Stochastic differential equation approximations of generative adversarial network training and its long-run behavior

DOI10.1017/JPR.2023.57MaRDI QIDQ6500023

Xin Guo, Haoyang Cao

Publication date: 10 May 2024

Published in: Journal of Applied Probability (Search for Journal in Brave)

zbMATH Keywords

stochastic differential equationstochastic gradient algorithmgenerative adversarial networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Applications of stochastic analysis (to PDEs, etc.) (60H30) Functional limit theorems; invariance principles (60F17)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On the diffusion approximation of nonconvex stochastic gradient descent
• Bridging the gap between constant step size stochastic gradient descent and Markov chains
• Invariant measures for stochastic nonlinear Schrödinger equations. Numerical approximations and symplectic structures
• An introduction to infinite-dimensional analysis
• Bounds for the Mixing Rate in the Theory of Stochastic Equations
• Deconstructing Generative Adversarial Networks

This page was built for publication: Stochastic differential equation approximations of generative adversarial network training and its long-run behavior