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- W3136198352 abstract "Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous analysis of the standard maximum-likelihood objective used to train conditional NPs. Moreover, we propose a new member to the Neural Process family called the Gaussian Neural Process (GNP), which models predictive correlations, incorporates translation equivariance, provides universal approximation guarantees, and demonstrates encouraging performance." @default.
- W3136198352 created "2021-03-29" @default.
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- W3136198352 date "2021-01-10" @default.
- W3136198352 modified "2023-09-27" @default.
- W3136198352 title "The Gaussian Neural Process" @default.
- W3136198352 hasPublicationYear "2021" @default.
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