### Page Content

### to Navigation

# Grégoire Montavon

- 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 senior researcher in the Machine Learning Group at the Technische Universität Berlin, and 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

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

## Publications

**Preprints**

- 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

CoRR abs/2009.11732 (2020) - J Kauffmann, L Ruff, G Montavon, KR Müller

The Clever Hans Effect in Anomaly Detection

CoRR abs/2006.10609 (2020) - 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

CoRR abs/2006.03589 (2020) - W Samek, G Montavon, S Lapuschkin, C Anders, KR Müller

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond

CoRR abs/2003.07631 (2020) - J Kauffmann, M Esders, G Montavon, W Samek, KR Müller

From Clustering to Cluster Explanations via Neural Networks

CoRR abs/1906.07633 (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

Introduction to Neural Networks

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**

- K Melnyk, S Klus, G Montavon, T Conrad

GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis

Applied Network Science (accepted) - 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 (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**

- 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)