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New method optimizes Adam optimizer memory parameter \beta

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for optimizing the memory parameter \beta in the Adam optimizer. This technique involves a short pilot training phase to select an optimal \beta value, which is then fixed for the full training process. By balancing sampling variability against the delay from averaging past gradients, the method proposes a cubic memory rule with coefficients estimated from gradient probes. Retrospective evaluations on vision and language tasks demonstrated a significant reduction in validation gaps compared to standard grid search and best-fit constant \beta values. AI

IMPACT This research could lead to more efficient and effective training of AI models by optimizing the Adam optimizer's memory parameter.

RANK_REASON The cluster contains a research paper detailing a new method for optimizing an AI model's training process. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New method optimizes Adam optimizer memory parameter \beta

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The cluster contains a research paper detailing a new method for optimizing an AI model's training process. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alberto Fern\'andez-Hern\'andez, Cristian P\'erez-Corral, Jose I. Mestre, Manuel F. Dolz, Enrique S. Quintana-Ort\'i ·

    Early Memory Selection for Balanced Adam

    arXiv:2610.08624v1 Announce Type: cross Abstract: We propose a method for choosing the shared memory parameter $\beta_1=\beta_2=\beta$ in Adam from a short pilot training. The selected $\beta$ remains fixed during the subsequent full training. A local model of Adam's normalized d…

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