Neural Operator Processes (NOPs) unify neural-process conditioning with neural-operator decoding for probabilistic full-field prediction from sparse joint input-output observations.
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces SFConvCNPs and SFVConvCNPs using set Fourier convolutions and Volterra expansions for translation-equivariant neural processes on irregular data with global receptive fields and linear scaling.
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
citing papers explorer
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Neural Operator Processes for Probabilistic Operator Learning under Partial Observations
Neural Operator Processes (NOPs) unify neural-process conditioning with neural-operator decoding for probabilistic full-field prediction from sparse joint input-output observations.
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Revisiting Neural Processes via Fourier Transform and Volterra Series
Introduces SFConvCNPs and SFVConvCNPs using set Fourier convolutions and Volterra expansions for translation-equivariant neural processes on irregular data with global receptive fields and linear scaling.
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Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.