Grégoire Montavon

Grégoire Montavon is a Research Associate in the Machine Learning Group at the Technische Universität Berlin, and Junior 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 research interests include explainable AI, deep neural networks, and unsupervised learning.

Grégoire Montavon's current research is on advancing the foundations and algorithms of explainable AI (XAI) in the context of deep neural networks. One particular focus is on closing the gap between existing XAI methods and practical desiderata. Examples include using XAI to build more trustworthy and autonomous machine learning models and using XAI to model the behavior of complex real-world systems so that the latter become meaningfully actionable.

See also: https://bifold.berlin/research/#dnn

• The Layer-Wise Relevance Propagation (LRP) method. LRP robustly and efficiently explains deep neural network predictions in terms of input features.
• The Deep Taylor Decomposition framework, which connects mathematically the LRP procedure to Taylor expansions and leads to a systematic way of designing LRP propagation rules.
• The "Neuralization-Propagation" framework for explaining non-neural network models, which consists of rewriting non neural network models (e.g. one-class SVMs, K-means) as strictly equivalent neural networks, and using the neural network representation and LRP to produce explanations.
• Higher-order extensions of LRP (BiLRP and GNN-LRP). They enable to identify joint feature contributions in models such as graph neural networks or deep similarity models.
• Method to systematically verify that trained neural networks predict as expected and are not subject to a Clever Hans effect.

These contributions are described in more details in our recent review paper on Explainable AI.

See also: www.heatmapping.org

Winter semester 2021/2022:

• Machine Learning 1

Summer semester 2022:

• Machine Learning 2

Preprints

• L Andéol, Y Kawakami, Y Wada, T Kanamori, KR Müller, G Montavon
  Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
  CoRR abs/2106.04923, v1 (2021)
• J Kauffmann, L Ruff, G Montavon, KR Müller
  The Clever Hans Effect in Anomaly Detection
  CoRR abs/2006.10609, v1 (2020)

 Edited books

• W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller (Eds.)
  Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
  Springer LNCS 11700 (2019)
• G Montavon, G Orr, KR Müller (Eds.)
  Neural Networks: Tricks of the Trade, 2nd Edn
  Springer LNCS 7700 (2012)

 Book chapters

• G Montavon, J Kauffmann, W Samek, KR Müller
  Explaining the Predictions of Unsupervised Learning Models
  in xxAI - Beyond Explainable Artificial Intelligence, Springer LNAI 13200 (2022)
• W Samek, L Arras, A Osman, G Montavon, KR Müller
  Explaining the Decisions of Convolutional and Recurrent Neural Networks
  in: Mathematical Aspects of Deep Learning (2022), to appear
• G Montavon
  Introduction to Neural Networks
  in Machine Learning Meets Quantum Physics, 37-62 (2020)
• G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller
  Layer-Wise Relevance Propagation: An Overview
  in Explainable AI, Springer LNCS 11700 (2019)
• L Arras, J Arjona, M Widrich, G Montavon, M Gillhofer, KR Müller, S Hochreiter, W Samek
  Explaining and Interpreting LSTMs
  in Explainable AI, Springer LNCS 11700 (2019)
• G Montavon
  Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison
  in Explainable AI, Springer LNCS 11700 (2019)
• C Anders, G Montavon, W Samek, KR Müller
  Understanding Patch-Based Learning of Video Data by Explaining Predictions
  in Explainable AI, Springer LNCS 11700 (2019)
• L Rieger, P Chormai, G Montavon, LK Hansen, KR Müller
  Structuring Neural Networks for More Explainable Predictions
  in Explainable and Interpretable Models in Computer Vision and Machine Learning, pp 115-131, Springer SSCML (2018)
• G Montavon, KR Müller
  Deep Boltzmann Machines and the Centering Trick
  in Neural Networks: Tricks of the Trade, 2nd Edn, pp 621-637, Springer LNCS, vol. 7700 (2012)

Journal publications

• J Kauffmann, M Esders, L Ruff, G Montavon, W Samek, KR Müller
  From Clustering to Cluster Explanations via Neural Networks
  IEEE Transactions on Neural Networks and Learning Systems, early access (2022)
• H El-Hajj, M Zamani, J Büttner, J Martinetz, O Eberle, N Shlomi, A Siebold, G Montavon, KR Müller, H Kantz, M Valleriani
  An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning
  Datenbank-Spektrum (2022)
• S Letzgus, P Wagner, J Lederer, W Samek, KR Müller, G Montavon
  Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective
  Signal Processing Magazine, 39(4):40-58 (2022)
• O Eberle, J Büttner, F Kräutli, KR Müller, M Valleriani, G Montavon
  Building and Interpreting Deep Similarity Models
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3):1149-1161 (2022)
• T Schnake, O Eberle, J Lederer, S Nakajima, K T. Schütt, KR Müller, G Montavon
  Higher-Order Explanations of Graph Neural Networks via Relevant Walks
  IEEE Transactions on Pattern Analysis and Machine Intelligence, early access (2021)
• S Agarwal, N Tosi, P Kessel, D Breuer, G Montavon
  Deep learning for surrogate modeling of two-dimensional mantle convection
  Physical Review Fluids 6 (11), 113801 (2021)
• S Agarwal, N Tosi, P Kessel, S Padovan, D Breuer, G Montavon
  Toward constraining Mars' thermal evolution using machine learning
  Earth and Space Science 8 (4), e2020EA001484 (2021)
• W Samek, G Montavon, S Lapuschkin, C Anders, KR Müller
  Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
  Proceedings of the IEEE 109(3):247-278 (2021)
• L Ruff, J Kauffmann, R Vandermeulen, G Montavon, W Samek, M Kloft, T Dietterich, KR Müller
  A Unifying Review of Deep and Shallow Anomaly Detection
  Proceedings of the IEEE 109(5):756-795 (2021)
• K Melnyk, S Klus, G Montavon, T Conrad
  GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
  Applied Network Science 5(1):96 (2020)
• S Agarwal, N Tosi, D Breuer, S Padovan, P Kessel, G Montavon
  A machine-learning-based surrogate model of Mars’ thermal evolution
  Geophysical Journal International, ggaa234 (2020)
• P Baumeister, S Padovan, N Tosi, G Montavon, N Nettelmann, J MacKenzie, M Godolt
  Machine learning inference of the interior structure of low-mass exoplanets
  The Astrophysical Journal, 118(1):42 (2020)
• J Kauffmann, KR Müller, G Montavon
  Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
  Pattern Recognition, 107198 (2020)
• P Jurmeister, M Bockmayr, P Seegerer, T Bockmayr,D Treue, G Montavon, C Vollbrecht, A Arnold, D Teichmann, K Bressem, U Schüller, Mv Laffert, KR Müller, D Capper, F Klauschen
  Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases
  Science Translational Medicine 11(509):eaaw8513 (2019)
• S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, KR Müller
  Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
  Nature Communications 10:1096 (2019)
• D Heim, G Montavon, P Hufnagl, KR Müller, F Klauschen
  Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers
  Genome Medicine, 10:83 (2018)
• G Montavon, W Samek, KR Müller
  Methods for Interpreting and Understanding Deep Neural Networks
  Digital Signal Processing, 73:1-15 (2018)
• L Arras, F Horn, G Montavon, KR Müller, W Samek
  "What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
  PLOS ONE, 12(8):e0181142 (2017)
• G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
  Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
  Pattern Recognition, 65:211–222 (2017)
• W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller
  Evaluating the Visualization of What a Deep Neural Network has Learned
  IEEE Transactions on Neural Networks and Learning Systems, 28(11):2660-2673 (2016)
• S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek
  On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation
  PLOS ONE, 10(7):e0130140 (2015)
• G Montavon, M Rupp, V Gobre, A Vazquez-Mayagoitia, K Hansen, A Tkatchenko, KR Müller, OAv Lilienfeld
  Machine Learning of Molecular Electronic Properties in Chemical Compound Space
  New Journal of Physics, 15 095003 (2013)
• K Hansen, G Montavon, F Biegler, S Fazli, M Rupp, M Scheffler, OAv Lilienfeld, A Tkatchenko, KR Müller
  Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
  Journal of Chemical Theory and Computation, 9(8):3404-3419 (2013)
• G Montavon, M Braun, T Krueger, KR Müller
  Analyzing Local Structure in Kernel-based Learning: Explanation, Complexity and Reliability Assessment
  IEEE Signal Processing Magazine, 30(4):62-74 (2013)
• G Montavon, M Braun, KR Müller
  Kernel Analysis of Deep Networks
  Journal of Machine Learning Research, 12:2563-2581 (2011)

Conference Publications

• P Xiong, T Schnake, G Montavon, KR Müller, S Nakaijma
  Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
  ICML 2022, accepted
• A Ali, T Schnake, O Eberle, G Montavon, KR Müller, L Wolf. XAI for Transformers: Better Explanations through Conservative Propagation
  ICML 2022, accepted
  
• G Montavon, KR Müller, M Cuturi
  Wasserstein Training of Restricted Boltzmann Machines
  NIPS 2016
• F Arbabzadah, G Montavon, KR Müller, W Samek
  Identifying Individual Facial Expressions by Deconstructing a Neural Network
  GCPR 2016
• A Binder, G Montavon, S Bach, KR Müller, W Samek
  Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
  ICANN 2016
• S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek
  Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
  CVPR 2016
• A Binder, S Bach, G Montavon, KR Müller, W Samek
  Layer-wise Relevance Propagation for Deep Neural Network Architectures
  ICISA 2016
• G Montavon, K Hansen, S Fazli, M Rupp, F Biegler, A Ziehe, A Tkatchenko, OAv Lilienfeld, KR Müller
  Learning Invariant Representations of Molecules for Atomization Energy Prediction
  NIPS 2012
• G Montavon, M Braun, KR Müller
  Deep Boltzmann Machines as Feed-Forward Hierarchies
  AISTATS 2012
• G Montavon, M Braun, KR Müller
  Layer-Wise Analysis of Deep Networks with Gaussian Kernels
  NIPS 2010

Software Publications

• M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, W Samek, KR Müller, S Dähne, PJ Kindermans
  iNNvestigate neural networks!
  Journal of Machine Learning Research, Software Track (2019)
• S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek
  The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks
  Journal of Machine Learning Research, Software Track (2016)

Workshop Publications

• L Arras, G Montavon, KR Müller, W Samek
  Explaining Recurrent Neural Network Predictions in Sentiment Analysis
  EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (2017)
• W Samek, G Montavon, A Binder, S Lapuschkin, KR Müller
  Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation
  NIPS Workshop on Interpretable ML for Complex Systems (2016)
• L Arras, F Horn, G Montavon, KR Müller, W Samek
  Explaining Predictions of Non-Linear Classifiers in NLP
  ACL Workshop on Representation Learning for NLP (2016)
• G Montavon, S Bach, A Binder, W Samek, KR Müller
  Deep Taylor Decomposition of Neural Networks
  ICML Workshop on Visualization for Deep Learning (2016)
• A Binder, W Samek, G Montavon, S Bach, KR Müller
  Analyzing and Validating Neural Networks Predictions
  ICML Workshop on Visualization for Deep Learning (2016)
• G Montavon, KR Müller
  Neural Networks for Computational Chemistry: Pitfalls and Recommendations
  MRS Online Proceedings Library (2013)
• G Montavon
  Deep Learning for Spoken Language Identification
  NIPS Workshop on Deep Learning for Speech Recognition and Related Applications (2009)

Theses

• G Montavon
  On Layer-Wise Representations in Deep Neural Networks
  PhD Thesis, Technische Universität Berlin, Germany (2013)
• G Montavon
  A Machine Learning Approach to Classification of Low Resolution Histological Samples
  Master Thesis, École Polytechnique Fédérale de Lausanne, Switzerland (2009)

Unpublished

• J Kauffmann, G Montavon, LA Lima, S Nakajima, KR Müller, N Görnitz
  Unsupervised Detection and Explanation of Latent-class Contextual Anomalies (2018)
• F Horn, L Arras, G Montavon, KR Müller, W Samek
  Exploring text datasets by visualizing relevant words (2017)