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EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring
Our methodology efficiently discovers an interpretable and generalizable zero-cost proxy that gives state of the art score-accuracy correlation on all datasets and search spaces of NASBench-201 and Network Design Spaces (NDS).
A Hardware-Aware System for Accelerating Deep Neural Network Optimization
These methods decouple the super-network training from the sub-network search and thus decrease the computational burden of specializing to different hardware platforms.
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