{
  "id": "2408.16686",
  "version": "v1",
  "published": "2024-08-29T16:32:24.000Z",
  "updated": "2024-08-29T16:32:24.000Z",
  "title": "CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes",
  "authors": [
    "Rahul Khorana"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "We present a novel framework for learning on CW-complex structured data points. Recent advances have discussed CW-complexes as ideal learning representations for problems in cheminformatics. However, there is a lack of available machine learning methods suitable for learning on CW-complexes. In this paper we develop notions of convolution and attention that are well defined for CW-complexes. These notions enable us to create the first neural network that can receive a CW-complex as input. We illustrate and interpret this framework in the context of supervised prediction.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-08-29T16:32:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional networks",
      "attention networks",
      "cw-complex structured data points",
      "first neural network",
      "ideal learning representations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}