Grégoire Montavon

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Klaus-Robert MüllerTU Berlin & Korea University & Google Deepmind & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
Alexander BinderAssociate Professor, Singapore Institute of Technology (SIT)Verified email at singaporetech.edu.sg
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Verified email at lmu.de
Leila ArrasResearch Associate, Fraunhofer HHI, BIFOLD, Berlin, GermanyVerified email at hhi.fraunhofer.de
Jacob KauffmannTU BerlinVerified email at tu-berlin.de
Christopher J. AndersTechnische Universität BerlinVerified email at tu-berlin.de
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Lukas RuffAignosticsVerified email at aignostics.com
Oliver EberleTU BerlinVerified email at tu-berlin.de
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinVerified email at utoronto.ca
Thomas SchnakeTechnical University of BerlinVerified email at tu-berlin.de
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Marco CuturiApple / CREST-ENSAE.Verified email at apple.com
Siddhant AgarwalAirbusVerified email at airbus.com
Pattarawat ChormaiTechnische Universität Berlin and Max Planck School of CognitionVerified email at tu-berlin.de
Ping XiongTechnical University BerlinVerified email at tu-berlin.de
Léo AndéolInstitut de Mathématiques de Toulouse / SNCFVerified email at math.univ-toulouse.fr
Johanna VielhabenFraunhofer HHIVerified email at hhi.fraunhofer.de

Grégoire Montavon

Freie Universität Berlin

Verified email at fu-berlin.de - Homepage

Explainable AI Machine Learning Data Science


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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek PloS one 10 (7), e0130140, 2015	4072	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018	2493	2018
Explaining nonlinear classification decisions with deep taylor decomposition G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller Pattern recognition 65, 211-222, 2017	1418	2017
Evaluating the visualization of what a deep neural network has learned W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller IEEE transactions on neural networks and learning systems 28 (11), 2660-2673, 2016	1188	2016
Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019	1067	2019
Unmasking Clever Hans predictors and assessing what machines really learn S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, KR Müller Nature communications 10 (1), 1096, 2019	993	2019
Neural networks-tricks of the trade second edition G Montavon, G Orr, KR Müller Springer, DOI 10, 978-3, 2012	806*	2012
Explaining deep neural networks and beyond: a review of methods and applications W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller Proceedings of the IEEE 109 (3), 2021	781*	2021
Machine learning of molecular electronic properties in chemical compound space G Montavon, M Rupp, V Gobre, A Vazquez-Mayagoitia, K Hansen, ... New Journal of Physics 15 (9), 095003, 2013	697	2013
A unifying review of deep and shallow anomaly detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE, 1-40, 2021	661	2021
Assessment and validation of machine learning methods for predicting molecular atomization energies K Hansen, G Montavon, F Biegler, S Fazli, M Rupp, M Scheffler, ... Journal of Chemical Theory and Computation 9 (8), 3404-3419, 2013	660	2013
Layer-wise relevance propagation: an overview G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller Explainable AI: interpreting, explaining and visualizing deep learning, 193-209, 2019	653	2019
Layer-wise relevance propagation for neural networks with local renormalization layers A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016	405	2016
Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017	393	2017
iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019	367	2019
" What is relevant in a text document?": An interpretable machine learning approach L Arras, F Horn, G Montavon, KR Müller, W Samek PloS one 12 (8), e0181142, 2017	349	2017
Analyzing classifiers: Fisher vectors and deep neural networks S Lapuschkin, A Binder, G Montavon, KR Muller, W Samek Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	243	2016
Higher-order explanations of graph neural networks via relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 7581 …, 2022	195*	2022
Layer-wise relevance propagation for deep neural network architectures A Binder, S Bach, G Montavon, KR Müller, W Samek Information science and applications (ICISA) 2016, 913-922, 2016	182	2016
Learning Invariant Representations of Molecules for Atomization Energy Prediction G Montavon, K Hansen, S Fazli, M Rupp, F Biegler, A Ziehe, ... Advances in Neural Information Processing Systems 25, 449-457, 2012	182	2012

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