{"slug": "the-dynamics-of-quasiregular-neural-learning", "title": "The Dynamics of Quasiregular Neural Learning", "summary": "A study by Matthia Sabatelli, published on arXiv as 2609.26018v1, found that neural networks can partially acquire exceptions to a dominant regularity, then regress toward that regularity, and finally recover — a pattern the paper calls overregularization, motivated by U-shaped learning in language acquisition. The overregularization effect becomes substantially stronger when exceptions are rare, despite their early acquisition, and does not emerge equally across all regularities tested. The results isolate a simple form of competition between regularities and exceptions during neural learning.", "body_md": "# The Dynamics of Quasiregular Neural Learning\n\nBy Matthia SabatelliSource: \n\n[arXiv cs.LG](https://arxiv.org/list/cs.LG/recent)\narXiv:2609.26018v1 Announce Type: new \nAbstract: 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 \n\n[regression](https://www.machinebrief.com/glossary/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.\nGet AI news in your inbox\n\nDaily digest of what matters in AI.", "url": "https://wpnews.pro/news/the-dynamics-of-quasiregular-neural-learning", "canonical_source": "https://www.machinebrief.com/news/the-dynamics-of-quasiregular-neural-learning-v8ls", "published_at": "2026-09-23 04:00:00+00:00", "updated_at": "2026-09-23 04:55:17.673686+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": ["Matthia Sabatelli", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/the-dynamics-of-quasiregular-neural-learning", "markdown": "https://wpnews.pro/news/the-dynamics-of-quasiregular-neural-learning.md", "text": "https://wpnews.pro/news/the-dynamics-of-quasiregular-neural-learning.txt", "jsonld": "https://wpnews.pro/news/the-dynamics-of-quasiregular-neural-learning.jsonld"}}