## Grégoire MontavonPostDoc at TU Berlin > Faculty IV > Theoretical Computer Science > Machine Learning Email: gregoire.montavon@tu-berlin.de 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. |

- ICANN 2016 satellite event on machine learning and interpretability on 6 September 2016 in Barcelona
- A tutorial for implementing deep Taylor decomposition.

- G Montavon, S Bach, A Binder, W Samek, KR Müller. Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

arXiv (preprint), 2015 [bibtex]

- W Samek, A Binder, G Montavon, S Bach, KR Müller. Evaluating the Visualization of What a Deep Neural Network has Learned

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016 [preprint, bibtex] - 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 [bibtex] - 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), 2016 - S Bach, 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 [bibtex] - 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), 2016 [bibtex] - 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, 2015 [bibtex]

- 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, M Cuturi. Wasserstein Training of Restricted Boltzmann Machines

Advances in Neural Information Processing Systems (NIPS), 2016 [preprint, bibtex] - G Montavon, KR Müller. Deep Boltzmann Machines and the Centering Trick

in Neural Networks: Tricks of the Trade, 2nd Edn, Springer LNCS, 2012, [bibtex] [code] [pdf] - G Montavon, M Braun, KR Müller. Deep Boltzmann Machines as Feed-Forward Hierarchies

International Conference on Artificial Intelligence and Statistics (AISTATS), 2012, [bibtex]

- G Montavon. On Layer-Wise Representations in Deep Neural Networks

PhD Thesis, TU Berlin, 2013 [bibtex]

- G Montavon, M Braun, KR Müller. Kernel Analysis of Deep Networks

Journal of Machine Learning Research (JMLR), 2011 [bibtex] [code] - G Montavon, M Braun, KR Müller. Layer-Wise Analysis of Deep Networks with Gaussian Kernels

Advances in Neural Information Processing Systems (NIPS), 2010 [bibtex]

- 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, 2013 [bibtex] - 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, 2013 [bibtex] - G Montavon, KR Müller. Neural Networks for Computational Chemistry: Pitfalls and Recommendations

MRS Online Proceedings Library, 2013 [bibtex] - 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 [bibtex]

- G Montavon, G Orr, KR Müller (Eds.). Neural Networks: Tricks of the Trade, 2nd Edn

Springer, Lecture Notes in Computer Science (LNCS), 2012, [bibtex, amazon.com, springer.com]

- G Montavon, M Braun, T Krueger, KR Müller. Analyzing Local Structure in Kernel-based Learning: Explanation, Complexity and Reliability Assessment

IEEE Signal Processing Magazine, 2013 [bibtex] - G Montavon. Deep Learning for Spoken Language Identification

NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, 2009 [bibtex]

- G Montavon. A Machine Learning Approach to Classification of Low Resolution Histological Samples

Master Thesis, École Polytechnique Fédérale de Lausanne, Switzerland, 2009 [bibtex]