Inhalt des Dokuments
Grégoire Montavon
- Research Associate | TU Berlin
Junior Research Group Lead | BIFOLD
- Address:
Grégoire Montavon
Sekr. MAR 4-1
Marchstr. 23
D-10587 Berlin
Germany - Room: MAR 4.059
- E-mail: gregoire.montavon@tu-berlin.de
Biography
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.
Research on Explainable AI
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
Research Highlights
- 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
Publications
Preprints
- J Kauffmann, M Esders, G Montavon, W Samek, KR Müller
From Clustering to Cluster Explanations via Neural Networks
CoRR abs/1906.07633, v2 (2021) - 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
- 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 AI for Regression Models
Signal Processing Magazine, accepted (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)