AWhat changed
Combines Python object-oriented GP model interfaces with TensorFlow automatic differentiation, variational/sparse and sampling methods, contributing differentiable Cholesky and GPU triangular-solve support.
Scientific scope
Combines Python object-oriented GP model interfaces with TensorFlow automatic differentiation, variational/sparse and sampling methods, contributing differentiable Cholesky and GPU triangular-solve support. Approximate inference remains approximate outside exact-Gaussian cases; original2016test-coverage/timing reports are not current-library audits or universal speedups. Existing GP inference formulas, TensorFlow and blocked Cholesky differentiation algorithms are inherited. General numerical software only, not an application-domain achievement.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Approximate inference remains approximate outside exact-Gaussian cases; original2016test-coverage/timing reports are not current-library audits or universal speedups. Existing GP inference formulas, TensorFlow and blocked Cholesky differentiation algorithms are inherited. General numerical software only, not an application-domain achievement.
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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