Grégoire Montavon

Cited by

	All	Since 2018
Citations	15335	14169
h-index	34	32
i10-index	49	45

4600

2300

1150

3450

2013201420152016201720182019202020212022202356 79 148 233 540 1046 1804 2732 3640 4507 398

Public access

View all

34 articles

4 articles

available

not available

Based on funding mandates

Co-authors

Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & Max Planck Institute for InformaticsVerified email at tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at hhi.fraunhofer.de
Alexander BinderAssociate Professor, Singapore Institute of Technology (SIT) and University of Oslo (UiO)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, GermanyVerified email at hhi.fraunhofer.de
Jacob KauffmannTU BerlinVerified email at tu-berlin.de
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Christopher J. AndersTechnische Universität BerlinVerified email at tu-berlin.de
O. Anatole von LilienfeldUniversity of Toronto/Vector Institute/Technical University BerlinVerified email at utoronto.ca
Kristof T. SchüttPfizerVerified email at pfizer.com
Lukas RuffAignosticsVerified email at aignostics.com
Oliver EberleTU BerlinVerified email at tu-berlin.de
Mikio L. BraunTU Berlin (allumni)Verified email at getyourguide.de
Thomas SchnakeTechnical University of BerlinVerified email at tu-berlin.de
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Jonas LedererPhD student, TU BerlinVerified email at tu-berlin.de
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Marco CuturiApple / CREST-ENSAE.Verified email at apple.com
Simon LetzgusTechnical University of BerlinVerified email at tu-berlin.de

Grégoire Montavon

Freie Universität Berlin

Verified email at fu-berlin.de - Homepage

Explainable AI Neural Networks Unsupervised Learning


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	3238	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018	2009	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	1154	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	947	2016
Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019	777	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	743	2019
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	617	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	606	2013
Neural networks: tricks of the trade G Montavon, G Orr, KR Müller springer, 2012	563*	2012
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	451	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	446*	2021
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	385	2021
Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017	333	2017
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	308	2016
" 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	303	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	299	2019
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	205	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
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	152	2016
The LRP toolbox for artificial neural networks S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek The Journal of Machine Learning Research 17 (1), 3938-3942, 2016	148	2016

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors