AWhat changed
Aggregates permutation-invariant context representations for conditional predictions; latent Neural Processes add a shared global latent to generate coherent function samples.
Scientific scope
Aggregates permutation-invariant context representations for conditional predictions; latent Neural Processes add a shared global latent to generate coherent function samples. The deterministic CNP's factored target predictions lose cross-target covariance and need not be a consistent Bayesian family across context sets. The latent extension uses learned variational conditionals, not exact GP posterior inference; replacing an intractable conditional prior changes the ideal objective. Linear context/target scaling is architecture-specific. Related sources bundled with separate9/7bylines.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The deterministic CNP's factored target predictions lose cross-target covariance and need not be a consistent Bayesian family across context sets. The latent extension uses learned variational conditionals, not exact GP posterior inference; replacing an intractable conditional prior changes the ideal objective. Linear context/target scaling is architecture-specific. Related sources bundled with separate9/7bylines.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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