University of Bergen's GsCNN Mixes Scalars and Vectors for Spherical AI

A new SO(3)-equivariant architecture lifts the kernel and nonlinearity constraints of spherical CNNs, improving wind forecasts and spherical MNIST tasks.

Read the full story at AlphaSignal ↗

Timeline · 1 report

2026-11-02 18:00 · AlphaSignal
University of Bergen's GsCNN Mixes Scalars and Vectors for Spherical AI

More stories

Introducing the Grid Protectors Program — OpenAI News
Anthropic reports $17B quarterly revenue but losses continue in fresh IPO filing — Reuters AI
Marvell to acquire optical components maker Lumentum in $115 billion all-stock deal — DigiTimes Asia
OpenAI weighs rebranding data centers as 'AI compute campuses' amid growing public backlash — The Verge AI
October 2026 AI roundup highlights local LLM releases and Google announcements — Google AI Blog
Anthropic limits Claude Code to prevent recreation of existing video games — Engadget
OpenAI to merge ChatGPT chat and work tabs with shared usage allowance — TechCrunch AI
Claude Code ecosystem expands as tools, agents and a security scare reshape developer workflows — Microsoft Research Blog

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