| DeepFool: a simple and accurate method to fool deep neural networks SM Moosavi-Dezfooli, A Fawzi, P Frossard IEEE CVPR, 2016 | 6519 | 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 | 4910 | 2013 |
| Universal adversarial perturbations SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard IEEE CVPR, 2017 | 3373 | 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 | 1934 | 2018 |
| Dictionary learning I Tošić, P Frossard Signal Processing Magazine, IEEE 28 (2), 27-38, 2011 | 1144 | 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 | 729 | 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 | 481 | 2019 |
| Robustness of classifiers: from adversarial to random noise A Fawzi, SM Moosavi-Dezfooli, P Frossard NIPS, 2016 | 436 | 2016 |
| Analysis of classifiers’ robustness to adversarial perturbations A Fawzi, O Fawzi, P Frossard Machine Learning, 2017 | 426 | 2017 |
| Digress: Discrete denoising diffusion for graph generation C Vignac, I Krawczuk, A Siraudin, B Wang, V Cevher, P Frossard ICLR, 2023 | 384 | 2023 |
| 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 | 378 | 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 | 334 | 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 | 301 | 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 | 254 | 2013 |
| Empirical study of the topology and geometry of deep networks A Fawzi, SM Moosavi-Dezfooli, P Frossard, S Soatto IEEE CVPR, 2018 | 242* | 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 | 240 | 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 | 234 | 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 | 233* | 2017 |
| Learning heat diffusion graphs D Thanou, X Dong, D Kressner, P Frossard IEEE Transactions on Signal and Information Processing over Networks 3 (3 …, 2017 | 211 | 2017 |
| 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 | 205 | 2020 |
Pierre VandergheynstProfessor of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)epfl.ch의 이메일 확인됨