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Grégoire Montavon
Grégoire Montavon
Freie Universität Berlin
Verifierad e-postadress på fu-berlin.de - Startsida
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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
40692015
Methods for interpreting and understanding deep neural networks
G Montavon, W Samek, KR Müller
Digital Signal Processing, 2018
24922018
Explaining nonlinear classification decisions with deep taylor decomposition
G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
Pattern recognition 65, 211-222, 2017
14182017
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
11872016
Explainable AI: interpreting, explaining and visualizing deep learning
W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller
Springer Nature, 2019
10652019
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
9922019
Neural networks-tricks of the trade second edition
G Montavon, G Orr, KR Müller
Springer, DOI 10, 978-3, 2012
805*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
780*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
6972013
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
6602021
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
6602013
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
6512019
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
4052016
Explaining recurrent neural network predictions in sentiment analysis
L Arras, G Montavon, KR Müller, W Samek
arXiv preprint arXiv:1706.07206, 2017
3942017
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
J. Mach. Learn. Res. 20 (93), 1-8, 2019
3662019
" 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
3512017
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
2432016
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
1822016
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
1822012
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Artiklar 1–20