| Learning more universal representations for transfer-learning Y Tamaazousti, H Le Borgne, C Hudelot, M Tamaazousti IEEE transactions on pattern analysis and machine intelligence 42 (9), 2212-2224, 2019 | 73 | 2019 |
| Random matrix theory proves that deep learning representations of gan-data behave as gaussian mixtures MEA Seddik, C Louart, M Tamaazousti, R Couillet International Conference on Machine Learning, 8573-8582, 2020 | 69 | 2020 |
| Deep Multi-class Adversarial Specularity Removal J Lin, MEA Seddik, M Tamaazousti, Y Tamaazousti, A Bartoli arXiv preprint arXiv:1904.02672, 2019 | 35 | 2019 |
| Kernel Random Matrices of Large Concentrated Data: The Example of GAN-Generated Images MEA Seddik, M Tamaazousti, R Couillet ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal …, 2019 | 24 | 2019 |
| Soil moisture estimation using Sentinel-1/-2 imagery coupled with cycleGAN for time-series gap filing N Efremova, MEA Seddik, E Erten IEEE Transactions on Geoscience and Remote Sensing 60, 1-11, 2021 | 19 | 2021 |
| When random tensors meet random matrices MEA Seddik, M Guillaud, R Couillet The Annals of Applied Probability 34 (1A), 203-248, 2024 | 15 | 2024 |
| A Kernel Random Matrix-Based Approach for Sparse PCA MEA Seddik, M Tamaazousti, R Couillet ICLR 2019 - International Conference on Learning Representations, 2019 | 14 | 2019 |
| Generative collaborative networks for single image super-resolution MEA Seddik, M Tamaazousti, J Lin Neurocomputing 398, 293-303, 2020 | 11 | 2020 |
| The unexpected deterministic and universal behavior of large softmax classifiers MEA Seddik, C Louart, R Couillet, M Tamaazousti International Conference on Artificial Intelligence and Statistics, 1045-1053, 2021 | 10 | 2021 |
| Deep miner: a deep and multi-branch network which mines rich and diverse features for person re-identification A Benzine, MEA Seddik, J Desmarais arXiv preprint arXiv:2102.09321, 2021 | 10 | 2021 |
| Node feature kernels increase graph convolutional network robustness MEA Seddik, C Wu, JF Lutzeyer, M Vazirgiannis International Conference on Artificial Intelligence and Statistics, 6225-6241, 2022 | 7 | 2022 |
| Lightweight neural networks from pca & lda based distilled dense neural networks MEA Seddik, H Essafi, A Benzine, M Tamaazousti 2020 IEEE International Conference on Image Processing (ICIP), 3060-3064, 2020 | 5 | 2020 |
| SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework CJ Foley, S Vaze, MEA Seddiq, A Unagaev, N Efremova arXiv preprint arXiv:2003.10823, 2020 | 5 | 2020 |
| From outage probability to ALOHA MAC layer performance analysis in distributed WSNs V Toldov, L Clavier, N Mitton 2018 IEEE Wireless Communications and Networking Conference (WCNC), 1-6, 2018 | 5 | 2018 |
| Deciphering lasso-based classification through a large dimensional analysis of the iterative soft-thresholding algorithm M Tiomoko, E Schnoor, MEA Seddik, I Colin, A Virmaux International Conference on Machine Learning, 21449-21477, 2022 | 4 | 2022 |
| How bad is training on synthetic data? a statistical analysis of language model collapse MEA Seddik, SW Chen, S Hayou, P Youssef, M Debbah arXiv preprint arXiv:2404.05090, 2024 | 2 | 2024 |
| Neural Networks Classify through the Class-wise Means of their Representations MEA Seddik, M Tamaazousti | 2 | 2021 |
| Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks S Vaze, J Foley, M Seddiq, A Unagaev, N Efremova arXiv preprint arXiv:2009.07000, 2020 | 2 | 2020 |
| Investigating Regularization of Self-Play Language Models R Alami, A Abubaker, M Achab, MEA Seddik, S Lahlou arXiv preprint arXiv:2404.04291, 2024 | 1 | 2024 |
| Learning from low rank tensor data: A random tensor theory perspective MEA Seddik, M Tiomoko, A Decurninge, M Panov, M Gauillaud Uncertainty in Artificial Intelligence, 1858-1867, 2023 | 1 | 2023 |
Romain COUILLETUGA IDEX DataScience Chair, GIPSA-lab, University Grenoble-Alpes在 gipsa-lab.grenoble-inp.fr 的电子邮件经过验证
