World's Best Scientists 2026 revealed!
Grégoire Montavon

Grégoire Montavon

D-Index & Metrics

Computer Science

D-Index
34
Citations
20317
World Ranking
11863
National Ranking
586

Grégoire Montavon publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Grégoire Montavon sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 100 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Grégoire Montavon D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Grégoire Montavon sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Grégoire Montavon is currently affiliated with Freie Universität Berlin in Germany. Their research primarily focuses on the field of Computer Science, with significant contributions across various subfields including Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Signal Processing, Molecular Biology, and Computer Vision and Pattern Recognition.

The scientist's work extensively covers key topics such as Explainable Artificial Intelligence (XAI), Machine Learning and Data Classification, Anomaly Detection Techniques and Applications, Adversarial Robustness in Machine Learning, Radiomics and Machine Learning in Medical Imaging, Time Series Analysis and Forecasting, and AI in cancer detection.

Montavon's publication record demonstrates involvement in notable venues, showing versatility in communicating research across related fields. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Pattern Recognition
  • Information Fusion
  • SSRN Electronic Journal

Some recent papers authored or co-authored by Montavon include:

  • Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, Proceedings of the IEEE)
  • Higher-Order Explanations of Graph Neural Networks via Relevant Walks (2021, IEEE Transactions on Pattern Analysis and Machine Intelligence)
  • From Clustering to Cluster Explanations via Neural Networks (2022, IEEE Transactions on Neural Networks and Learning Systems)
  • Towards explaining anomalies: A deep Taylor decomposition of one-class models (2020, Pattern Recognition)
  • Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective (2022, IEEE Signal Processing Magazine)

The scientist frequently collaborates with several co-authors, reflecting ongoing partnerships in research projects. These frequent collaborators include:

  • Klaus-Robert Müller
  • Wojciech Samek
  • Oliver Eberle
  • Frederick Klauschen
  • Klaus-Robert Müller

Best Publications

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

    Sebastian Bach;Alexander Binder;Grégoire Montavon;Frederick Klauschen

  • Methods for interpreting and understanding deep neural networks

    Grégoire Montavon;Wojciech Samek;Klaus Robert Müller;Klaus Robert Müller;Klaus Robert Müller

  • Neural Networks: Tricks of the Trade

    Unknown

  • Explaining nonlinear classification decisions with deep Taylor decomposition

    Grégoire Montavon;Sebastian Lapuschkin;Alexander Binder;Wojciech Samek

  • A Unifying Review of Deep and Shallow Anomaly Detection

    Lukas Ruff;Jacob R. Kauffmann;Robert A. Vandermeulen;Gregoire Montavon

  • Unmasking Clever Hans predictors and assessing what machines really learn.

    Sebastian Lapuschkin;Stephan Wäldchen;Alexander Binder;Grégoire Montavon

  • Evaluating the Visualization of What a Deep Neural Network Has Learned

    Wojciech Samek;Alexander Binder;Gregoire Montavon;Sebastian Lapuschkin

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

    Wojciech Samek;Gregoire Montavon;Sebastian Lapuschkin;Christopher J. Anders

  • Explainable ai – preface

    Wojciech Samek;Grégoire Montavon;Andrea Vedaldi;Lars Kai Hansen

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

    Wojciech Samek;Grégoire Montavon;Sebastian Lapuschkin;Christopher J. Anders

  • Layer-Wise Relevance Propagation: An Overview

    Grégoire Montavon;Alexander Binder;Sebastian Lapuschkin;Wojciech Samek

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

    Katja Hansen;Grégoire Montavon;Franziska Biegler;Siamac Fazli

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

    Grégoire Montavon;Matthias Rupp;Vivekanand Gobre;Alvaro Vazquez-Mayagoitia

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

    Alexander Binder;Grégoire Montavon;Sebastian Lapuschkin;Klaus-Robert Müller;Klaus-Robert Müller

  • Explaining Recurrent Neural Network Predictions in Sentiment Analysis

    Leila Arras;Grégoire Montavon;Klaus-Robert Müller;Wojciech Samek

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

    Leila Arras;Franziska Horn;Grégoire Montavon;Klaus Robert Müller;Klaus Robert Müller;Klaus Robert Müller

  • Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

    Sebastian Lapuschkin;Alexander Binder;Gregoire Montavon;Klaus-Robert Muller

  • Higher-Order Explanations of Graph Neural Networks via Relevant Walks.

    Thomas Schnake;Oliver Eberle;Jonas Lederer;Shinichi Nakajima

  • iNNvestigate Neural Networks

    Maximilian Alber;Sebastian Lapuschkin;Philipp Seegerer;Miriam Hägele

  • Layer-Wise Relevance Propagation for Deep Neural Network Architectures

    Alexander Binder;Sebastian Bach;Gregoire Montavon;Klaus-Robert Müller

  • Learning Invariant Representations of Molecules for Atomization Energy Prediction

    Grégoire Montavon;Katja Hansen;Siamac Fazli;Matthias Rupp

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

    Philipp Jurmeister;Philipp Jurmeister;Philipp Jurmeister;Michael Bockmayr;Michael Bockmayr;Philipp Seegerer;Teresa Bockmayr

  • Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective

    Unknown

  • Deep Boltzmann Machines and the Centering Trick

    Grégoire Montavon;Klaus Robert Müller;Klaus Robert Müller

  • The LRP toolbox for artificial neural networks

    Sebastian Lapuschkin;Alexander Binder;Grégoire Montavon;Klaus-Robert Müller

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

    Grégoire Montavon;Matthias Rupp;Vivekanand Gobre;Alvaro Vazquez-Mayagoitia

  • Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

    Alexander Binder;Grégoire Montavon;Sebastian Bach;Klaus-Robert Müller

  • Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

    Sebastian Bach;Alexander Binder;Grégoire Montavon;Klaus-Robert Müller

Frequent Co-Authors

Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Wojciech Samek
Wojciech Samek Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
Nicola Tosi
Nicola Tosi German Aerospace Center
Doris Breuer
Doris Breuer German Aerospace Center
Alexandre Tkatchenko
Alexandre Tkatchenko University of Luxembourg
Matthias Rupp
Matthias Rupp Luxembourg Institute of Science and Technology
O. Anatole von Lilienfeld
O. Anatole von Lilienfeld University of Toronto
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Lars Kai Hansen
Lars Kai Hansen Technical University of Denmark
Sepp Hochreiter
Sepp Hochreiter Johannes Kepler University of Linz

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