Grégoire Montavon
Grégoire Montavon
確認したメール アドレス: tu-berlin.de - ホームページ
タイトル引用先
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), 2015
7962015
Methods for interpreting and understanding deep neural networks
G Montavon, W Samek, KR Müller
Digital Signal Processing, 2018
5322018
Explaining nonlinear classification decisions with deep taylor decomposition
G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller
Pattern Recognition 65, 211-222, 2017
3652017
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
3542013
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
3212013
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
2992016
Neural networks: tricks of the trade
G Montavon, G Orr, KR Müller
springer, 2012
290*2012
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
1022012
Explaining recurrent neural network predictions in sentiment analysis
L Arras, G Montavon, KR Müller, W Samek
arXiv preprint arXiv:1706.07206, 2017
942017
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
902016
Kernel Analysis of Deep Networks
G Montavon, ML Braun, KR Müller
Journal of Machine Learning Research 12, 2563-2581, 2011
892011
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
872019
" 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), 2017
852017
Deep Boltzmann machines and the centering trick
G Montavon, KR Müller
Neural Networks: Tricks of the Trade, 621-637, 2012
812012
Wasserstein training of restricted Boltzmann machines
G Montavon, KR Müller, M Cuturi
Advances in Neural Information Processing Systems, 3718-3726, 2016
76*2016
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
672016
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
642016
Deep learning for spoken language identification
G Montavon
NIPS Workshop on deep learning for speech recognition and related …, 2009
592009
Explaining predictions of non-linear classifiers in nlp
L Arras, F Horn, G Montavon, KR Müller, W Samek
arXiv preprint arXiv:1606.07298, 2016
442016
Analyzing local structure in kernel-based learning: Explanation, complexity, and reliability assessment
G Montavon, ML Braun, T Krueger, KR Muller
IEEE Signal Processing Magazine 30 (4), 62-74, 2013
442013
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