Müller, Klaus-Robert

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

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 4,524

Methods for interpreting and understanding deep neural networks

Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 2,665

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,312

Machine Learning Force Fields

Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,647

Explaining nonlinear classification decisions with deep taylor decomposition

Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 1,357

Review of the BCI Competition IV

Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., et al. (2012) · Frontiers in Neuroscience

EEG and Brain-Computer InterfacesMuscle activation and electromyography studies

Cited by 1,288

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

Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage

EEG and Brain-Computer InterfacesNeural and Behavioral Psychology Studies

Cited by 1,138

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

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 1,080

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 849

Neurophysiological predictor of SMR-based BCI performance

Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M., Kübler, A., Müller, K.-R., Curio, G., & Dickhaus, T. (2010) · NeuroImage

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 674

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 388

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Arras, L., Montavon, G., Müller, K.-R., & Samek, W. (2017) · Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Explainable Artificial Intelligence (XAI)Sentiment Analysis and Opinion Mining

Cited by 361

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

Blankertz, B., Tangermann, M., Vidaurre, C., Fazli, S., Sannelli, C., Haufe, S., Maeder, C., Ramsey, L., Sturm, I., Curio, G., et al. (2010) · Frontiers in Neuroscience

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 357

Psychological predictors of SMR-BCI performance

Hammer, E. M., Halder, S., Blankertz, B., Sannelli, C., Dickhaus, T., Kleih, S., Müller, K.-R., & Kübler, A. (2012) · Biological Psychology

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 267

Machine-learning-based coadaptive calibration for brain-computer interfaces

Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Neural Computation

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 207

Explainable Deep One-Class Classification

Liznerski, P., Ruff, L., Vandermeulen, R. A., Franks, B. J., Kloft, M., & Müller, K.-R. (2020) · arXiv

Anomaly Detection Techniques and ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 185

Explanations can be manipulated and geometry is to blame

Dombrowski, A.-K., Alber, M., Anders, C. J., Ackermann, M., Müller, K.-R., & Kessel, P. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 171

Co-adaptive calibration to improve BCI efficiency

Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Journal of Neural Engineering

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 169

Morphological and molecular breast cancer profiling through explainable machine learning

Binder, A., Bockmayr, M., Hägele, M., Wienert, S., Heim, D., Hellweg, K., Ishii, M., Stenzinger, A., Hocke, A., Denkert, C., et al. (2021) · Nature Machine Intelligence

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 156

Combining sparsity and rotational invariance in EEG/MEG source reconstruction

Haufe, S., Nikulin, V. V., Ziehe, A., Müller, K.-R., & Nolte, G. (2008) · NeuroImage

EEG and Brain-Computer InterfacesNumerical methods in inverse problems

Cited by 130

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

Blankertz, B., Krauledat, M., Dornhege, G., Williamson, J., Murray-Smith, R., & Müller, K.-R. (2007) · Lecture Notes in Computer Science

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 106

Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution

Höhne, J., Holz, E., Staiger-Sälzer, P., Müller, K.-R., Kübler, A., & Tangermann, M. (2014) · PLoS ONE

EEG and Brain-Computer InterfacesGaze Tracking and Assistive Technology

Cited by 85

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

RudolfV: A Foundation Model by Pathologists for Pathologists

Dippel, J., Feulner, B., Winterhoff, T., Milbich, T., Tietz, S., Schallenberg, S., Dernbach, G., Kunft, A., Heinke, S., Eich, M.-L., et al. (2024) · arXiv

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 27