Learnability of high-dimensional targets by two-parameter models and gradient flow

Autor:	Yarotsky, Dmitry
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We explore the theoretical possibility of learning $d$-dimensional targets with $W$-parameter models by gradient flow (GF) when $WComment: Camera-ready NeurIPS 2024 version; some extra comments and figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.17089 Zobrazit plný text záznamu View this record from Arxiv