FAPS is a new function-space posterior sampling method built on flow-matching priors that unifies stochastic-process regression and PDE inverse problems while avoiding explicit prior density evaluation.
Bruinsma, Andrew Y
6 Pith papers cite this work. Polarity classification is still indexing.
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APIC applies Neural Processes in a two-branch latent model to amortize Kennedy-O'Hagan-style calibration, separating instance-specific parameters from shared structural discrepancies for fast inference on new realizations.
Set Fourier convolutions plus a Volterra cascade yield scalable, translation-equivariant CNPs that handle irregular inputs with global receptive fields and beat strong baselines.
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.
STNPs extend TNPs with a spectral aggregator that estimates context spectra, forms spectral mixtures, and injects task-adaptive frequency features to better handle periodicity.
A revised DMBN with positional time encoding improves temporal representation and generalization in neural processes for multimodal robotic action prediction.
citing papers explorer
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Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems
FAPS is a new function-space posterior sampling method built on flow-matching priors that unifies stochastic-process regression and PDE inverse problems while avoiding explicit prior density evaluation.
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APIC: Amortized Physics-Informed Calibration using Neural Processes
APIC applies Neural Processes in a two-branch latent model to amortize Kennedy-O'Hagan-style calibration, separating instance-specific parameters from shared structural discrepancies for fast inference on new realizations.
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Revisiting Neural Processes via Fourier Transform and Volterra Series
Set Fourier convolutions plus a Volterra cascade yield scalable, translation-equivariant CNPs that handle irregular inputs with global receptive fields and beat strong baselines.
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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.
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Spectral Transformer Neural Processes
STNPs extend TNPs with a spectral aggregator that estimates context spectra, forms spectral mixtures, and injects task-adaptive frequency features to better handle periodicity.
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Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction
A revised DMBN with positional time encoding improves temporal representation and generalization in neural processes for multimodal robotic action prediction.