{
  "id": "1605.09081",
  "version": "v1",
  "published": "2016-05-30T00:50:39.000Z",
  "updated": "2016-05-30T00:50:39.000Z",
  "title": "Understanding Convolutional Neural Networks",
  "authors": [
    "Jayanth Koushik"
  ],
  "comment": "Statistical Machine Learning Course Project at Carnegie Mellon University",
  "categories": [
    "stat.ML",
    "cs.AI",
    "cs.CV",
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "Convoulutional Neural Networks (CNNs) exhibit extraordinary performance on a variety of machine learning tasks. However, their mathematical properties and behavior are quite poorly understood. There is some work, in the form of a framework, for analyzing the operations that they perform. The goal of this project is to present key results from this theory, and provide intuition for why CNNs work.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-05-30T00:50:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "understanding convolutional neural networks",
      "convoulutional neural networks",
      "machine learning tasks",
      "extraordinary performance",
      "cnns work"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}