Grégoire Montavon

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i10-index	62	59

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & 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 BinderChair Computer Vision, OvGU, Magdeburg, GermanyVerified email at ovgu.de
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
Jacob KauffmannTU BerlinVerified email at tu-berlin.de
Lukas RuffAignosticsVerified email at aignostics.com
Oliver EberleTU BerlinVerified email at tu-berlin.de
Leila ArrasResearch Associate, Fraunhofer HHI, BIFOLD, Berlin, GermanyVerified email at hhi.fraunhofer.de
Christopher J. AndersCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at riken.jp
Thomas SchnakeTechnical University of BerlinVerified email at tu-berlin.de
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Shinichi NakajimaBIFOLD, Technische Universität 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
Siddhant AgarwalHelmholtz-Zentrum HereonVerified email at hereon.de
Marco CuturiApple / CREST-ENSAE.Verified email at apple.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
Johanna VielhabenFraunhofer HHIVerified email at hhi.fraunhofer.de
Léo AndéolInstitut de Mathématiques de Toulouse / SNCFVerified email at math.univ-toulouse.fr

Grégoire Montavon

Guest professor, Freie Universität Berlin

Verified email at fu-berlin.de - Homepage

Explainable AI Machine Learning Data Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	5242	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018	3090	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	1706	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	1455	2016
Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019	1389	2019
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	1271	2021
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	1261	2019
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	1032	2021
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	944	2019
Neural networks-tricks of the trade second edition G Montavon, G Orr, KR Müller Springer, DOI 10, 978-3, 2012	922*	2012
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	794	2013
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	715	2013
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	573	2016
Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017	471	2017
iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... Journal of machine learning research 20 (93), 1-8, 2019	439	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	394	2017
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	306*	2022
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	269	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	210	2012
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	206	2016

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