Research paper analyzing gradient flows in rotation representations for neural networks. Proposes training without orthogonalization constraints and applying SVD at inference time, potentially reducing training overhead while maintaining performance.
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Training Without Orthogonalization, Inference With SVD: A Gradient Analysis of Rotation Representations
Researchers propose training neural networks without orthogonalization constraints, then applying SVD only at inference time, potentially reducing training overhead while maintaining rotation representation quality.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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