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New paper analyzes critical initialization effects on neural network derivatives

By PulseAugur Editorial · [2 sources] ·

A new paper explores the critical initialization of wide scalar-input networks, focusing on how it affects higher input derivatives. Researchers found that while the "edge-of-chaos" condition preserves first-order perturbations, it destabilizes higher derivatives crucial for physics-informed losses and score matching. The study derives mean-field recursions to analyze these effects, showing that second-derivative variance grows linearly with depth when activation functions have non-zero curvature. For residual networks, the variance of derivatives is proven to remain bounded. AI

IMPACT Provides theoretical insights into neural network initialization, potentially guiding future model architectures and training stability.

RANK_REASON The cluster contains an academic paper detailing novel research findings in neural network initialization.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New paper analyzes critical initialization effects on neural network derivatives

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The cluster contains an academic paper detailing novel research findings in neural network initialization.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Critical initialization destabilizes higher input derivatives in wide scalar-input networks

    The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matching and derivative regularization depend on higher input derivatives. For smooth scalar-input fully connected networks, using a joi…

  2. arXiv stat.ML TIER_1 English(EN) · Prashant Singh, Pranav Singh ·

    Critical initialization destabilizes higher input derivatives in wide scalar-input networks

    arXiv:2609.09244v1 Announce Type: new Abstract: The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matching and derivative regularization depend on higher input derivatives. For smooth sc…

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