1998 · Neural Computation
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
8,084 citationsRead
About
Klaus-Robert Müller is a German computer scientist and physicist, most noted for his work in machine learning and brain–computer interfaces.
Most cited
710 papers in total2015 · PLoS ONE
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
4,604 citationsRead
2017 · Digital Signal Processing
Methods for interpreting and understanding deep neural networks
2,698 citationsRead
2012 · Physical Review Letters
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
2,331 citationsRead
2008 · IEEE Signal Processing Magazine
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
2,090 citationsRead
1997 · Lecture notes in computer science
Kernel principal component analysis
1,941 citationsRead
Newest first
710 papers in total2025
- Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force FieldsJournal of the American Chemical Society · 101 citations
- Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligenceNature Cancer · 67 citations
2024
- Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragmentsScience Advances · 138 citations
- A Euclidean transformer for fast and stable machine learned force fieldsNature Communications · 79 citations
2023
- Accurate global machine learning force fields for molecules with hundreds of atomsScience Advances · 177 citations
- Toward Explainable Artificial Intelligence for Precision PathologyAnnual Review of Pathology Mechanisms of Disease · 138 citations
- AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmarkJournal of the Franklin Institute · 73 citations
- Single-cell gene regulatory network prediction by explainable AINucleic Acids Research · 65 citations
2022
- Inverse design of 3d molecular structures with conditional generative neural networksNature Communications · 189 citations
- Toward Explainable Artificial Intelligence for Regression Models: A methodological perspectiveIEEE Signal Processing Magazine · 122 citations
- From Clustering to Cluster Explanations via Neural NetworksIEEE Transactions on Neural Networks and Learning Systems · 114 citations
- DNA methylation-based classification of sinonasal tumorsNature Communications · 95 citations
- BIGDML—Towards accurate quantum machine learning force fields for materialsNature Communications · 74 citations
- Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brainNeuroImage · 60 citations
2021
- Machine Learning Force FieldsChemical Reviews · 1,575 citations
- Explaining Deep Neural Networks and Beyond: A Review of Methods and ApplicationsProceedings of the IEEE · 1,439 citations
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical SystemsChemical Reviews · 845 citations
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effectsNature Communications · 343 citations
- Pruning by explaining: A novel criterion for deep neural network pruningPattern Recognition · 265 citations
- Higher-Order Explanations of Graph Neural Networks via Relevant WalksIEEE Transactions on Pattern Analysis and Machine Intelligence · 262 citations
- Machine learning of solvent effects on molecular spectra and reactionsChemical Science · 176 citations
- Morphological and molecular breast cancer profiling through explainable machine learningNature Machine Intelligence · 156 citations
- Finding and removing Clever Hans: Using explanation methods to debug and improve deep modelsInformation Fusion · 120 citations
- Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profilingSeminars in Cancer Biology · 88 citations
- Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance MethodologyMachine Learning and Knowledge Extraction · 70 citations
- DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studiesNAR Genomics and Bioinformatics · 66 citations
- Leaf-inspired homeostatic cellulose biosensorsScience Advances · 61 citations
2020
- Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy ConstraintsIEEE Transactions on Neural Networks and Learning Systems · 1,299 citations
- Quantum chemical accuracy from density functional approximations via machine learningNature Communications · 385 citations
- Exploring chemical compound space with quantum-based machine learningNature Reviews Chemistry · 369 citations
Research in numbers
From OpenAlex- article156
- conference paper22
- preprint7
- book chapter7
- review5
- NeuroImage13
- PLoS ONE10
- Journal of Neural Engineering9
- Lecture notes in computer science8
- arXiv (Cornell University)7
- Wojciech Samek38
- Benjamin Blankertz35
- Grégoire Montavon27
- Alexandre Tkatchenko18
- Sebastian Lapuschkin17
Experience and education
Universities
1992 to 2022Karlsruhe Institute of Technology
2015City College of New York
2011 to 2012University of California, Los Angeles
2010University of Insubria
2010University of British Columbia
2008National Yang Ming Chiao Tung University
2000 to 2008University of Potsdam
Industry and labs
1998 to 2024Max Planck Society
2020 to 2023Google (United States)
2021Mayo Clinic