Machine learning

Neural Architecture Search

Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.

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Sources

  1. Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link
  2. Liu, H. et al. (2019). DARTS: Differentiable Architecture Search. ICLR. link

Related methods

Referenced by

ScholarGateNeural Architecture Search (Neural Architecture Search (NAS)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/neural-architecture-search