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Pascal Frossard
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Citado por
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Año
DeepFool: a simple and accurate method to fool deep neural networks
SM Moosavi-Dezfooli, A Fawzi, P Frossard
IEEE CVPR, 2016
63452016
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
DI Shuman, SK Narang, P Frossard, A Ortega, P Vandergheynst
IEEE signal processing magazine 30 (3), 83-98, 2013
48142013
Universal adversarial perturbations
SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard
IEEE CVPR, 2017
32642017
Graph signal processing: Overview, challenges, and applications
A Ortega, P Frossard, J Kovačević, JMF Moura, P Vandergheynst
Proceedings of the IEEE 106 (5), 808-828, 2018
18662018
Dictionary learning
I Tošić, P Frossard
Signal Processing Magazine, IEEE 28 (2), 27-38, 2011
11302011
Learning Laplacian Matrix in Smooth Graph Signal Representations
X Dong, D Thanou, P Frossard, P Vandergheynst
IEEE Transactions on Signal Processing 64 (23), 6160 - 6173, 2016
7152016
Learning graphs from data: A signal representation perspective
X Dong, D Thanou, M Rabbat, P Frossard
IEEE Signal Processing Magazine 36 (3), 44-63, 2019
4672019
Robustness of classifiers: from adversarial to random noise
A Fawzi, SM Moosavi-Dezfooli, P Frossard
NIPS, 2016
4312016
Analysis of classifiers’ robustness to adversarial perturbations
A Fawzi, O Fawzi, P Frossard
Machine Learning, 2017
4182017
Robustness via curvature regularization, and vice versa
SM Moosavi-Dezfooli, A Fawzi, J Uesato, P Frossard
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
3742019
Adaptive data augmentation for image classification
A Fawzi, H Samulowitz, D Turaga, P Frossard
2016 IEEE international conference on image processing (ICIP), 3688-3692, 2016
3302016
Digress: Discrete denoising diffusion for graph generation
C Vignac, I Krawczuk, A Siraudin, B Wang, V Cevher, P Frossard
ICLR, 2023
3192023
Graph-based compression of dynamic 3D point cloud sequences
D Thanou, PA Chou, P Frossard
IEEE Transactions on Image Processing 25 (4), 1765-1778, 2016
2962016
Clustering on multi-layer graphs via subspace analysis on Grassmann manifolds
X Dong, P Frossard, P Vandergheynst, N Nefedov
IEEE Transactions on signal processing 62 (4), 905-918, 2013
2542013
Empirical study of the topology and geometry of deep networks
A Fawzi, SM Moosavi-Dezfooli, P Frossard, S Soatto
IEEE CVPR, 2018
238*2018
Sparsefool: a few pixels make a big difference
A Modas, SM Moosavi-Dezfooli, P Frossard
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
2322019
The robustness of deep networks: A geometrical perspective
A Fawzi, SM Moosavi-Dezfooli, P Frossard
IEEE Signal Processing Magazine 34 (6), 50-62, 2017
227*2017
Adaptive quantization for deep neural network
Y Zhou, SM Moosavi-Dezfooli, NM Cheung, P Frossard
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
2212018
Learning heat diffusion graphs
D Thanou, X Dong, D Kressner, P Frossard
IEEE Transactions on Signal and Information Processing over Networks 3 (3 …, 2017
2042017
Graph signal processing for machine learning: A review and new perspectives
X Dong, D Thanou, L Toni, M Bronstein, P Frossard
IEEE Signal processing magazine 37 (6), 117-127, 2020
1952020
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