• Corpus ID: 102352813

ASAP: Architecture Search, Anneal and Prune

@inproceedings{Noy2019ASAPAS,
  title={ASAP: Architecture Search, Anneal and Prune},
  author={Asaf Noy and Niv Nayman and T. Ridnik and Nadav Zamir and Sivan Doveh and Itamar Friedman and Raja Giryes and Lihi Zelnik-Manor},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2019}
}
Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models. Yet, their main drawback is the computational complexity of the search process. As some primal methods optimized over a discrete search space, thousands of days of GPU were required for convergence. A recent approach is based on constructing a differentiable search space that enables gradient-based optimization, which reduces the search time to a few days. While successful, it… 

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