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New neural process methods leverage Fourier and Volterra series

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods to improve neural processes (NPs), a type of probabilistic model used for function approximation from limited data. Their work addresses limitations in existing translation-equivariant NPs by introducing set Fourier convolutions (SFConvs) and leveraging Volterra expansions. These innovations enable models to operate on irregularly sampled points with global receptive fields and linear scaling, offering greater interpretability and efficiency. AI

IMPACT Introduces novel techniques for function approximation, potentially improving performance in scientific and engineering applications.

RANK_REASON The cluster contains an academic paper detailing new methods for neural processes.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New neural process methods leverage Fourier and Volterra series

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The cluster contains an academic paper detailing new methods for neural processes.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Peiman Mohseni, Nick Duffield, Raymond K. W. Wong ·

    Revisiting Neural Processes via Fourier Transform and Volterra Series

    arXiv:2606.01172v1 Announce Type: cross Abstract: 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 …

  2. arXiv stat.ML TIER_1 English(EN) · Raymond K. W. Wong ·

    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 sy…

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