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TU Berlin

Inhalt des Dokuments

Grégoire Montavon

Contact
Address: Room: 4.059, Marchstr. 23, D-10587 Berlin
E-mail:
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 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
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

Teaching

Winter semester 2019/20
Teaching assistant for Machine Learning 1
Organizer for Seminar Big Data & Scalable ML
Summer semester 2019
Teaching assistant for Machine Learning 2
Organizer for Deep Neural Networks

Publications

Preprints
2019-05-18
J Kauffmann, M Esders, G Montavon, W Samek, KR Müller. From Clustering to Cluster Explanations via Neural Networks
2018-06-29
J Kauffmann, G Montavon, LA Lima, S Nakajima, KR Müller, N Görnitz.Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
2018-05-16
J Kauffmann, KR Müller, G Montavon. Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
2017-07-17
F Horn, L Arras, G Montavon, KR Müller, W Samek. Exploring text datasets by visualizing relevant words
Edited Books
2019
W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller (Eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer LNCS 11700
2012
G Montavon, G Orr, KR Müller (Eds.) Neural Networks: Tricks of the Trade, 2nd Edn, Springer LNCS 7700
Book Chapters
2019
G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller. Layer-Wise Relevance Propagation: An Overview in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 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: Interpreting, Explaining and Visualizing Deep Learning, Springer LNCS 11700
2019
G Montavon. Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 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: Interpreting, Explaining and Visualizing Deep Learning, Springer LNCS 11700
2018
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
2012
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
Journal Publications
2019
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
2018
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
2017
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
2016
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
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
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
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
2011
G Montavon, M Braun, KR Müller. Kernel Analysis of Deep Networks
Journal of Machine Learning Research, 12:2563-2581
Conference Publications
2016
G Montavon, KR Müller, M Cuturi. Wasserstein Training of Restricted Boltzmann Machines
Advances in Neural Information Processing Systems (NIPS) (code)
2016
F Arbabzadah, G Montavon, KR Müller, W Samek. Identifying Individual Facial Expressions by Deconstructing a Neural Network
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
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
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
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
Advances in Neural Information Processing Systems (NIPS)
2012
G Montavon, M Braun, KR Müller. Deep Boltzmann Machines as Feed-Forward Hierarchies
International Conference on Artificial Intelligence and Statistics (AISTATS)
2010
G Montavon, M Braun, KR Müller. Layer-Wise Analysis of Deep Networks with Gaussian Kernels
Advances in Neural Information Processing Systems (NIPS)
Other Publications
2019
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 (JMLR/MLOSS)
2017
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
2016
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
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 (JMLR/MLOSS)
2013
G Montavon, KR Müller. Neural Networks for Computational Chemistry: Pitfalls and Recommendations
MRS Online Proceedings Library
2009
G Montavon. Deep Learning for Spoken Language Identification
NIPS Workshop on Deep Learning for Speech Recognition and Related Applications
Theses
2013
G Montavon. On Layer-Wise Representations in Deep Neural Networks
PhD Thesis, Technische Universität Berlin, Germany
2009
G Montavon. A Machine Learning Approach to Classification of Low Resolution Histological Samples
Master Thesis, École Polytechnique Fédérale de Lausanne, Switzerland

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