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Louart, Cosme

  1. A Random Matrix Approach to Neural Networks
    2017/02/17 by Louart, Cosme, Liao, Zhenyu, Couillet, Romain · 10 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Probability (math.PR)
  2. Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
    2020/01/21 by Seddik, Mohamed El Amine, Louart, Cosme, Tamaazousti, Mohamed +1 · 6 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  3. Concentration of Measure and Large Random Matrices with an application to Sample Covariance Matrices
    2018/05/21 by Cosme Louart, Romain Couillet, Louart, Cosme +1 · 3 citations
    Mathematics · #60B12 #60B20 #Advanced Combinatorial Mathematics #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Random Matrices and Applications
  4. Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
    2024/06/14 by Romain Ilbert, Malik Tiomoko, Ilbert, Romain +11 · 3 citations
    Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Statistical Mechanics and Entropy
  5. Spectral properties of sample covariance matrices arising from random matrices with independent non identically distributed columns
    2021/09/06 by Louart, Cosme, Couillet, Romain · 1 citation
    #15A52 #60B12 #62J10 #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Probability (math.PR)
  6. A Concentration of Measure and Random Matrix Approach to Large\n Dimensional Robust Statistics
    2020/06/17 by Cosme Louart, Louart, Cosme, Romain Couillet +1 · 1 citation
    Mathematics · #60B20 #62F35 #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Point processes and geometric inequalities #Probability (math.PR) #Random Matrices and Applications #Statistical Methods and Inference