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Feb, 2024
基于张量列车逼近的生成模型与Hamilton-Jacobi-Bellman方程
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
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David Sommer, Robert Gruhlke, Max Kirstein, Martin Eigel, Claudia Schillings
TL;DR
使用压缩多项式在张量列车(Tensor Train)格式上解决Hamilton-Jacobi-Bellman(HJB)方程的直接时间积分方法,以避免样本、标准化常数和高维度的问题。
Abstract
Sampling from
probability densities
is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of
reverse-time diffusion processes
dependin
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