{
  "id": "2304.01762",
  "version": "v1",
  "published": "2023-04-04T12:51:35.000Z",
  "updated": "2023-04-04T12:51:35.000Z",
  "title": "Incorporating Unlabelled Data into Bayesian Neural Networks",
  "authors": [
    "Mrinank Sharma",
    "Tom Rainforth",
    "Yee Whye Teh",
    "Vincent Fortuin"
  ],
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "We develop a contrastive framework for learning better prior distributions for Bayesian Neural Networks (BNNs) using unlabelled data. With this framework, we propose a practical BNN algorithm that offers the label-efficiency of self-supervised learning and the principled uncertainty estimates of Bayesian methods. Finally, we demonstrate the advantages of our approach for data-efficient learning in semi-supervised and low-budget active learning problems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-04-04T12:51:35.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "bayesian neural networks",
      "incorporating unlabelled data",
      "learning better prior distributions",
      "practical bnn algorithm",
      "principled uncertainty estimates"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}