{
  "id": "1407.7417",
  "version": "v1",
  "published": "2014-07-28T13:44:25.000Z",
  "updated": "2014-07-28T13:44:25.000Z",
  "title": "'Almost Sure' Chaotic Properties of Machine Learning Methods",
  "authors": [
    "Nabarun Mondal",
    "Partha P. Ghosh"
  ],
  "comment": "10 pages : to be submitted to Theoretical Computer Science. arXiv admin note: text overlap with arXiv:1111.4949",
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "It has been demonstrated earlier that universal computation is 'almost surely' chaotic. Machine learning is a form of computational fixed point iteration, iterating over the computable function space. We showcase some properties of this iteration, and establish in general that the iteration is 'almost surely' of chaotic nature. This theory explains the observation in the counter intuitive properties of deep learning methods. This paper demonstrates that these properties are going to be universal to any learning method.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-07-28T13:44:25.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "03D10",
      "65P20",
      "68Q05",
      "68Q87",
      "68T05"
    ],
    "keywords": [
      "machine learning methods",
      "chaotic properties",
      "computational fixed point iteration",
      "computable function space",
      "universal computation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1407.7417M"
    }
  }
}