The Gaussian Neural ProcessDownload PDF

23 Nov 2020 (modified: 10 Jan 2021)AABI2020Readers: Everyone
  • Keywords: Meta-Learning, Neural Processes, Gaussian Processes
  • 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.
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