• Home
  • About
  • A Brief History of AI
  • AI-Alerts
  • AI Magazine
  • AAAI Conferences
  • NeurIPS
  • Books
  • Classics

A new perspective on building efficient and expressive 3D equivariant graph neural networks

Feb-17-2026, 06:52:53 GMT–Neural Information Processing Systems 

Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.

  artificial intelligence, machine learning, survey article, (19 more...)

Neural Information Processing Systems

Feb-17-2026, 06:52:53 GMT

Conferences    PDF

Add feedback

  • Country:
    • Asia > China
      • Guangxi Province > Nanning (0.04)
    • North America > United States
      • Texas > Brazos County > College Station (0.04)
    • South America > Chile
      • Santiago Metropolitan Region > Santiago Province > Santiago (0.04)
  • Genre:
    • Overview (0.92)
    • Research Report (0.67)
  • Industry:
    • Energy (0.46)
    • Health & Medicine > Pharmaceuticals & Biotechnology (0.46)
  • Technology:
    • Information Technology > Artificial Intelligence > Machine Learning
      • Neural Networks > Deep Learning (1.00)
      • Statistical Learning (1.00)

  • By text
  • By views
  • By concept tags

Duplicate Docs Excel Report

Title
A new perspective on building efficient and expressive 3D equivariant graph neural networks
d212c6c26603c0eb3c9a6b6432386a1f-Paper-Conference.pdf
d212c6c26603c0eb3c9a6b6432386a1f-Supplemental-Conference.pdf

Similar Docs  Excel Report  more

TitleSimilaritySource
None found

Site Feedback

© 2026, i2k Connect Inc  ·  All Rights Reserved.
Privacy policy  ·  Terms of use  ·  License  ·  Legal Notices
This is i2kweb version 7.1.0-SNAPSHOT. Logged in as aitopics-guest for 59 more minutes (idle timeout).

Site Feedback

powered by
i2k Connect

aitopics.org uses cookies to deliver the best possible experience. By continuing to use this site, you consent to the use of cookies. Learn more ยป

Add feedback

Send feedback to help us improve this new enhanced search experience.

Thank You!