{
  "id": "1908.01394",
  "version": "v1",
  "published": "2019-08-04T20:29:28.000Z",
  "updated": "2019-08-04T20:29:28.000Z",
  "title": "Learning to Transport with Neural Networks",
  "authors": [
    "Andrea Schioppa"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "We compare several approaches to learn an Optimal Map, represented as a neural network, between probability distributions. The approaches fall into two categories: ``Heuristics'' and approaches with a more sound mathematical justification, motivated by the dual of the Kantorovitch problem. Among the algorithms we consider a novel approach involving dynamic flows and reductions of Optimal Transport to supervised learning.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-08-04T20:29:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural network",
      "optimal transport",
      "optimal map",
      "probability distributions",
      "novel approach"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}