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• W2895590610 abstract "We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density. Motivated by the success of generative adversarial networks, we construct our samplers using deep neural networks that transform a reference distribution to the target distribution. Training schemes are developed to minimize two variations of the Stein discrepancy, which is designed to work with un-normalized densities. Once trained, our samplers are able to generate samples instantaneously. We show that the proposed methods are theoretically sound and experience fewer convergence issues compared with traditional sampling approaches according to our empirical studies." @default.
• W2895590610 created "2018-10-12" @default.
• W2895590610 creator A5040717619 @default.
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• W2895590610 date "2018-10-08" @default.
• W2895590610 modified "2023-09-27" @default.
• W2895590610 title "Stein Neural Sampler." @default.
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• W2895590610 hasPublicationYear "2018" @default.
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