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Equivariance Breaks the Learning Rate

Manolache, Andrei et al.
arXiv (All) · Papers
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machine-learning
Equivariant networks are commonly trained with Adam, yet recent work reports that matrix-structured optimizers such as Muon can perform better on these architectures without explaining why. We identify one source of this difference inside equivariant linear layers. Each irrep block learns a channel-
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