Optimal errors and phase transitions in high-dimensional generalized linear models

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-correcting codes, or benchmark models in neural n...

Celý popis

Podrobná bibliografia

Vydané v:	Proceedings of the National Academy of Sciences - PNAS Ročník 116; číslo 12; s. 5451
Hlavní autori:	Barbier, Jean, Krzakala, Florent, Macris, Nicolas, Miolane, Léo, Zdeborová, Lenka
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 19.03.2019
Predmet:	generalized linear model high-dimensional inference approximate message-passing algorithm perceptron Bayesian inference
ISSN:	1091-6490, 1091-6490
On-line prístup:	Zistit podrobnosti o prístupe
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!