Grégoire Montavon

Grégoire Montavon 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. He is currently a Research Associate in the Machine Learning Group at TU Berlin. His research interests are in the areas of explainable AI, deep neural networks and machine learning.

The goal of this project is to develop methods to explain the decisions (e.g. classifications) of machine learning models in terms of input variables (i.e. which input variables are relevant for the model's decision).

• Explanations in deep neural networks is particularly difficult due to the high level of nonlinearity of the decision function. We have proposed a procedure called Layer-Wise Relevance Propagation (LRP) that robustly explains deep neural networks at low computational cost.
• We have proposed the Deep Taylor Decomposition framework, which connects mathematically the LRP procedure to Taylor expansions. Specifically, the LRP algorithm can be interpreted as a collection of Taylor expansions performed locally in the network, and this connection allows us to systematically bring LRP to new architectures with different layer types.
• In order to apply explanation techniques beyond neural networks, we have proposed a Neuralization-Propagation approach which consists of (1) rewriting non neural network models such as kernel-based anomaly detection or k-means clustering as strictly equivalent neural networks, and (2) use the neural network representation and LRP to produce the explanation. Unlike surrogate modeling approaches, our neuralization-propagation approach does not require any retraining.
• Because a simple attribution of the prediction on the input features may not be sufficient for certain tasks (e.g. predicting similarity or predicting graphs), we have developed extensions for these cases: BiLRP and GNN-LRP, which produce explanations in terms of pairs of input features or in terms of walks into the graph.
• The methods we have developed can serve to verify that trained neural networks predict as expected (i.e. are not subject to a Clever Hans effect), and we are also starting to apply our method to address challenges in the area of digital humanities and quantum chemistry.

Demos, tutorials, software, and list of publications are available at www.heatmapping.org

Winter semester 2020/2021: Machine Learning 1

Summer semester 2020: Machine Learning 2

Preprints

• K Melnyk, S Klus, G Montavon, T Conrad
  GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
  arXiv (August 2020)
• J Kauffmann, L Ruff, G Montavon, KR Müller
  The Clever Hans Effect in Anomaly Detection
  arXiv (June 2020)
• T Schnake, O Eberle, J Lederer, S Nakajima, K T. Schütt, KR Müller, G Montavon
  XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
  arXiv (June 2020)
• W Samek, G Montavon, S Lapuschkin, C Anders, KR Müller
  Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond
  arXiv (March 2020)
• J Kauffmann, M Esders, G Montavon, W Samek, KR Müller
  From Clustering to Cluster Explanations via Neural Networks
  arXiv (June 2019)

 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, 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

• 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 (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
• 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

• 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)