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Grokking transition in neural networks quantified, data complexity key

By PulseAugur Editorial · [1 sources] ·

Researchers have quantified the transition from memorization to generalization in neural networks, a phenomenon known as grokking. They discovered a power-law scaling relation for the onset time of grokking, indicating that data complexity is a more significant driver than model capacity. The study also identified a sharp phase boundary related to weight decay and observed monotonic compression of weight norms during the transition, suggesting implicit regularization favors simpler solutions. AI

IMPACT Provides a quantitative framework for predicting and controlling generalization transitions in overparameterized networks.

RANK_REASON Academic paper detailing a new quantitative analysis of a machine learning phenomenon. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Grokking transition in neural networks quantified, data complexity key

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Academic paper detailing a new quantitative analysis of a machine learning phenomenon. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anish Kataria ·

    Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking

    arXiv:2609.10657v1 Announce Type: cross Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of …

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