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2020 – today
- 2024
[i3]Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko:
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. CoRR abs/2406.10327 (2024)- 2021
- [c6]Mohamed El Amine Seddik, Cosme Louart, Romain Couillet, Mohamed Tamaazousti:
The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers. AISTATS 2021: 1045-1053 - 2020
- [c5]Malik Tiomoko, Cosme Louart, Romain Couillet:
Large Dimensional Asymptotics of Multi-Task Learning. ICASSP 2020: 8787-8791 - [c4]Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet:
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. ICML 2020: 8573-8582 - [i2]Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet:
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. CoRR abs/2001.08370 (2020)
2010 – 2019
- 2019
- [c3]Cosme Louart, Romain Couillet:
A Concentration of Measure Perspective to Robust Statistics. CAMSAP 2019: 679-683 - 2018
- [c2]Cosme Louart, Romain Couillet:
A Random Matrix and Concentration Inequalities Framework for Neural Networks Analysis. ICASSP 2018: 4214-4218 - 2017
- [c1]Cosme Louart, Romain Couillet:
Harnessing neural networks: A random matrix approach. ICASSP 2017: 2282-2286 - [i1]Cosme Louart, Zhenyu Liao, Romain Couillet:
A Random Matrix Approach to Neural Networks. CoRR abs/1702.05419 (2017)
Coauthor Index
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