AINewsnow

Fractional Laplace Neural Operators: Exact Architectures, an Expressivity Frontier at Criticality, and Certified Stability for Memory-Driven Network Dynamics

arXiv:2610.00515v1 Announce Type: new Abstract: Neural operators learn maps between function spaces, while hereditary network dynamics are described by Volterra resolvents with non-rational Laplace symbols. We introduce a fractional Laplace neural operator (fLNO) that embeds this structure in the l…

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-10-02 04:00 · arXiv stat.ML
    Fractional Laplace Neural Operators: Exact Architectures, an Expressivity Frontier at Criticality, and Certified Stability for Memory-Driven Network Dynamics

More stories

  1. Bring near-Astra intelligence to everyday work with GPT-6.1 Sol on Amazon Bedrock — AWS Machine Learning Blog
  2. Gemini 4 Argon: our next era of frontier intelligence — Google Gemini Blog
  3. Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs — Allen Institute for AI (Ai2)
  4. Google Releases New Gemini Model With Guardrails Amid A.I. Safety Debate — New York Times Technology
  5. OpenAI scraps release of its latest AI model over safety concerns — France 24 — Artificial Intelligence
  6. Introducing GPT-6.1 Sol — OpenAI News
  7. Google tests its plan for AI data centers in space with Project Suncatcher — Scientific American
  8. OpenAI’s Dots Are Always-On AI Agents—and Its Answer to Meta’s Muse — Wired AI

Get the daily brief of stories like this at 6:30 every morning →