Intrusion Detection

Klaus-Robert Müller, Konrad Rieck, Pavel Laskov, 2007, NATO ASI Mining Massive Data Sets for Security.

Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set

Igor V. Tetko, Artem Cherkasov, Klaus-Robert Müller, 2010, J. Chem. Inf. Model..

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning pdf

Wojciech Samek, Sebastian Lapuschkin, Klaus-Robert Müller, 2019, Pattern Recognit..

Review of the BCI Competition IV

Ad Aertsen, Hubert Preissl, Clemens Brunner, 2012, Front. Neurosci..

Clustering with the Fisher Score

Motoaki Kawanabe, Klaus-Robert Müller, Koji Tsuda, 2002, NIPS.

Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2011, Neural Computation.

Machine-Learning Based Co-adaptive Calibration : Towards a Cure for BCI illiteracy

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2010 .

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2009 .

The Berlin Brain-Computer Interface

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2008, WCCI.

Interpretable LSTMs For Whole-Brain Neuroimaging Analyses pdf

Klaus-Robert Müller, Wojciech Samek, Hauke R. Heekeren, 2018, ArXiv.

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

Wojciech Samek, Hauke R. Heekeren, Klaus-Robert Müller, 2018, Front. Neurosci..

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2019, Scientific Reports.

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Christopher J. Anders, K. Müller, S. Lapuschkin, 2021, ArXiv.

Morphological and molecular breast cancer profiling through explainable machine learning

Masaru Ishii, Klaus-Robert Müller, Daniel Heim, 2021, Nature Machine Intelligence.

Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images

Alexander Binder, René Saitenmacher, Klaus-Robert Müller, 2020, AI and ML for Digital Pathology.

iNNvestigate neural networks!

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2018, J. Mach. Learn. Res..

Inducing Metric Violations in Human Similarity Judgements

Klaus-Robert Müller, Felix A. Wichmann, Jakob H. Macke, 2006, NIPS.

Feature Discovery in Non-Metric Pairwise Data

Klaus-Robert Müller, Julian Laub, K. Müller, 2004, J. Mach. Learn. Res..

Explanations can be manipulated and geometry is to blame

Klaus-Robert Müller, Maximilian Alber, Christopher J. Anders, 2019, NeurIPS.

Support Vector Data Descriptions and $k$ -Means Clustering: One Class?

Shinichi Nakajima, Klaus-Robert Müller, Marius Kloft, 2018, IEEE Transactions on Neural Networks and Learning Systems.

SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor

Gunnar Rätsch, Klaus-Robert Müller, Marius Kloft, 2015, PloS one.

StructRank: A New Approach for Ligand-Based Virtual Screening

Klaus-Robert Müller, Ulf Brefeld, Fabian Rathke, 2011, J. Chem. Inf. Model..

An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

Klaus-Robert Müller, Siamac Fazli, Seong-Whan Lee, 2014, PloS one.

Unmasking Clever Hans predictors and assessing what machines really learn

K. Müller, Alexander Binder, G. Montavon, 2019, Nature Communications.

On Optimal Channel Configurations for SMR-based Brain–Computer Interfaces

Klaus-Robert Müller, Benjamin Blankertz, Thorsten Dickhaus, 2010, Brain Topography.

Visualization of anomaly detection using prediction sensitivity

Klaus-Robert Müller, Christin Schäfer, Konrad Rieck, 2005, Sicherheit.

Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2006, IEEE Transactions on Biomedical Engineering.

Spatio-spectral filters for improving the classification of single trial EEG

Klaus-Robert Müller, Steven Lemm, Gabriel Curio, 2005, IEEE Transactions on Biomedical Engineering.

Bypassing the Kohn-Sham equations with machine learning

Li Li, Klaus-Robert Müller, Kieron Burke, 2016, Nature Communications.

Classifying Single Trial EEG: Towards Brain Computer Interfacing

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2001, NIPS.

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

Robert Jenssen, Klaus-Robert Müller, Alexander Zien, 2012, PloS one.

A new scatter-based multi-class support vector machine

Robert Jenssen, Klaus-Robert Müller, Alexander Zien, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models pdf

Klaus-Robert Müller, Grégoire Montavon, Jacob Kauffmann, 2018, Pattern Recognit..

Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.

K-R Müller, S Michiels, F Klauschen, 2018, Seminars in cancer biology.

Explaining nonlinear classification decisions with deep Taylor decomposition

K. Müller, Alexander Binder, G. Montavon, 2015, Pattern Recognit..

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

Alexander Binder, Wojciech Samek, Klaus-Robert Müller, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces

Benjamin Blankertz, Klaus-Robert Müller, Roderick Murray-Smith, 2009, Int. J. Hum. Comput. Stud..

The Berlin Brain-Computer Interface (BBCI) – towards a new communication channel for online control in gaming applications

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2007, Multimedia Tools and Applications.

Myoelectric Control of Artificial Limbs¿Is There a Need to Change Focus? [In the Spotlight]

Dario Farina, Klaus-Robert Müller, Strahinja Dosen, 2012, IEEE Signal Process. Mag..

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

Klaus-Robert Müller, Wojciech Samek, Simon Wiedemann, 2019, IEEE Transactions on Neural Networks and Learning Systems.

On robust parameter estimation in brain–computer interfacing

Motoaki Kawanabe, Shinichi Nakajima, Klaus-Robert Müller, 2017, Journal of neural engineering.

Brain–computer interfacing under distraction: an evaluation study

Stephanie Brandl, Wojciech Samek, Klaus-Robert Müller, 2016, Journal of neural engineering.

Automated ocular artifact removal: comparing regression and component-based methods

Andreas Ziehe, Klaus-Robert Müller, Alois Schloegl, 2009 .

Combining Features for BCI

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2002, NIPS.

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

K. Müller, A. Tkatchenko, M. Gastegger, 2020, The Journal of chemical physics.

Robust Spatial Filtering with Beta Divergence

Motoaki Kawanabe, Klaus-Robert Müller, Wojciech Samek, 2013, NIPS.

Exploring chemical compound space with quantum-based machine learning

K. Müller, A. Tkatchenko, O. A. von Lilienfeld, 2019, Nature Reviews Chemistry.

Recent advances in brain-machine interfaces

Tadashi Isa, Klaus-Robert Müller, Eberhard E. Fetz, 2009, Neural Networks.

Explaining and Interpreting LSTMs pdf

Wojciech Samek, Klaus-Robert Müller, Leila Arras, 2019, Explainable AI.

Optimizing spatio-temporal filters for improving Brain-Computer Interfacing

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2005, NIPS.

Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces

Motoaki Kawanabe, Benjamin Blankertz, Klaus-Robert Müller, 2011, IEEE Transactions on Biomedical Engineering.

Compact and Computationally Efficient Representation of Deep Neural Networks

Klaus-Robert Müller, Wojciech Samek, Simon Wiedemann, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Enhancing the signal-to-noise ratio of ICA-based extracted ERPs

Klaus-Robert Müller, Steven Lemm, Gabriel Curio, 2006, IEEE Transactions on Biomedical Engineering.

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Klaus-Robert Müller, Alexandre Tkatchenko, Igor Poltavsky, 2018, Comput. Phys. Commun..

Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface

Klaus-Robert Müller, Gabriel Curio, Arno Villringer, 2022 .

Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

David A. Boas, Alexander von Lühmann, Klaus-Robert Müller, 2019, NeuroImage.

Basis profile curve identification to understand electrical stimulation effects in human brain networks

K. Müller, K. Miller, D. Hermes, 2021, bioRxiv.

A critical assessment of connectivity measures for EEG data: A simulation study

Stefan Haufe, Klaus-Robert Müller, Guido Nolte, 2013, NeuroImage.

Layer-Wise Relevance Propagation: An Overview

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2019, Explainable AI.

Robustifying models against adversarial attacks by Langevin dynamics

Shinichi Nakajima, Wojciech Samek, Klaus-Robert Müller, 2018, Neural Networks.

Asymptotically unbiased estimation of physical observables with neural samplers.

Shinichi Nakajima, Wojciech Samek, Klaus-Robert Müller, 2020, Physical review. E.

Berlin Brain-Computer Interface - The HCI communication channel for discovery

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2007, Int. J. Hum. Comput. Stud..

M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring

Heidrun Wabnitz, Klaus-Robert Müller, Alexander von Lühmann, 2017, IEEE Transactions on Biomedical Engineering.

Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

Explaining the unique nature of individual gait patterns with deep learning

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2018, Scientific Reports.

Efficient Exact Inference With Loss Augmented Objective in Structured Learning

Shinichi Nakajima, Klaus-Robert Müller, Alexander Bauer, 2017, IEEE Transactions on Neural Networks and Learning Systems.

Unsupervised Decomposition Methods for Analysis of Multimodal Neural Data

Klaus-Robert Müller, Frank C. Meinecke, Felix Biessmann, 2013 .

Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions

Klaus-Robert Müller, Felix Bießmann, Yusuke Murayama, 2012, NeuroImage.

Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information

Klaus-Robert Müller, Felix Bießmann, Yusuke Murayama, 2011, MLINI.

Temporal kernel CCA and its application in multimodal neuronal data analysis

Klaus-Robert Müller, Arthur Gretton, Felix Bießmann, 2010, Machine Learning.

A consistency-based model selection for one-class classification

Klaus-Robert Müller, David M. J. Tax, K. Müller, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Explainable artificial intelligence

Klaus-Robert Müller, Thomas Wiegand, Wojciech Samek, 2017 .

On the Byzantine Robustness of Clustered Federated Learning

Thomas Wiegand, Wojciech Samek, Klaus-Robert Müller, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Klaus-Robert Müller, Wojciech Samek, Simon Wiedemann, 2018, 2019 International Joint Conference on Neural Networks (IJCNN).

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models pdf

Klaus-Robert Müller, Thomas Wiegand, Wojciech Samek, 2017, ArXiv.

Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface

Klaus-Robert Müller, Han-Jeong Hwang, Chang-Hee Han, 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Layer-Wise Relevance Propagation for Deep Neural Network Architectures

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2016 .

Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals pdf

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2018, ArXiv.

Autonomous robotic nanofabrication with reinforcement learning

Kristof T. Schütt, K. Müller, C. Wagner, 2020, Science Advances.

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

Klaus-Robert Müller, Han-Jeong Hwang, Chang-Hee Han, 2020 .

Motion-Based Rapid Serial Visual Presentation for Gaze-Independent Brain-Computer Interfaces

Klaus-Robert Müller, Dong-Min Kim, Seong-Whan Lee, 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

A lower limb exoskeleton control system based on steady state visual evoked potentials

Klaus-Robert Müller, Seong-Whan Lee, No-Sang Kwak, 2015, Journal of neural engineering.

Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters

Klaus-Robert Müller, Heung-Il Suk, Siamac Fazli, 2014, PloS one.

Transferring Subspaces Between Subjects in Brain--Computer Interfacing

Wojciech Samek, Klaus-Robert Müller, Frank C. Meinecke, 2012, IEEE Transactions on Biomedical Engineering.

An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels

Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller, 2017, J. Mach. Learn. Res..

Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation pdf

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, 2016, ArXiv.

Evaluating deep transfer learning for whole-brain cognitive decoding pdf

Klaus-Robert Müller, Wojciech Samek, Ulman Lindenberger, 2021, J. Frankl. Inst..

Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

Han-Jeong Hwang, Jaeyoung Shin, Klaus-R Müller, 2016, Scientific Reports.

On the information and representation of non-Euclidean pairwise data

Joachim M. Buhmann, Volker Roth, Klaus-Robert Müller, 2006, Pattern Recognit..

Going Metric: Denoising Pairwise Data

Joachim M. Buhmann, Volker Roth, Klaus-Robert Müller, 2002, NIPS.

From Clustering to Cluster Explanations via Neural Networks pdf

Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, 2019, IEEE transactions on neural networks and learning systems.

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Stefan Haufe, Klaus-Robert Müller, Michael Tangermann, 2014, NeuroImage.

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Klaus-Robert Müller, Han-Jeong Hwang, Janne Mathias Hahne, 2017, PloS one.

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Stefan Haufe, Klaus-Robert Müller, Carmen Vidaurre, 2019, NeuroImage.

CSP patches: an ensemble of optimized spatial filters. An evaluation study

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2011, Journal of neural engineering.

Using Reduce for Replica Calculations

Paul Lukowicz, Klaus-Robert Müller, Werner M. Seiler, 1993 .

Spatial Filtering for Robust Myoelectric Control

Klaus-Robert Müller, Bernhard Graimann, Janne Mathias Hahne, 2012, IEEE Transactions on Biomedical Engineering.

IMPROVING CLASSIFICATION PERFORMANCE OF A BCI SYSTEM BY SHIFTING RAPID SERIAL VISUAL PRESENTATION

K. Müller, Seong-Whan Lee, Han-Jeong Hwang, 2017 .

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

Kristof T. Schütt, K. Müller, A. Tkatchenko, 2018, The European Physical Journal B.

Measuring phase synchronization of superimposed signals.

Andreas Ziehe, Klaus-Robert Müller, Jürgen Kurths, 2005, Physical review letters.

A mathematical model for the two-learners problem

Klaus-Robert Müller, Carmen Vidaurre, Martijn Schreuder, 2017, Journal of neural engineering.

Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA.

Klaus-Robert Müller, Yusuke Murayama, Axel Oeltermann, 2010, Magnetic resonance imaging.

Covariance shrinkage for autocorrelated data

Klaus-Robert Müller, Daniel Bartz, K. Müller, 2014, NIPS.

Toward a Direct Measure of Video Quality Perception Using EEG

Sebastian Bosse, Klaus-Robert Müller, Thomas Wiegand, 2012, IEEE Transactions on Image Processing.

Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition

Sebastian Bosse, Klaus-Robert Müller, Thomas Wiegand, 2018, IEEE Transactions on Circuits and Systems for Video Technology.

EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

Sebastian Bosse, Klaus-Robert Müller, Thomas Wiegand, 2015, Journal of neural engineering.

Adaptive Methods in BCI Research - An Introductory Tutorial

Klaus-Robert Müller, Carmen Vidaurre, Alois Schlögl, 2009 .

Stereoscopic depth increases intersubject correlations of brain networks

Klaus-Robert Müller, Felix Bießmann, Michael Gaebler, 2014, NeuroImage.

Rethinking BCI Paradigm and Machine Learning Algorithm as a Symbiosis: Zero Calibration, Guaranteed Convergence and High Decoding Performance

Klaus-Robert Müller, Michael Tangermann, David Hübner, 2019, Brain-Computer Interface Research.

Towards Zero Training for Brain-Computer Interfacing

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2008, PloS one.

A Note on Brain Actuated Spelling with the Berlin Brain-Computer Interface

Benjamin Blankertz, Klaus-Robert Müller, Roderick Murray-Smith, 2007, HCI.

On-line Differentiation Of Neuroelectric Activities: Algorithms And applications

B. Blankertz, G. Curio, G. Dornhege, 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

Improving BCI performance by task-related trial pruning

Klaus-Robert Müller, Mikio L. Braun, Claudia Sannelli, 2009, Neural Networks.

Playing Pinball with non-invasive BCI

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2008, NIPS.

N-ary decomposition for multi-class classification

Klaus-Robert Müller, Shen-Shyang Ho, Joey Tianyi Zhou, 2019, Machine Learning.

Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework

Stefan Haufe, Gitta Kutyniok, Klaus-Robert Müller, 2020, NeuroImage.

Large-scale EEG/MEG source localization with spatial flexibility

Stefan Haufe, Klaus-Robert Müller, Benjamin Blankertz, 2011, NeuroImage.

Estimating vector fields using sparse basis field expansions

Andreas Ziehe, Stefan Haufe, Klaus-Robert Müller, 2008, NIPS.

Combining sparsity and rotational invariance in EEG/MEG source reconstruction

Andreas Ziehe, Stefan Haufe, Klaus-Robert Müller, 2008, NeuroImage.

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2019, PloS one.

EEG-based BCI for the linear control of an upper-limb neuroprosthesis.

Klaus-Robert Müller, Carmen Vidaurre, Christian Klauer, 2016, Medical engineering & physics.

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Wojciech Samek, Gabriel Curio, Benjamin Blankertz, 2016, NeuroImage.

Neurophysiological assessment of perceived image quality using steady-state visual evoked potentials

Sebastian Bosse, Klaus-Robert Müller, Gabriel Curio, 2015, SPIE Optical Engineering + Applications.

Robust Common Spatial Filters with a Maxmin Approach

Motoaki Kawanabe, Klaus-Robert Müller, Wojciech Samek, 2014, Neural Computation.

Robust common spatial filters with a maxmin approach.

Motoaki Kawanabe, Klaus-Robert Müller, Carmen Vidaurre, 2009, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

A Maxmin Approach to Optimize Spatial Filters for EEG Single-Trial Classification

Motoaki Kawanabe, Klaus-Robert Müller, Benjamin Blankertz, 2009, IWANN.

Machine Learning and Applications for Brain-Computer Interfacing

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2007, HCI.

Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach

Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann, 2006, NIPS.

Importance-Weighted Cross-Validation for Covariate Shift

Klaus-Robert Müller, Masashi Sugiyama, Benjamin Blankertz, 2006, DAGM-Symposium.

Towards adaptive classification for BCI

Rajesh P. N. Rao, Klaus-Robert Müller, Benjamin Blankertz, 2006, Journal of neural engineering.

Recent Progress in Brain and Cognitive Engineering

Klaus-Robert Müller, Heinrich H. Bülthoff, Seong-Whan Lee, 2015, Trends in Augmentation of Human Performance.

Multifrequency Analysis of Brain-Computer Interfaces

Klaus-Robert Müller, Heung-Il Suk, Siamac Fazli, 2015 .

First study towards linear control of an upper-limb neuroprosthesis with an EEG-based Brain-Computer Interface

Francisco Velasco-Álvarez, Klaus-Robert Müller, Carmen Vidaurre, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Adaptation in Brain-Computer Interfaces

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States

Klaus-Robert Müller, Steven Lemm, Gabriel Curio, 2006, J. Univers. Comput. Sci..

A note on the Berlin Brain-Computer Interface

Benjamin Blankertz, Klaus-Robert Müller, Stefan Jähnichen, 2006 .

The Berlin Brain-Computer Interface: Report from the Feedback Sessions

Klaus-Robert Müller, Gabriel Curio, Benjamin Blankertz, 2005 .

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

K. Müller, A. Villringer, G. Nolte, 2021, NeuroImage.

Electroencephalography/sonication-mediated human brain-brain interfacing technology.

Klaus-Robert Müller, Byoung-Kyong Min, K. Müller, 2014, Trends in biotechnology.

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach.

Cecilia Clementi, Klaus-Robert Muller, Stefan Chmiela, 2020, The Journal of chemical physics.

Fairwashing Explanations with Off-Manifold Detergent pdf

Klaus-Robert Müller, Christopher J. Anders, Pan Kessel, 2020, ICML.

Brain-Computer Interfacing for multimedia quality assessment

Thomas Wiegand, Sebastian Bosse, Wojciech Samek, 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Subject-independent mental state classification in single trials

Klaus-Robert Müller, Benjamin Blankertz, Siamac Fazli, 2009, Neural Networks.

Noise robust estimates of correlation dimension and K2 entropy.

K. Müller, A. Ziehe, G. Nolte, 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

The effect of linear mixing in the EEG on Hurst exponent estimation

Stefan Haufe, Klaus-Robert Müller, Duncan A. J. Blythe, 2014, NeuroImage.

Co-adaptive calibration to improve BCI efficiency

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2011, Journal of neural engineering.

Curly: An AI-based Curling Robot Successfully Competing in the Olympic Discipline of Curling

Klaus-Robert Müller, Byung-Do Kim, Seong-Whan Lee, 2018, IJCAI.

Improving zero-training brain-computer interfaces by mixing model estimators

T Verhoeven, D Hübner, M Tangermann, 2017, Journal of neural engineering.

Uniqueness of Non-Gaussianity-Based Dimension Reduction

Motoaki Kawanabe, Klaus-Robert Müller, Fabian J. Theis, 2011, IEEE Transactions on Signal Processing.

Identifying Granger causal relationships between neural power dynamics and variables of interest

Stefan Haufe, Klaus-Robert Müller, Sven Dähne, 2015, NeuroImage.

Entropy-Constrained Training of Deep Neural Networks

Klaus-Robert Müller, Wojciech Samek, Simon Wiedemann, 2018, 2019 International Joint Conference on Neural Networks (IJCNN).

Understanding and Comparing Deep Neural Networks for Age and Gender Classification

Alexander Binder, Wojciech Samek, Sebastian Lapuschkin, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

Porosity estimation by semi-supervised learning with sparsely available labeled samples

Shinichi Nakajima, Marley M. B. R. Vellasco, Klaus-Robert Müller, 2017, Comput. Geosci..

What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis pdf

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2018, ArXiv.

Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution

Shinichi Nakajima, Klaus-Robert Müller, Wojciech Samek, 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

Features spaces and a learning system for structural-temporal data, and their application on a use case of real-time communication network validation data

Klaus-Robert Müller, Guido Schwenk, Ben Jochinke, 2020, PloS one.

ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI

Klaus-Robert Müller, Siamac Fazli, Márton Danóczy, 2011, NeuroImage.

Subject independent EEG-based BCI decoding

Klaus-Robert Müller, Benjamin Blankertz, Siamac Fazli, 2009, NIPS.

Mammography Image Quality Assurance Using Deep Learning

Clemens Elster, Tobias Schaeffter, Tobias Kretz, 2020, IEEE Transactions on Biomedical Engineering.

An Iterative Algorithm for Spatio-Temporal Filter Optimization

K. Aihara, G. Dornhege, R. Tomioka, 2006 .

Building and Interpreting Deep Similarity Models

Matteo Valleriani, Klaus-Robert Müller, Grégoire Montavon, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deep Transfer Learning For Whole-Brain fMRI Analyses pdf

Klaus-Robert Müller, Wojciech Samek, Armin W. Thomas, 2019, OR/MLCN@MICCAI.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

Klaus-Robert Müller, Benjamin Schrauwen, Michael Tangermann, 2014, Journal of neural engineering.

Automating the search for a patent’s prior art with a full text similarity search

Klaus-Robert Müller, Franziska Horn, Franziska Biegler, 2019, PloS one.

An Introduction to Brain-Computer Interfacing

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

Brain-Computer Interfaces for Communication and Motor Control—Perspectives on Clinical Applications

José del R. Millán, Klaus-Robert Müller, Thilo Hinterberger, 2007 .

THE BERLIN BRAIN-COMPUTER INTERFACE PRESENTS THE NOVEL MENTAL TYPEWRITER HEX-O-SPELL

Benjamin Blankertz, Klaus-Robert Müller, Roderick Murray-Smith, 2006 .

Rotation Invariant Clustering of 3D Cell Nuclei Shapes *

Wojciech Samek, Klaus-Robert Müller, Patrick Wagner, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Stefan Haufe, Klaus-Robert Müller, Jens Steinbrink, 2014, Journal of biomedical optics.

Optimizing spatial filters for the extraction of envelope-coupled neural oscillations

Stefan Haufe, Klaus-Robert Müller, Vadim V. Nikulin, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Machine-Learning Based Co-adaptive Calibration: A Perspective to Fight BCI Illiteracy

Klaus-Robert Müller, Benjamin Blankertz, Carmen Vidaurre, 2010, HAIS.

Brain-Computer Interfacing for Intelligent Systems

Desney S. Tan, José del R. Millán, Anton Nijholt, 2008, IEEE Intelligent Systems.

Learning representations of molecules and materials with atomistic neural networks pdf

Klaus-Robert Müller, Alexandre Tkatchenko, Kristof T. Schütt, 2018, Machine Learning Meets Quantum Physics.

Quantum-chemical insights from interpretable atomistic neural networks

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2018, Explainable AI.

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Michael Gastegger, Klaus-Robert Müller, Alexandre Tkatchenko, 2019, Nature Communications.

Towards an enhanced ERP speller based on the visual processing of face familiarity

Klaus-Robert Müller, Siamac Fazli, Seong-Whan Lee, 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Klaus-Robert Müller, Michael Tangermann, Johannes Höhne, 2014, PloS one.

The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis

Klaus-Robert Müller, Benjamin Blankertz, Forooz Shahbazi Avarvand, 2016, NeuroImage.

A better metric in kernel adaptive filtering

Klaus-Robert Müller, Masahiro Yukawa, Airi Takeuchi, 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

Investigating the influence of noise and distractors on the interpretation of neural networks pdf

Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans, 2016, ArXiv.

Learning how to explain neural networks: PatternNet and PatternAttribution

Kristof T. Schütt, D. Erhan, K. Müller, 2017, ICLR.

Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG

Motoaki Kawanabe, Stefan Haufe, Klaus-Robert Müller, 2009, IEEE Transactions on Biomedical Engineering.

Explainable Artificial Intelligence

Michael Ridley, K. Müller, W. Samek, 2019, Ethics of Artificial Intelligence.

Regression for sets of polynomial equations

Franz J. Király, Klaus-Robert Müller, Frank C. Meinecke, 2011, AISTATS.

Scrutinizing XAI using linear ground-truth data with suppressor variables

K. Müller, S. Haufe, Rick Wilming, 2021, Machine Learning.

Towards Robust Explanations for Deep Neural Networks pdf

Klaus-Robert Müller, Christopher J. Anders, Pan Kessel, 2020, Pattern Recognit..

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

Thomas Wiegand, Wojciech Samek, Klaus-Robert Müller, 2020, npj Digital Medicine.

ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines

Klaus-Robert Müller, Marius Kloft, Nico Görnitz, 2017, PloS one.

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

P. Laskov, C. Schäfer, I. Kotenko, 2004, Prax. Inf.verarb. Kommun..

When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning

Alexander Binder, Anne K. Porbadnigk, Claudia Sannelli, 2014, 2014 International Winter Workshop on Brain-Computer Interface (BCI).

Discovering topics in text datasets by visualizing relevant words pdf

Klaus-Robert Müller, Wojciech Samek, Franziska Horn, 2017, ArXiv.

Exploring text datasets by visualizing relevant words pdf

Klaus-Robert Müller, Wojciech Samek, Franziska Horn, 2017, ArXiv.

The BCI competition III: validating alternative approaches to actual BCI problems

G. Pfurtscheller, K.-R. Muller, B. Blankertz, 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]

Klaus-Robert Müller, Michael Tangermann, David Hübner, 2018, IEEE Computational Intelligence Magazine.

Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

Klaus-Robert Müller, Michael Tangermann, Konstantin Schmid, 2017, PloS one.

Virtual screening for PPAR-gamma ligands using the ISOAK molecular graph kernel and gaussian processes

K-R Müller, T Schroeter, M Rupp, 2009 .

Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2005, NIPS.

The Berlin brain-computer interface: EEG-based communication without subject training

K.-R. Muller, B. Blankertz, F. Losch, 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

K. Müller, G. Montavon, T. Kanamori, 2021, Neural Networks.

Insights from Classifying Visual Concepts with Multiple Kernel Learning

Motoaki Kawanabe, Shinichi Nakajima, Alexander Binder, 2011, PloS one.

Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

Sebastian Bosse, Klaus-Robert Müller, Wojciech Samek, 2019, Digit. Signal Process..

Unmixing Hyperspectral Data

Andreas Ziehe, Lucas C. Parra, Klaus-Robert Müller, 1999, NIPS.

Identifying interactions in mixed and noisy complex systems.

Andreas Ziehe, Klaus-Robert Müller, Guido Nolte, 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed pdf

Wojciech Samek, Sebastian Lapuschkin, Klaus-Robert Müller, 2019, ArXiv.

Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control

D. Farina, L. C. Parra, J. M. Hahne, 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Identification of nonstationary dynamics in physiological recordings

Klaus-Robert Müller, Klaus Pawelzik, Jens Kohlmorgen, 2000, Biological Cybernetics.

Tackling noise, artifacts and nonstationarity in BCI with robust divergences

Klaus-Robert Müller, Wojciech Samek, K. Müller, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Optimizing for Measure of Performance in Max-Margin Parsing

Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz, 2017, IEEE Transactions on Neural Networks and Learning Systems.

Simultaneous and proportional control of 2D wrist movements with myoelectric signals

Dario Farina, Lucas C. Parra, Klaus-Robert Müller, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

Integration of Multivariate Data Streams With Bandpower Signals

Klaus-Robert Müller, Siamac Fazli, Felix Bießmann, 2013, IEEE Transactions on Multimedia.

Feature Importance Measure for Non-linear Learning Algorithms pdf

Klaus-Robert Müller, Marius Kloft, Nico Görnitz, 2016, ArXiv.

Learning and Evaluation in Presence of Non-i.i.d. Label Noise

Alexander Binder, Klaus-Robert Müller, Marius Kloft, 2014, AISTATS.

How to iNNvestigate neural networks' predictions!

Klaus-Robert Müller, Wojciech Samek, Sebastian Lapuschkin, 2018 .

Klaus-Robert Müller, Franziska Horn, K. Müller, 2017, ArXiv.

Unsupervised Detection and Explanation of Latent-class Contextual Anomalies pdf

Shinichi Nakajima, Klaus-Robert Müller, Grégoire Montavon, 2018, ArXiv.

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2001, ICANN.

Accurate Maximum-Margin Training for Parsing With Context-Free Grammars

Klaus-Robert Müller, Mikio L. Braun, Alexander Bauer, 2017, IEEE Transactions on Neural Networks and Learning Systems.

SchNet - A deep learning architecture for molecules and materials.

Kristof T. Schütt, K. Müller, A. Tkatchenko, 2017, The Journal of chemical physics.

From Clustering to Cluster Explanations via Neural Networks

Jacob R. Kauffmann, K. Müller, G. Montavon, 2019, IEEE transactions on neural networks and learning systems.

Evaluating the Visualization of What a Deep Neural Network Has Learned

K. Müller, Alexander Binder, G. Montavon, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

K. Müller, W. Samek, T. Wiegand, 2017, ArXiv.

A Mathematical Programming Approach to the Kernel Fisher Algorithm

K. Müller, S. Mika, G. Rätsch, 2000, NIPS.

Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance

Stefan Haufe, Klaus-Robert Müller, Carmen Vidaurre, 2020, bioRxiv.

Investigating effects of different artefact types on motor imagery BCI

Wojciech Samek, Klaus-Robert Müller, Irene Winkler, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI

K. Müller, S. Fazli, M. Danóczy, 2011, NeuroImage.

Learning invariances with Stationary Subspace Analysis

Motoaki Kawanabe, Klaus-Robert Müller, Frank C. Meinecke, 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

Stationary Subspace Analysis

Klaus-Robert Müller, Frank C. Meinecke, Paul von Bünau, 2009, ICA.

Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

K.-R. Muller, B. Blankertz, F. Losch, 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Brain-computer interface research at the Wadsworth Center.

D J McFarland, J R Wolpaw, T M Vaughan, 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

Understanding Patch-Based Learning of Video Data by Explaining Predictions

Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, 2019, Explainable AI.

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

K. Müller, F. Meinecke, Sven Dähne, 2014, NeuroImage.

Classification of structured validation data using stateless and stateful features

Klaus-Robert Müller, Ralf Pabst, Guido Schwenk, 2019, Comput. Commun..

Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom

Klaus-Robert Müller, Han-Jeong Hwang, Janne Mathias Hahne, 2014, Journal of neural engineering.

Pattern Recognition

Alfred Kobsa, Moni Naor, Josef Kittler, 2015, Lecture Notes in Computer Science.

Eyes-closed hybrid brain-computer interface employing frontal brain activation

Klaus-Robert Müller, Jaeyoung Shin, Han-Jeong Hwang, 2018, PloS one.