{
  "id": 66874,
  "title": "CENDRe: Concept Extraction with Natural Domain Representations",
  "url": "https://urgent.news/2026/07/31/cendre-concept-extraction-with-natural-domain-representations",
  "topic": "ai",
  "section": "AI",
  "published": "2026-07-31T16:56:27.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2607.29621v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical domains requires understanding the temporal and spectral patterns that drive their predictions. Concept extraction (CE) methods identify such patterns by analyzing representations within the models' latent space. However, existing time-series CE methods have three limitations:…",
  "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."
}