Stein variational gradient descent with learned direction

DOI10.1016/j.ins.2023.118975OpenAlexW4366439784MaRDI QIDQ6124701

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Publication date: 28 March 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2023.118975

zbMATH Keywords

posteriorneural networkBayesian modelingStein discrepancy

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Numerical optimization and variational techniques (65K10) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Stein's density approach and information inequalities
• Multilayer feedforward networks are universal approximators
• Asymptotic evaluation of certain markov process expectations for large time. IV
• Universal approximation bounds for superpositions of a sigmoidal function
• Integral Probability Metrics and Their Generating Classes of Functions
• Scaling Limit of the Stein Variational Gradient Descent: The Mean Field Regime
• Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
• Prescribing a System of Random Variables by Conditional Distributions
• Approximation by superpositions of a sigmoidal function

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