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RiPPLE introduces a method for ranking neural architectures by training a small set of anchors to early stages, extrapolating learning curves, and propagating labels, achieving efficient and accurate performance prediction across search spaces.
Tyto by ai-coustics is a tool that provides audio insights to predict voice AI performance.
This paper studies performance prediction for symbolic (e.g., Python) and prompt programs using a Bayesian coin-flip model, finding that symbolic programs have all-or-nothing performance while prompt programs have diffuse priors, and introduces RAP (Retrieved Approximate Prior) for performance prediction.