| DeepFool: a simple and accurate method to fool deep neural networks SM Moosavi-Dezfooli, A Fawzi, P Frossard IEEE CVPR, 2016 | 5834 | 2016 |
| 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 | 4519 | 2013 |
| Universal adversarial perturbations SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard IEEE CVPR, 2017 | 3030 | 2017 |
| 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 | 1680 | 2018 |
| Dictionary learning I Tošić, P Frossard Signal Processing Magazine, IEEE 28 (2), 27-38, 2011 | 1088 | 2011 |
| 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 | 666 | 2016 |
| 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 | 428 | 2019 |
| Robustness of classifiers: from adversarial to random noise A Fawzi, SM Moosavi-Dezfooli, P Frossard NIPS, 2016 | 408 | 2016 |
| Analysis of classifiers’ robustness to adversarial perturbations A Fawzi, O Fawzi, P Frossard Machine Learning, 2017 | 396 | 2017 |
| 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 | 333 | 2019 |
| 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 | 307 | 2016 |
| 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 | 291 | 2016 |
| 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 | 237 | 2013 |
| Empirical study of the topology and geometry of deep networks A Fawzi, SM Moosavi-Dezfooli, P Frossard, S Soatto IEEE CVPR, 2018 | 219* | 2018 |
| The robustness of deep networks: A geometrical perspective A Fawzi, SM Moosavi-Dezfooli, P Frossard IEEE Signal Processing Magazine 34 (6), 50-62, 2017 | 212* | 2017 |
| Digress: Discrete denoising diffusion for graph generation C Vignac, I Krawczuk, A Siraudin, B Wang, V Cevher, P Frossard ICLR, 2023 | 211 | 2023 |
| 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 | 210 | 2019 |
| 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 | 190 | 2018 |
| Learning heat diffusion graphs D Thanou, X Dong, D Kressner, P Frossard IEEE Transactions on Signal and Information Processing over Networks 3 (3 …, 2017 | 186 | 2017 |
| Clustering with multi-layer graphs: A spectral perspective X Dong, P Frossard, P Vandergheynst, N Nefedov IEEE Transactions on Signal Processing 60 (11), 5820-5831, 2012 | 185 | 2012 |
Pierre VandergheynstProfessor of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)在 epfl.ch 的电子邮件经过验证
