Grégoire Montavon

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Indice H	40	39
i10-index	62	59

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

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Grégoire Montavon

Guest professor, Freie Universität Berlin

Email verificata su fu-berlin.de - Home page

Explainable AI Machine Learning Data Science


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	5178	2015
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing, 2018	3052	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	1692	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	1441	2016
Explainable AI: interpreting, explaining and visualizing deep learning W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller Springer Nature, 2019	1364	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	1245	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	1240	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	1005	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	930	2019
Neural networks-tricks of the trade second edition G Montavon, G Orr, KR Müller Springer, DOI 10, 978-3, 2012	916*	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	793	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	711	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	566	2016
Explaining recurrent neural network predictions in sentiment analysis L Arras, G Montavon, KR Müller, W Samek arXiv preprint arXiv:1706.07206, 2017	469	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	435	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	391	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	305*	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	268	2016
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
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	205	2012

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