Random Matrix Methods for Machine Learning

Couillet, Romain; Liao, Zhenyu

ISBN 10: 1009123238 / ISBN 13: 9781009123235

Editore: Cambridge University Press, 2022

In. Codice articolo ria9781009123235_new

Riassunto:

This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.

Informazioni sugli autori: Romain Couillet is a Full Professor at Grenoble-Alpes University, France. Prior to that, he was a Full Professor at CentraleSupélec, University of Paris-Saclay. His research topics are in random matrix theory applied to statistics, machine learning, and signal processing. He is the recipient of the 2021 IEEE/SEE Glavieux prize, of the 2013 CNRS Bronze Medal, and of the 2013 IEEE ComSoc Outstanding Young Researcher Award.

Zhenyu Liao is an Associated Professor with Huazhong University of Science and Technology (HUST), China. He is the recipient of the 2021 East Lake Youth Talent Program Fellowship of HUST, the 2019 ED STIC Ph.D. Student Award, and the 2016 Supélec Foundation Ph.D. Fellowship of University of Paris-Saclay, France.

Dati bibliografici

Titolo: Random Matrix Methods for Machine Learning
Casa editrice: Cambridge University Press
Data di pubblicazione: 2022
Legatura: Rilegato
Condizione: New