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Neural network symmetry breaking at edge of stability analyzed

By PulseAugur Editorial · [1 sources] ·

Researchers have analyzed how continuous symmetries in neural networks are affected by gradient flow, particularly at the edge of stability. They found that finite-step gradient descent can break these conservation laws, leading to oscillations around a smoother trajectory described by central flow dynamics. The study investigates the curvature correction's impact on these dynamics, showing how it induces a drift determined by oscillation covariance in critical parameter directions. This analysis, applied to scalar two-layer models, two-layer matrix factorization, and two-layer ReLU networks, reveals which conservation laws are preserved and which are broken at the edge of stability. AI

IMPACT Provides theoretical insights into neural network training dynamics, potentially informing future model architectures and optimization techniques.

RANK_REASON Academic paper on neural network dynamics. [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 →

Neural network symmetry breaking at edge of stability analyzed

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Academic paper on neural network dynamics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Naima Elosegui Borras, Georgios Arvanitidis ·

    Symmetry Breaking at the Edge of Stability via Central Flow Analysis

    arXiv:2611.06718v1 Announce Type: new Abstract: Continuous symmetries in neural networks lead to conserved quantities that are invariant under gradient flow, such as layer balancedness in linear and ReLU networks. Finite-step gradient descent need not preserve these laws, particu…

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