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 |
| 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 include interpretable machine learning and deep neural networks. |
Research
| 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 |
| 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
| Machine Learning 2 |
| Machine Learning 1 |
| Seminar Big Data & Scalable ML |
Publications
| 2020-03-17 | W Samek, G Montavon, S Lapuschkin, C Anders, KR Müller. Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond |
| 2020-03-11 | O Eberle, J Büttner, F Kräutli, KR Müller, M Valleriani, G Montavon. Building and Interpreting Deep Similarity Models |
| 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 |
| 2017-07-17 | F Horn, L Arras, G Montavon, KR Müller, W Samek. Exploring text datasets by visualizing relevant words |
| 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 |
| 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 |
| 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 |
| 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 |
| 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) |
| 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 |
| 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 |
Zusatzinformationen / Extras
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