June 11, 2024, 4:48 a.m. | Ziqiao Wang, Yongyi Mao

cs.LG updates on arXiv.org arxiv.org

arXiv:2211.10691v2 Announce Type: replace
Abstract: Stochastic differential equations (SDEs) have been shown recently to characterize well the dynamics of training machine learning models with SGD. When the generalization error of the SDE approximation closely aligns with that of SGD in expectation, it provides two opportunities for understanding better the generalization behaviour of SGD through its SDE approximation. Firstly, viewing SGD as full-batch gradient descent with Gaussian gradient noise allows us to obtain trajectory-based generalization bound using the information-theoretic bound from …

abstract approximation arxiv cs.it cs.lg differential dynamics error information lens machine machine learning machine learning models math.it replace stochastic terminal training type via

Senior Data Engineer

@ Displate | Warsaw

Automation and AI Strategist (Remote - US)

@ MSD | USA - New Jersey - Rahway

Assistant Manager - Prognostics Development

@ Bosch Group | Bengaluru, India

Analytics Engineer - Data Solutions

@ MSD | IND - Maharashtra - Pune (Wework)

Jr. Data Engineer (temporary)

@ MSD | COL - Cundinamarca - Bogotá (Colpatria)

Senior Data Engineer

@ KION Group | Atlanta, GA, United States