Grégoire Montavon

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyAdresse e-mail validée de tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowAdresse e-mail validée de tu-berlin.de
Alexander BinderChair Computer Vision, OvGU, Magdeburg, GermanyAdresse e-mail validée de ovgu.de
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteAdresse e-mail validée de hhi.fraunhofer.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Adresse e-mail validée de lmu.de
Jacob KauffmannTU BerlinAdresse e-mail validée de tu-berlin.de
Lukas RuffAignosticsAdresse e-mail validée de aignostics.com
Oliver EberleTU BerlinAdresse e-mail validée de tu-berlin.de
Leila ArrasResearch Associate, Fraunhofer HHI, BIFOLD, Berlin, GermanyAdresse e-mail validée de hhi.fraunhofer.de
Christopher J. AndersCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoAdresse e-mail validée de riken.jp
Thomas SchnakeTechnical University of BerlinAdresse e-mail validée de tu-berlin.de
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Adresse e-mail validée de list.lu
Shinichi NakajimaBIFOLD, Technische Universität BerlinAdresse e-mail validée de tu-berlin.de
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinAdresse e-mail validée de utoronto.ca
Siddhant AgarwalHelmholtz-Zentrum HereonAdresse e-mail validée de hereon.de
Marco CuturiApple / CREST-ENSAE.Adresse e-mail validée de apple.com
Pattarawat ChormaiTechnische Universität Berlin and Max Planck School of CognitionAdresse e-mail validée de tu-berlin.de
Ping XiongTechnical University BerlinAdresse e-mail validée de tu-berlin.de
Johanna VielhabenFraunhofer HHIAdresse e-mail validée de hhi.fraunhofer.de
Léo AndéolInstitut de Mathématiques de Toulouse / SNCFAdresse e-mail validée de math.univ-toulouse.fr

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Grégoire Montavon

Guest professor, Freie Universität Berlin

Adresse e-mail validée de fu-berlin.de - Page d'accueil

Explainable AI Machine Learning Data Science


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	5398	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018	3171	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	1747	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	1490	2016
Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019	1429	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	1357	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	1310	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	1092	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	991	2019
Neural networks-tricks of the trade second edition G Montavon, G Orr, KR Müller Springer, DOI 10, 978-3, 2012	949*	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	811	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	727	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	587	2016
Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017	480	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	445	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	402	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	322*	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	278	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	213	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	208	2016

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