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New criterion quantifies depth sufficiency in neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a first-order criterion to determine if a residual neural network has reached sufficient depth. This criterion, based on the concept of residual non-degeneracy, proves that additional depth is valuable only when conditional activation gradients project onto an admissible residual tangent space. The study demonstrates that activation-gradient magnitude serves as a reliable diagnostic for the remaining empirical value of residual depth across various model architectures, including ResNets, GPT-2, and Pythia checkpoints. Furthermore, function-preserving growth achieved performance competitive with training from scratch. AI

IMPACT Provides a theoretical framework for optimizing neural network architectures and training efficiency.

RANK_REASON Academic paper detailing a new theoretical criterion for neural network depth. [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 →

New criterion quantifies depth sufficiency in neural networks

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Academic paper detailing a new theoretical criterion for neural network depth. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zeyu Liu, Jinhao Zhang, Yunquan Zhang, Guangming Tan, Xiang Gao, Fangming Liu, Daning Cheng ·

    Quantifying Depth Sufficiency in Residual Neural Networks: A First-Order Criterion

    arXiv:2608.14664v1 Announce Type: new Abstract: How can we determine whether a trained neural network is already deep enough? We study this under a fixed function-preserving residual-growth protocol specifying insertion locations, residual families, zero-output initializations, a…

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