| GSPBOX: A toolbox for signal processing on graphs N Perraudin, J Paratte, D Shuman, L Martin, V Kalofolias, ... arXiv preprint arXiv:1408.5781, 2014 | 387 | 2014 |
| Stationary signal processing on graphs N Perraudin, P Vandergheynst IEEE Transactions on Signal Processing 65 (13), 3462-3477, 2017 | 275 | 2017 |
| A fast Griffin-Lim algorithm N Perraudin, P Balazs, PL Sondergaard 2013 IEEE Workshop on Applications of Signal Processing to Audio and …, 2013 | 231 | 2013 |
| Deepsphere: Efficient spherical convolutional neural network with healpix sampling for cosmological applications N Perraudin, M Defferrard, T Kacprzak, R Sgier Astronomy and Computing 27, 130-146, 2019 | 220 | 2019 |
| A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs F Grassi, A Loukas, N Perraudin, B Ricaud IEEE Transactions on Signal Processing 66 (3), 817 - 829, 2017 | 198 | 2017 |
| Fast robust pca on graphs N Shahid, N Perraudin, V Kalofolias, P Vandergheynst IEEE Journal of Selected Topics in Signal Processing 10 (4), 740-756, 2016 | 143 | 2016 |
| Forecasting time series with VARMA recursions on graphs E Isufi, A Loukas, N Perraudin, G Leus IEEE Transactions on Signal Processing 67 (18), 4870-4885, 2019 | 128 | 2019 |
| DeepSphere: a graph-based spherical CNN M Defferrard, M Milani, F Gusset, N Perraudin Eighth International Conference on Learning Representations (ICLR), 2020 | 111 | 2020 |
| Large scale graph learning from smooth signals V Kalofolias, N Perraudin ICLR, International Conference on Learning Representations, 2019 | 101 | 2019 |
| UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods N Perraudin, V Kalofolias, D Shuman, P Vandergheynst arXiv preprint arXiv:1402.0779, 2014 | 99 | 2014 |
| Adversarial Generation of Time-Frequency Features with application in audio synthesis A Marafioti, N Holighaus, N Perraudin, P Majdak 36th International Conference on Machine Learning (ICML) 97, 4352--4362, 2019 | 95 | 2019 |
| Global and local uncertainty principles for signals on graphs N Perraudin, B Ricaud, D Shuman, P Vandergheynst APSIPA Transactions on Signal and Information 7, 2018 | 89 | 2018 |
| A context encoder for audio inpainting A Marafioti, N Perraudin, N Holighaus, P Majdak IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 (12 …, 2019 | 87 | 2019 |
| Accelerated filtering on graphs using lanczos method A Susnjara, N Perraudin, D Kressner, P Vandergheynst arXiv preprint arXiv:1509.04537, 2015 | 75 | 2015 |
| SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators K Martinkus, A Loukas, N Perraudin, R Wattenhofer 39th International Conference on Machine Learning 162, 15159--15179, 2022 | 74 | 2022 |
| Pygsp: Graph signal processing in python M Defferrard, L Martin, R Pena, N Perraudin URL https://github. com/epfl-lts2/pygsp, 2017 | 63 | 2017 |
| Stationary time-vertex signal processing A Loukas, N Perraudin EURASIP journal on advances in signal processing 2019 (1), 1-19, 2019 | 61 | 2019 |
| Diffusion models for graphs benefit from discrete state spaces KK Haefeli, K Martinkus, N Perraudin, R Wattenhofer LOG 2022, The First Learning on Graphs Conference, 2022 | 60 | 2022 |
| Towards stationary time-vertex signal processing N Perraudin, A Loukas, F Grassi, P Vandergheynst 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 59 | 2017 |
| GACELA: A generative adversarial context encoder for long audio inpainting of music A Marafioti, P Majdak, N Holighaus, N Perraudin IEEE Journal of Selected Topics in Signal Processing 15 (1), 120-131, 2020 | 54 | 2020 |
Pierre VandergheynstProfessor of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)epfl.ch의 이메일 확인됨