Paper recorded by Signals 4 on 2026-08-31 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01
Category: cs.LG · 机器学习 · first seen 2026-09-01
Rotational symmetry is one of the most important structural principles in machine learning on 3D data. In applications ranging from physics and materials science to 3D computer vision, predictions should not depend on an arbitrary choice of coordinate frame. Rotational equivariance captures this requirement mathematically by enforcing that a rotation of the input induces a corresponding transforma