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Grégoire Montavon

Room: 4.059

gregoire.montavon@tu-berlin.de [1]

Sekr. MAR 4-1
Marchstr. 23
D-10587 Berlin

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 neural networks, machine learning and data analysis.

Research

2015-2019: Explaining machine learning decisions

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). Techniques that have been developed include the deep Taylor decomposition, that operates by performing a first-order Taylor expansion at each neuron of a deep network. These expansions are then recombined to produce a relevance propagation algorithm, where the model's decision is redistributed from layer to layer until the input is reached. www.heatmapping.org [2]

2012-2013: Machine learning in the chemical compound space

The project consists of using machine learning to dramatically accelerate the calculation of molecular properties (e.g. atomization energy or polarizability) for large collections of molecules. These properties are usually obtained from complex physics simulations, that must be performed for each molecule individually. The machine learning speedup is mainly based on exploiting similarity between molecules, and on automatically extracting a more compact task representation. www.quantum-machine.org [3]

Teaching

Summer semester 2019

Teaching assistant for Machine Learning 2 [4]
Organizer for Deep Neural Networks [5]

Winter semester 2018/19

Teaching assistant for Machine Learning 1 [6]
Organizer for Seminar Big Data & Scalable ML [7]

Publications

Preprints
2018-08-13	M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, W Samek, KR Müller, S Dähne, PJ Kindermans. iNNvestigate neural networks! [8]
2018-06-29	J Kauffmann, G Montavon, LA Lima, S Nakajima, KR Müller, N Görnitz.Unsupervised Detection and Explanation of Latent-class Contextual Anomalies [9]
2018-06-11	C Anders, G Montavon, W Samek, KR Müller. Understanding Patch-Based Learning by Explaining Predictions [10]
2018-05-16	J Kauffmann, KR Müller, G Montavon. Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models [11]
2017-07-17	F Horn, L Arras, G Montavon, KR Müller, W Samek. Exploring text datasets by visualizing relevant words [12]

Edited Books
2012	G Montavon, G Orr, KR Müller. Neural Networks: Tricks of the Trade, 2nd Edn [13], Springer LNCS

Book Chapters
2018	L Rieger, P Chormai, G Montavon, LK Hansen, KR Müller. Structuring Neural Networks for More Explainable Predictions [14] in Explainable and Interpretable Models in Computer Vision and Machine Learning, pp 115-131, Springer SSCML
2012	G Montavon, KR Müller. Deep Boltzmann Machines and the Centering Trick [15] in Neural Networks: Tricks of the Trade, 2nd Edn, pp 621-637, Springer LNCS, vol. 7700

Journal Publications
2019	S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, KR Müller. Unmasking Clever Hans Predictors and Assessing What Machines Really Learn [16] Nature Communications 10:1096
2018	D Heim, G Montavon, P Hufnagl, KR Müller, F Klauschen. Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers [17] Genome Medicine, 10:83
2018	G Montavon, W Samek, KR Müller. Methods for Interpreting and Understanding Deep Neural Networks [18] Digital Signal Processing, 73:1-15
2017	L Arras, F Horn, G Montavon, KR Müller, W Samek. "What is Relevant in a Text Document?": An Interpretable Machine Learning Approach [19] PLOS ONE, 12(8):e0181142
2017	G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller. Explaining NonLinear Classification Decisions with Deep Taylor Decomposition [20] Pattern Recognition, 65:211–222
2016	W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller. Evaluating the Visualization of What a Deep Neural Network has Learned [21] IEEE Transactions on Neural Networks and Learning Systems, 28(11):2660-2673
2015	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 [22] PLOS ONE, 10(7):e0130140
2013	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 [23] 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 [24] 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 [25] IEEE Signal Processing Magazine, 30(4):62-74
2011	G Montavon, M Braun, KR Müller. Kernel Analysis of Deep Networks [26] Journal of Machine Learning Research, 12:2563-2581

Conference Publications
2016	G Montavon, KR Müller, M Cuturi. Wasserstein Training of Restricted Boltzmann Machines [27] Advances in Neural Information Processing Systems (NIPS) (code [28])
2016	F Arbabzadah, G Montavon, KR Müller, W Samek. Identifying Individual Facial Expressions by Deconstructing a Neural Network [29] German Conference on Pattern Recognition (GCPR)
2016	A Binder, G Montavon, S Bach, KR Müller, W Samek. Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers [30] International Conference on Artificial Neural Networks (ICANN)
2016	S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek. Analyzing Classifiers: Fisher Vectors and Deep Neural Networks [31] IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016	A Binder, S Bach, G Montavon, KR Müller, W Samek. Layer-wise Relevance Propagation for Deep Neural Network Architectures [32] International Conference on Information Science and Applications (ICISA)
2012	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 [33] Advances in Neural Information Processing Systems (NIPS)
2012	G Montavon, M Braun, KR Müller. Deep Boltzmann Machines as Feed-Forward Hierarchies [34] International Conference on Artificial Intelligence and Statistics (AISTATS)
2010	G Montavon, M Braun, KR Müller. Layer-Wise Analysis of Deep Networks with Gaussian Kernels [35] Advances in Neural Information Processing Systems (NIPS)

Other Publications
2017	L Arras, G Montavon, KR Müller, W Samek. Explaining Recurrent Neural Network Predictions in Sentiment Analysis [36] EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
2016	W Samek, G Montavon, A Binder, S Lapuschkin, KR Müller. Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation [37] 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 [38] ACL Workshop on Representation Learning for NLP
2016	G Montavon, S Bach, A Binder, W Samek, KR Müller. Deep Taylor Decomposition of Neural Networks [39] ICML Workshop on Visualization for Deep Learning
2016	A Binder, W Samek, G Montavon, S Bach, KR Müller. Analyzing and Validating Neural Networks Predictions [40] ICML Workshop on Visualization for Deep Learning
2016	S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks [41] Journal of Machine Learning Research, Software Track (JMLR/MLOSS)
2013	G Montavon, KR Müller. Neural Networks for Computational Chemistry: Pitfalls and Recommendations [42] MRS Online Proceedings Library
2009	G Montavon. Deep Learning for Spoken Language Identification [43] NIPS Workshop on Deep Learning for Speech Recognition and Related Applications

Theses
2013	G Montavon. On Layer-Wise Representations in Deep Neural Networks [44] PhD Thesis, Technische Universität Berlin, Germany
2009	G Montavon. A Machine Learning Approach to Classification of Low Resolution Histological Samples [45] Master Thesis, École Polytechnique Fédérale de Lausanne, Switzerland

------ Links: ------

[1] https://www.tu-berlin.de/allgemeine_seiten/contact/
parameter/en/id/66940/?no_cache=1&ask_mail=XJKrvgAC
A9FVHPCLJRtrvrhWrKO9zu6Fi0ec5pyWG7AvcTLhowrV8Q%3D%3D&am
p;ask_name=GREGOIRE%20MONTAVON

[2] http://www.heatmapping.org/

[3] http://www.quantum-machine.org/

[4] http://wiki.ml.tu-berlin.de/wiki/Main/SS19_ML2

[5] http://wiki.ml.tu-berlin.de/wiki/Main/SS19_DNN

[6] https://wiki.ml.tu-berlin.de/wiki/Main/WS18_Maschin
ellesLernen1

[7] http://wiki.ml.tu-berlin.de/wiki/Main/WS18_BigData

[8] https://arxiv.org/abs/1808.04260

[9] https://arxiv.org/abs/1806.11326

[10] https://arxiv.org/abs/1806.06926

[11] https://arxiv.org/abs/1805.06230

[12] https://arxiv.org/abs/1707.05261

[13] https://link.springer.com/book/10.1007%2F978-3-642
-35289-8

[14] https://doi.org/10.1007/978-3-319-98131-4_5

[15] http://link.springer.com/chapter/10.1007%2F978-3-6
42-35289-8_33

[16] http://dx.doi.org/10.1038/s41467-019-08987-4

[17] https://doi.org/10.1186/s13073-018-0591-9

[18] https://doi.org/10.1016/j.dsp.2017.10.011

[19] http://dx.doi.org/10.1371/journal.pone.0181142

[20] http://dx.doi.org/10.1016/j.patcog.2016.11.008

[21] http://dx.doi.org/10.1109/TNNLS.2016.2599820

[22] http://dx.doi.org/10.1371/journal.pone.0130140

[23] http://iopscience.iop.org/1367-2630/15/9/095003/

[24] http://pubs.acs.org/doi/abs/10.1021/ct400195d

[25] http://dx.doi.org/10.1109/MSP.2013.2249294

[26] http://jmlr.csail.mit.edu/papers/v12/montavon11a.h
tml

[27] https://papers.nips.cc/paper/6248-wasserstein-trai
ning-of-restricted-boltzmann-machines

[28] http://doc.ml.tu-berlin.de/wrbm/

[29] http://dx.doi.org/10.1007/978-3-319-45886-1_28

[30] http://dx.doi.org/10.1007/978-3-319-44781-0_8

[31] http://dx.doi.org/10.1109/CVPR.2016.318

[32] http://dx.doi.org/10.1007/978-981-10-0557-2_87

[33] https://papers.nips.cc/paper/4830-learning-invaria
nt-representations-of-molecules-for-atomization-energy-
prediction

[34] http://jmlr.org/proceedings/papers/v22/montavon12/
montavon12.pdf

[35] http://papers.nips.cc/paper/4061-layer-wise-analys
is-of-deep-networks-with-gaussian-kernels

[36] https://arxiv.org/abs/1706.07206

[37] http://arxiv.org/abs/1611.08191

[38] http://arxiv.org/abs/1606.07298

[39] http://icmlviz.github.io/assets/papers/13.pdf

[40] http://icmlviz.github.io/assets/papers/18.pdf

[41] http://www.jmlr.org/papers/v17/15-618.html

[42] http://journals.cambridge.org/action/displayAbstra
ct?fromPage=online&aid=8841670

[43] http://gregoire.montavon.name/publications/2009-de
epspeech.pdf

[44] http://opus4.kobv.de/opus4-tuberlin/frontdoor/inde
x/index/docId/4467

[45] http://gregoire.montavon.name/publications/2009-ms
thesis.pdf

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