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arXiv Machine Learning By Eunho Jeong, Joon Jang, Hyeonjin Kim

Asymmetry Tunneling Beyond Adaptive Equivariance Relaxation

Read the original on arXiv Machine Learning →

The paper investigates whether adaptive control of equivariance relaxation is necessary for neural networks to capture symmetry-breaking structure in data. By adding a residual path to a strictly equivariant model and limiting their interaction, the authors observe that the residual can either collapse or survive, a phenomenon they term asymmetry tunneling. They analyze this behavior across five benchmarks using four diagnostics and conclude that adaptive control of equivariance relaxation is not required to model symmetry-breaking patterns.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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