| 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 | 710 | 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 |
| 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 |
| 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 | 204 | 2020 |
| Learning parametric dictionaries for signals on graphs D Thanou, DI Shuman, P Frossard IEEE Transactions on Signal Processing 62 (15), 3849-3862, 2014 | 149 | 2014 |
| Learning time varying graphs V Kalofolias, A Loukas, D Thanou, P Frossard 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 100 | 2017 |
| Distributed average consensus with quantization refinement D Thanou, E Kokiopoulou, Y Pu, P Frossard IEEE Transactions on Signal Processing 61 (1), 194-205, 2012 | 78 | 2012 |
| Laplacian matrix learning for smooth graph signal representation X Dong, D Thanou, P Frossard, P Vandergheynst 2015 IEEE international conference on Acoustics, Speech and Signal …, 2015 | 75 | 2015 |
| Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations K Sherratt, H Gruson, H Johnson, R Niehus, B Prasse, F Sandmann, ... Elife 12, e81916, 2023 | 62 | 2023 |
| Graph-based motion estimation and compensation for dynamic 3D point cloud compression D Thanou, PA Chou, P Frossard 2015 IEEE International Conference on Image Processing (ICIP), 3235-3239, 2015 | 62 | 2015 |
| Reconstruction of time-varying graph signals via Sobolev smoothness JH Giraldo, A Mahmood, B Garcia-Garcia, D Thanou, T Bouwmans IEEE Transactions on Signal and Information Processing over Networks 8, 201-214, 2022 | 60 | 2022 |
| Parametric dictionary learning for graph signals D Thanou, DI Shuman, P Frossard 2013 IEEE Global Conference on Signal and Information Processing, 487-490, 2013 | 54 | 2013 |
| Combining anatomical and functional networks for neuropathology identification: A case study on autism spectrum disorder S Itani, D Thanou Medical image analysis 69, 101986, 2021 | 50 | 2021 |
| Interpretable stability bounds for spectral graph filters H Kenlay, D Thanou, X Dong International conference on machine learning, 5388-5397, 2021 | 46 | 2021 |
| Graph learning under sparsity priors HP Maretic, D Thanou, P Frossard 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 46 | 2017 |
| On the stability of graph convolutional neural networks under edge rewiring H Kenlay, D Thanou, X Dong ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 37 | 2021 |
| Graph transform optimization with application to image compression G Fracastoro, D Thanou, P Frossard IEEE Transactions on Image Processing 29, 419-432, 2019 | 34 | 2019 |
| Learning graphs from signal observations under smoothness prior X Dong, D Thanou, P Frossard, P Vandergheynst arXiv preprint arXiv:1406.7842, 2014 | 28 | 2014 |
| A graph signal processing framework for the classification of temporal brain data S Itani, D Thanou 2020 28th European Signal Processing Conference (EUSIPCO), 1180-1184, 2021 | 24 | 2021 |
Pascal FrossardEcole Polytechnique Fédérale de Lausanne, EPFL在 epfl.ch 的电子邮件经过验证