Inhalt des Dokuments
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
- 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) - 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
- 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 (2021), accepted - 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 (2021) - K Melnyk, S Klus, G Montavon, T Conrad
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
Applied Network Science (2020) - 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)