
This paper establishes quantitative convergence results for the value functions and optimal parameters of neural SDEs as the sample size grows to infinity. The authors analyze the Hamilton-Jacobi-Bellman equation corresponding to the N-particle system and obtain uniform regularity estimates, which are then used to show the convergence of the minima of objective functionals and optimal parameters.


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convergence-analysis-of-controlled-particle-systems-in-deep-learning-from-finite-to-infinite-sample-size


Convergence Analysis of Controlled Particle Systems in Deep Learning: From Finite to Infinite Sample Size
