EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring. (arXiv:2209.07413v2 [cs.LG] UPDATED)

Neural Architecture Search (NAS) has significantly improved productivity in
the design and deployment of neural networks (NN). As NAS typically evaluates
multiple models by training them partially or completely, the improved
productivity comes at the cost of significant carbon footprint. To alleviate
this expensive training routine, zero-shot/cost proxies analyze an NN at
initialization to generate a score, which correlates highly with its true
accuracy. Zero-cost proxies are currently designed by experts conducting
multiple cycles of empirical testing on possible algorithms, …

architecture arxiv cost proxies scoring