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New method probes neural network loss sharpness for stable learning rates

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to estimate the local sharpness of a loss function in neural networks, a critical factor for stable gradient steps. By analyzing the step size accepted during Armijo backtracking line searches, they can derive a low-cost probe for the top Hessian eigenvalue. This probe, when used once during initialization, provides a learning rate safeguard that makes optimizers like Adam and AdamW robust to excessively large initial learning rates across a wide range of values and architectures. AI

IMPACT This research offers a low-cost method to improve the stability of training neural networks by safeguarding learning rates.

RANK_REASON The item is an academic paper detailing a new method for analyzing neural network loss functions. [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 method probes neural network loss sharpness for stable learning rates

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The item is an academic paper detailing a new method for analyzing neural network loss functions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashmitha R, J\"org Frochte ·

    Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam

    arXiv:2607.03998v1 Announce Type: new Abstract: The local sharpness of the loss, the top Hessian eigenvalue $\lambda_1$, determines the largest stable gradient step, but measuring it normally requires Lanczos or Hessian-vector iterations. We observe that a single Armijo backtrack…

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