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Grégoire Montavon

Lupe [1]

  • Address:
    Grégoire Montavon
    Sekr. MAR 4-1
    Marchstr. 23
    D-10587 Berlin
    Germany
  • Room: MAR 4.059
  • E-mail: gregoire.montavon@tu-berlin.de [2]

Biography

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.

Research on Explainable AI

Lupe [3]

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) [4] that robustly explains deep neural networks at low computational cost.
  • We have proposed the Deep Taylor Decomposition [5] 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 [6] 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 [7] and GNN-LRP [8], 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 [9] 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 [10]

Teaching

Winter semester 2020/2021:

  • Machine Learning 1 [11]

Summer semester 2020:

  • Machine Learning 2 [12]

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 [13]
    arXiv (September 2020)
  • K Melnyk, S Klus, G Montavon, T Conrad
    GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis [14]
    arXiv (August 2020)
  • J Kauffmann, L Ruff, G Montavon, KR Müller
    The Clever Hans Effect in Anomaly Detection [15]
    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 [16]
    arXiv (June 2020)
  • W Samek, G Montavon, S Lapuschkin, C Anders, KR Müller
    Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond [17]
    arXiv (March 2020)
  • J Kauffmann, M Esders, G Montavon, W Samek, KR Müller
    From Clustering to Cluster Explanations via Neural Networks [18]
    arXiv (June 2019)

 Edited books

  • W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller (Eds.)
    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning [19]
    Springer LNCS 11700 (2019)
  • G Montavon, G Orr, KR Müller (Eds.)
    Neural Networks: Tricks of the Trade, 2nd Edn [20]
    Springer LNCS 7700 (2012)

 Book chapters

  • G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller
    Layer-Wise Relevance Propagation: An Overview [21]
    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 [22]
    in Explainable AI, Springer LNCS 11700 (2019)
  • G Montavon
    Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison [23]
    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 [24]
    in Explainable AI, Springer LNCS 11700 (2019)
  • L Rieger, P Chormai, G Montavon, LK Hansen, KR Müller
    Structuring Neural Networks for More Explainable Predictions [25]
    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 [26]
    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 [27]
    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 [28]
    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 [29]
    The Astrophysical Journal, 118(1):42 (2020)
  • J Kauffmann, KR Müller, G Montavon
    Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models [30]
    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 [31]
    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 [32]
    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 [33]
    Genome Medicine, 10:83 (2018)
  • G Montavon, W Samek, KR Müller
    Methods for Interpreting and Understanding Deep Neural Networks [34]
    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 [35]
    PLOS ONE, 12(8):e0181142 (2017)
  • G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
    Explaining NonLinear Classification Decisions with Deep Taylor Decomposition [36]
    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 [37]
    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 [38]
    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 [39]
    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 [40]
    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 [41]
    IEEE Signal Processing Magazine, 30(4):62-74 (2013)
  • G Montavon, M Braun, KR Müller
    Kernel Analysis of Deep Networks [42]
    Journal of Machine Learning Research, 12:2563-2581 (2011)

Conference Publications

  • G Montavon, KR Müller, M Cuturi
    Wasserstein Training of Restricted Boltzmann Machines [43]
    NIPS 2016
  • F Arbabzadah, G Montavon, KR Müller, W Samek
    Identifying Individual Facial Expressions by Deconstructing a Neural Network [44]
    GCPR 2016
  • A Binder, G Montavon, S Bach, KR Müller, W Samek
    Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers [45]
    ICANN 2016
  • S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek
    Analyzing Classifiers: Fisher Vectors and Deep Neural Networks [46]
    CVPR 2016
  • A Binder, S Bach, G Montavon, KR Müller, W Samek
    Layer-wise Relevance Propagation for Deep Neural Network Architectures [47]
    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 [48]
    NIPS 2012
  • G Montavon, M Braun, KR Müller
    Deep Boltzmann Machines as Feed-Forward Hierarchies [49]
    AISTATS 2012
  • G Montavon, M Braun, KR Müller
    Layer-Wise Analysis of Deep Networks with Gaussian Kernels [50]
    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! [51]
    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 [52]
    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 [53]
    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 [54]
    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 [55]
    ACL Workshop on Representation Learning for NLP (2016)
  • G Montavon, S Bach, A Binder, W Samek, KR Müller
    Deep Taylor Decomposition of Neural Networks [56]
    ICML Workshop on Visualization for Deep Learning (2016)
  • A Binder, W Samek, G Montavon, S Bach, KR Müller
    Analyzing and Validating Neural Networks Predictions [57]
    ICML Workshop on Visualization for Deep Learning (2016)
  • G Montavon, KR Müller
    Neural Networks for Computational Chemistry: Pitfalls and Recommendations [58]
    MRS Online Proceedings Library (2013)
  • G Montavon
    Deep Learning for Spoken Language Identification [59]
    NIPS Workshop on Deep Learning for Speech Recognition and Related Applications (2009)

Theses

  • G Montavon
    On Layer-Wise Representations in Deep Neural Networks [60]
    PhD Thesis, Technische Universität Berlin, Germany (2013)
  • G Montavon
    A Machine Learning Approach to Classification of Low Resolution Histological Samples [61]
    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 [62] (2018)
  • F Horn, L Arras, G Montavon, KR Müller, W Samek
    Exploring text datasets by visualizing relevant words [63] (2017)
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