The Dynamics of Quasiregular Neural Learning
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Computer Science > Machine Learning
Title:The Dynamics of Quasiregular Neural Learning
Abstract:Many learning problems combine a dominant regularity with systematic exceptions. Motivated by U-shaped learning in language acquisition, we study this interaction in controlled quasiregular regression problems where regular and exceptional solutions are explicitly known. Neural networks can partially acquire exceptions, subsequently regress toward the dominant regularity, and finally recover. This overregularization becomes substantially stronger when exceptions are rare, despite their early acquisition, but does not emerge equally across all regularities considered. Our results isolate a simple form of competition between regularities and exceptions during neural learning.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.26018 [cs.LG] |
| (or arXiv:2609.26018v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26018
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Matthia Sabatelli [view email][v1] Tue, 22 Sep 2026 11:21:51 UTC (1,302 KB)
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