{
  "id": "2107.07343",
  "version": "v1",
  "published": "2021-07-04T15:15:36.000Z",
  "updated": "2021-07-04T15:15:36.000Z",
  "title": "Mutation is all you need",
  "authors": [
    "Lennart Schneider",
    "Florian Pfisterer",
    "Martin Binder",
    "Bernd Bischl"
  ],
  "comment": "Accepted for the 8th ICML Workshop on Automated Machine Learning (2021). 10 pages, 1 table, 3 figures",
  "categories": [
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks. BANANAS is one state-of-the-art NAS method that is embedded within the Bayesian optimization framework. Recent experimental findings have demonstrated the strong performance of BANANAS on the NAS-Bench-101 benchmark being determined by its path encoding and not its choice of surrogate model. We present experimental results suggesting that the performance of BANANAS on the NAS-Bench-301 benchmark is determined by its acquisition function optimizer, which minimally mutates the incumbent.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-07-04T15:15:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural architecture search",
      "acquisition function optimizer",
      "deep neural networks",
      "state-of-the-art nas method",
      "bayesian optimization framework"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}