1. 20 Sep 2026

    Why AI Scaling Hits a Wall: The Hidden Ceiling Behind Large Language Models

    ... deep geometric, statistical, and optimization ... International Conference on Machine Learning (ICML 2026), arXiv preprint arXiv:2602.05970.

    c3.unu.edu ↗
  2. 16 Sep 2026

    Young Applied Mathematicians Conference (YAMC) | Politecnico di Torino

    The topics covered may include, but are not limited to: Machine Learning, Deep Reinforcement Learning, Geometric Deep Learning, Generative Models, ...

    www.polito.it ↗
  3. 16 Sep 2026

    Young Applied Mathematicians Conference (YAMC) | Politecnico di Torino

    The topics covered may include, but are not limited to: Machine Learning, Deep Reinforcement Learning, Geometric Deep Learning, Generative Models, ...

    www.polito.it ↗
  4. 14 Sep 2026

    New AI approaches to help understand complex biological data - Technology Org

    In the first paper, VBA: Vector Bundle Attention for Intrinsically Geometric Representation Learning, the researchers introduce Vector Bundle ...

    www.technology.org ↗
  5. 01 Sep 2026

    New AI approaches to help understand complex biological data - Phys.org

    In the first paper, "VBA: Vector Bundle Attention for intrinsically geometric representation learning," the researchers introduce Vector Bundle ...

    phys.org ↗
  6. 01 Sep 2026

    AI Breakthroughs Unravel Complex Biological Data - Mirage News

    The studies, VBA: Vector Bundle Attention for Intrinsically Geometric Representation Learning and Dynamic Fractal Mamba: A Neural Renormalization ...

    www.miragenews.com ↗
  7. 25 Aug 2026

    Navigating the Materials Space with Machine-Learning-Generated Electronic Fingerprints

    Lenssen, Fast graph representation learning with PyTorch geometric, arXiv:1903.02428. G. Kresse and J. Hafner, Ab initio molecular dynamics for ...

    journals.aps.org ↗
Sources

Where this comes from

Artificial Intelligence — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/1990879549…

401 items Polled 22 Sep, 12:14 UTC 200

Agentic AI — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/4849751788…

401 items Polled 22 Sep, 12:14 UTC 200

AI Agents — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/4849751788…

401 items Polled 22 Sep, 12:14 UTC 200

Data Science — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/4836803184…

401 items Polled 22 Sep, 12:14 UTC 200

Large Language Model — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/8510459957…

401 items Polled 22 Sep, 12:14 UTC 200

Machine Learning — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/4836803184…

401 items Polled 22 Sep, 12:14 UTC 200

Deep Learning — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/1643871049…

401 items Polled 22 Sep, 12:14 UTC 200

LLMs Alignment — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/5121641697…

45 items Polled 22 Sep, 12:14 UTC 200

AI Alignment — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/1764885284…

401 items Polled 22 Sep, 12:14 UTC 200

Representation Learning — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/1758013403…

146 items Polled 22 Sep, 12:14 UTC 200

Reinforcement Learning — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/1720175169…

401 items Polled 22 Sep, 12:14 UTC 200

Generative AI — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/7811591585…

401 items Polled 22 Sep, 12:14 UTC 200

Multimodal AI — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/9568555102…

401 items Polled 22 Sep, 12:14 UTC 200

Mechanistic Interpretability — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/8541059511…

33 items Polled 22 Sep, 12:14 UTC 200

RLHF — Google

https://www.google.co.in/alerts/feeds/05832220720342067762/7811591585…

32 items Polled 22 Sep, 12:14 UTC 200

Feeds are configured through the LEARN_FEEDS environment variable, so new sources can be added without a code change. Each poll sends the stored ETag and Last-Modified headers, so an unchanged feed answers 304 and costs the publisher nothing.