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New GLOBE framework enhances coreset selection for on-device AI training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GLOBE (Gradient Local-Balanced Extraction), a novel framework for coreset selection in on-device deep neural network training. GLOBE addresses limitations of existing methods by capturing evolving optimization dynamics and handling correlated samples through trajectory-aligned gradient matching. The framework formulates sample selection as a globally optimized sparse weighting problem, utilizing multiple training checkpoints to construct gradient trajectories and employing regularization techniques to induce sparsity and stabilize weights. Experiments show GLOBE outperforms existing methods in downstream test accuracy, especially at low data retention ratios. AI

IMPACT Enhances data efficiency in on-device AI training by improving coreset selection methods.

RANK_REASON The cluster contains a research paper detailing a new framework for coreset selection in machine learning. [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 GLOBE framework enhances coreset selection for on-device AI training

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The cluster contains a research paper detailing a new framework for coreset selection in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hetian Liu, Jin Cui, Mengcheng Shi, Yanbin Hu, Xinyue Long, Boran Zhao, Pengju Pen ·

    GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection

    arXiv:2608.02690v1 Announce Type: new Abstract: On-device training of deep neural networks is fundamentally constrained by the computational and memory costs of large-scale datasets. Coreset selection offers a practical solution by retaining only a compact subset of real training…

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