AWhat changed
Replaced conditionally affine scalar transforms by monotonic neural networks, including deep sigmoidal flow modules for richer density estimation and variational posterior approximation.
Scientific scope
Replaced conditionally affine scalar transforms by monotonic neural networks, including deep sigmoidal flow modules for richer density estimation and variational posterior approximation. The source's universal-approximation statements require positive continuous densities and convergence in distribution, not exact finite-network representation of arbitrary discrete or singular laws. Monotonicity needs positivity/activation assumptions; invertibility alone does not give a cheap closed-form inverse. Theorems were read as statements, not independently replayed.
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 source's universal-approximation statements require positive continuous densities and convergence in distribution, not exact finite-network representation of arbitrary discrete or singular laws. Monotonicity needs positivity/activation assumptions; invertibility alone does not give a cheap closed-form inverse. Theorems were read as statements, not independently replayed.
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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