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New method profiles Hessian mass in machine learning models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for evaluating block-diagonal curvature models in machine learning. This approach measures the squared off-block fraction of the parameter Hessian and analyzes the within-group curvature profile. Experiments on CIFAR-100 ResNet-18 and ResNet-50 models demonstrated that while initial discarded fractions can be similar, their profiles may differ. The study also compared different candidates for correction, finding that off-group Hessian action alone can increase minibatch loss, but combined candidates may offer lower loss. AI

IMPACT Introduces a novel method for analyzing model curvature, potentially improving the understanding and optimization of deep learning architectures.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new diagnostic method for machine learning models. [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 profiles Hessian mass in machine learning models

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The cluster contains a research paper published on arXiv detailing a new diagnostic method for machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Maxim Bolshim (ITMO University, Saint Petersburg, Russia), Alexander Kugaevskikh (ITMO University, Saint Petersburg, Russia) ·

    Profile-aware diagnostics of discarded parameter-Hessian mass

    arXiv:2611.01335v2 Announce Type: replace Abstract: Evaluating block-diagonal curvature models involves two questions: how much curvature they omit, and what a specific correction changes at a finite step. We measure the squared off-block fraction of the parameter Hessian against…

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