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We propose two applications of our setting: (i) resolving eigenbasis ambiguities in spectral graph neural networks, and (ii) handling rotational symmetries in point clouds. We empirically validate our methods on molecular and protein classification, as well as point cloud classification tasks. Our adaptive canonicalization outperforms the three other common solutions to equivariant machine learning: data augmentation, standard canonicalization, and equivariant architectures.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.24886 [cs.LG] |
| (or arXiv:2509.24886v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2509.24886 arXiv-issued DOI via DataCite |
From: Ya-Wei Eileen Lin [view email]
[v1]
Mon, 29 Sep 2025 14:59:46 UTC (125 KB)
[v2]
Mon, 1 Dec 2025 10:37:22 UTC (13,272 KB)
[v3]
Thu, 16 Apr 2026 13:48:06 UTC (13,305 KB)
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