How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks 事件

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks arXiv:2605.27662v1 Announce Type: cross Abstract: Equivariant neural networks encode geometric symmetries by construction, yet they are often difficult to optimize and can underperform less constrained architectures. A growing body of work addresses this through architectural modifications such as constraint relaxation or approximate equivariance, while the role of the optimizer remains comparatively underexplored. We stu