Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding

Stein variational gradient descent (SVGD) is a prominent particle-based variational inference method used for sampling a target distribution. In this paper, we propose two novel trainable algorithms based on SVGD: deep-unfolded SVGD (DUSVGD) and Chebyshev-step based DUSVGD (C-DUSVGD). DUSVGD incorpo...

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Bibliografiske detaljer

Principais autores:	Yuya Kawamura, Satoshi Takabe
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2024-01-01
Serier:	IEEE Access
Fag:	Deep learning deep unfolding particle-based variational inference Stein variational gradient descent
Online adgang:	https://ieeexplore.ieee.org/document/10770226/
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