Grégoire Montavon
Grégoire Montavon
Verified email at tu-berlin.de - Homepage
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
13082015
Methods for interpreting and understanding deep neural networks
G Montavon, W Samek, KR Müller
Digital Signal Processing, 2018
8712018
Explaining nonlinear classification decisions with deep taylor decomposition
G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
Pattern Recognition 65, 211-222, 2017
5272017
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
4442016
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
4342013
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
4172013
Neural networks: tricks of the trade
G Montavon, G Orr, KR Müller
springer, 2012
354*2012
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), 1-8, 2019
2032019
Explaining recurrent neural network predictions in sentiment analysis
L Arras, G Montavon, KR Müller, W Samek
arXiv preprint arXiv:1706.07206, 2017
1672017
" 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
1622017
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
1282012
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
1232016
Explainable AI: interpreting, explaining and visualizing deep learning
W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller
Springer Nature, 2019
1202019
Layer-wise relevance propagation for neural networks with local renormalization layers
A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek
International Conference on Artificial Neural Networks, 63-71, 2016
1162016
Kernel Analysis of Deep Networks
G Montavon, ML Braun, KR Müller
Journal of Machine Learning Research 12, 2563-2581, 2011
1022011
Deep Boltzmann machines and the centering trick
G Montavon, KR Müller
Neural Networks: Tricks of the Trade, 621-637, 2012
942012
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
J. Mach. Learn. Res. 20 (93), 1-8, 2019
912019
Wasserstein training of restricted Boltzmann machines
G Montavon, KR Müller, M Cuturi
Advances in Neural Information Processing Systems 29, 3718-3726, 2016
91*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
822016
Deep learning for spoken language identification
G Montavon
NIPS Workshop on deep learning for speech recognition and related …, 2009
692009
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