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Grégoire Montavon

Grégoire Montavon

D-Index & Metrics

Computer Science

D-Index
34
Citations
20317
World Ranking
11865
National Ranking
585

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
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
Marco Cuturi
Marco Cuturi École Nationale de la Statistique et de l'Administration Économique

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