Revisiting Neural Processes via Fourier Transform and Volterra Series
Modeling unknown latent functions from finite, irregularly sampled measurements is a recurring challenge across science and engineering. Neural processes (NPs), a family of probabilistic functional models, are promising solutions -- especially when endowed with domain-specific symmetries like translation equivariance, which improve sample efficiency and generalization. Yet existing translation-equivariant NPs face two limitations: (i) they stack generic components with non-linearities, obscuring the induced function class and limiting interpretability; and (ii) convolutional designs are limited by local receptive fields and the need to embed inputs onto a dense uniform grid, while attenti...
| Authors | Peiman Mohseni, Nick Duffield, Raymond K. W. Wong |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 13 July 2026 |
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