AWhat changed
Relaxes discrete operation choices into softmax mixtures and approximates a train/validation bilevel objective with alternating weight and architecture-gradient updates before discretization.
Scientific scope
Relaxes discrete operation choices into softmax mixtures and approximates a train/validation bilevel objective with alternating weight and architecture-gradient updates before discretization. Gradient-based search selected architectures within a researcher-defined continuous relaxation; human authors designed the method, search space and evaluation. One-step unrolling and finite differences approximate the bilevel gradient. The original explicitly gives no convergence guarantee; the relaxed optimum need not preserve performance after discrete projection.
Evidence
Gradient-based search selected architectures within a researcher-defined continuous relaxation; human authors designed the method, search space and evaluation. WRS has not independently rerun these experiments. Gradient-based search selected architectures within a researcher-defined continuous relaxation; human authors designed the method, search space and evaluation. One-step unrolling and finite differences approximate the bilevel gradient. The original explicitly gives no convergence guarantee; the relaxed optimum need not preserve performance after discrete projection.
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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