{
  "id": 17905451,
  "title": "Lightweight Representation Learning for Low Tucker Rank Tensors",
  "url": "https://urgent.news/2026/10/31/lightweight-representation-learning-for-low-tucker-rank-tensors",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-31T18:09:13.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.01168v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Low Tucker rank tensors provide compact representations of multidimensional dependencies, but standard learning pipelines can incur substantial computational and memory overhead from redundant intermediate quantities. We reduce this overhead without modifying the underlying representation objective or parameterization. Our approach combines three complementary components: (i) sequential…",
  "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."
}