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Klaus-Robert Müller

  1. Methods for Interpreting and Understanding Deep Neural Networks
    2017/06/24 by Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller +1 · 1 voice · 130 citations
    Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #Neural Networks and Applications #cs.LG #stat.ML
  2. Getting aligned on representational alignment
    2023/10/18 by Ilia Sucholutsky, Lukas Muttenthaler, Sucholutsky, Ilia +66 · 10 voices · 45 citations
    Biochemistry, Genetics and Molecular Biology · Materials Science · Neuroscience · #Bioinformatics and Genomic Networks #Machine Learning in Materials Science #Neural dynamics and brain function #cs.AI #cs.LG #cs.NE #q-bio.NC
  3. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
    2017/08/28 by Wojciech Samek, Thomas Wiegand, Samek, Wojciech +4 · 1 voice · 48 citations
    Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.CY #cs.NE #stat.ML
  4. Machine Learning for Molecular Simulation
    2020/02/24 by Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller +1 · 53 citations
    Materials Science · Physics and Astronomy · #Block Copolymer Self-Assembly #Machine Learning in Materials Science #Quantum many-body systems
  5. Fairwashing Explanations with Off-Manifold Detergent
    2020/07/20 by Christopher J. Anders, Anders, Christopher J., Pasliev, Plamen +6 · 7 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification
  6. MambaLRP: Explaining Selective State Space Sequence Models
    2024/06/11 by Farnoush Rezaei Jafari, Jafari, Farnoush Rezaei, Grégoire Montavon +6 · 1 voice · 9 citations
    Computer Science · Decision Sciences · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Simulation Techniques and Applications #cs.AI #cs.LG #stat.ML
  7. Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
    2020/03/17 by Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin +2 · 1 voice · 1 citation
    Computer Science · Mathematics · #cs.LG #cs.AI #cs.CV #cs.NE #stat.ML
  8. Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
    2025/01/01 by Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva +23 · 1 voice · 7 citations
    Materials Science · Computer Science · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Nuclear Materials and Properties
  9. Interpretable Deep Neural Networks for Single-Trial EEG Classification
    2016/04/27 by Irene Sturm, Sebastian Lapuschkin, Sturm, Irene +6 · 1 citation
    Neuroscience · #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
  10. Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
    2025/08/31 by Adil Kabylda, J. Thorben Frank, Sergio Suarez Dou +6 · 1 voice · 6 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics
  11. Mark My Words: Dangers of Watermarked Images in ImageNet
    2023/03/09 by Kirill Bykov, Bykov, Kirill, Klaus‐Robert Müller +4 · 1 voice
    Computer Science · #Advanced Steganography and Watermarking Techniques #Digital Media Forensic Detection #Generative Adversarial Networks and Image Synthesis #cs.AI #cs.CR #cs.CV #cs.LG
  12. The utility of explainable AI for MRI analysis: Relating model predictions to neuroimaging features of the aging brain
    2025/01/01 by S. Hofmann, Ole Goltermann, Nico Scherf +6 · 1 voice · 2 citations
    Computer Science · Medicine · #Explainable Artificial Intelligence (XAI) #Machine Learning in Healthcare #Radiomics and Machine Learning in Medical Imaging
  13. xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
    2024/11/12 by Marvin Sextro, Gabriel Dernbach, Sextro, Marvin +13 · 1 citation
    Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Bioinformatics and Genomic Networks #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging
  14. Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations
    2026/07/31 by Johannes Maeß, Leon Werner, J. Thorben Frank +5
    Computer Science · #cs.LG #cs.AI