{
  "id": 9469579,
  "title": "Learning Holographic Reduced Representations with Clifford Variational Autoencoders",
  "url": "https://urgent.news/2026/09/23/learning-holographic-reduced-representations-with-clifford",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-23T17:10:56.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.28409v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly generated atomic vector symbols and fractional power encodings of real-valued data. Embedding unstructured data remains an open question. We present \\textit{Clifford-VAE}, a variational autoencoder that learns to project data onto a Clifford torus in…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}