HPNAS-RL jointly searches RL architectures and hyperparameters for driving
In a driving simulator the joint search beat standalone NAS, producing higher-reward policies with fewer parameters.
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In a driving simulator the joint search beat standalone NAS, producing higher-reward policies with fewer parameters.
Architecture search yields a 409-parameter Sentinel-2 bloom model, lifting AUC to 0.820.
PPNAS combines scarce architecture labels with zero-cost proxies for label-efficient neural architecture search.
CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.
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