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Prof. Dr. Grégoire Montavon

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Freie Universität Berlin
Dept. of Mathematics and Computer Science, Institute for Computer Science

Arnimallee 7, 14195 Berlin
https://www.mi.fu-berlin.de/en/inf/groups/ag-KIML/members/Professoren/Montavon.html

© Montavon

Prof. Dr. Grégoire Montavon

Research Group Lead

 

 

Prof. Dr. Grégoire Montavon is a Guest Professor at the Freie Universität Berlin and a Research Group Lead in the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received a Masters degree in Communication Systems from École Polytechnique Fédérale de Lausanne in 2009, and a Ph.D. degree in Machine Learning from the Technische Universität Berlin in 2013.

His current research focuses on methods of explainable AI (XAI) for deep neural networks and unsupervised learning, and on closing the gap between existing XAI methods and practical desiderata. This includes using XAI to build more trustworthy machine learning models and using XAI to extract actionable insights from complex datasets.

Jointly with his colleagues, he contributed to Layer-Wise Relevance Propagation (LRP), an efficient method for explaining the predictions of large deep neural networks. He and his co-authors also contributed to the "Neuralization-Propagation" framework which rewrites popular unsupervised learning models as functionally equivalent neural networks for explainability purposes, and higher-order extensions of LRP (BiLRP and GNN-LRP) which enable the identification of joint features contributions in models with product structures.

  • 2013 Dimitris N. Chorafas Award
  • 2020 Pattern Recognition Best Paper Award
  • 2022 Digital Signal Processing Best Paper Award

  • Explainable AI
  • Machine Learning
  • Data Science

Scholar,  European Laboratory for Learning and Intelligent Systems (ELLIS), 2021–now  

Frederick Klauschen, Jonas Dippel, Philipp Keyl, Philipp Jurmeister, Michael Bockmayr, Andreas Mock, Oliver Buchstab, Maximilian Alber, Lukas Ruff, Grégoire Montavon & Klaus-Robert Müller

Explainable artificial intelligence in pathology

February 05 , 2024
https://doi.org/10.1007/s00292-024-01308-7

Frederick Klauschen, Jonas Dippel, Philipp Keyl, Philipp Jurmeister, Michael Bockmayr, Andreas Mock, Oliver Buchstab, Maximilian Alber, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller

Toward Explainable Artificial Intelligence for Precision Pathology

October 23 , 2023
https://doi.org/10.1146/annurev-pathmechdis-051222-113147

Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon

Learning domain invariant representations by joint Wasserstein distance minimization.

July 31 , 2023
https://doi.org/10.1016/j.neunet.2023.07.028

Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

June 17 , 2023
https://doi.ieeecomputersociety.org/10.1109/CVPR52729.2023.01549

Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus Robert Müller, Shinichi Nakajima

Relevant Walk Search for Explaining Graph Neural Networks

April 24 , 2023
https://proceedings.mlr.press/v202/xiong23b.html

News
BIFOLD Update| Feb 23, 2021

2020 pattern recognition best paper award

A team of scientists from TU Berlin, Fraunhofer Heinrich Hertz Institute (HHI) and University of Oslo has jointly received the 2020 “Pattern Recognition Best Paper Award” and “Pattern Recognition Medal” of the international scientific journal Pattern Recognition. The award committee honored the publication “Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition” by Dr. Grégoire Montavon and Prof. Dr. Klaus-Robert Müller from TU Berlin, Prof. Dr. Alexander Binder from University of Oslo, as well as Dr. Wojciech Samek and Dr. Sebastian Lapuschkin from HHI.

Machine Learning| Sep 23, 2020

Using machine learning to combat the coronavirus

A joint team of researchers from TU Berlin and the University of Luxembourg is exploring why a spike protein in the SARS-CoV-2 virus is able to bind much more effectively to human cells than other coronaviruses. Google.org is funding the research with 125,000 US dollars.