{
  "id": 19067,
  "url": "https://arxiv.org/abs/2608.12084v1",
  "title": "NAE: Normalizing AutoEncoder",
  "summary": "We consider the setting of Normalizing flows with approximate inverses, an established paradigm spanning both full-dimensional ($d=D$) and bottleneck ($d<D$) settings, and group these models under the term flow autoencoders. We present a theoretical investigation into their training dynamics and prove that the proposed loss used by existing approaches is suboptimal; specifically, both encoder and decoder surrogates must be optimized in alignment with reconstruction loss. Guided by these insights",
  "authors": "Muhammad Abdur Rafae, Niels Landwehr",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T14:07:09.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19067",
  "original_url": "https://arxiv.org/abs/2608.12084v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}