{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/neural-architecture-search",
  "headline": "Neural Architecture Search: Automated Design of Deep Neural Networks",
  "description": "Neural Architecture Search (NAS) automates the design of neural network architectures — replacing years of human intuition and trial-and-error with algorithmic search. From RL-based methods consuming thousands of GPU-hours to modern supernet-based approaches finding architectures in hours, NAS is democratizing optimal network design.",
  "dateCreated": "2026-05-24T02:49:13.641Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Neural Architecture Search: Methods, Applications, and Theory (Nature Collection)",
      "sameAs": "https://www.nature.com/collections/adjaeijhja"
    },
    {
      "@type": "CreativeWork",
      "name": "Generative neural architecture search (GNAS)",
      "sameAs": "https://www.sciencedirect.com/science/article/pii/S092523122501032X"
    }
  ]
}