New theory reframes optimizer stability in deep learning

Researchers have identified a phenomenon in deep learning where gradient-based optimizers maintain stable Hessian eigenvalues above theoretically predicted instability thresholds. This deviation, observed to be as high as 21.1 times the predicted bound, is systematic and dependent on the specific optimizer used. The study proposes a new formulation for the stability threshold, based on the directional Hessian and gradient-alignment score, which accounts for the optimizer's actual update and offers new diagnostic tools for understanding its role in balancing temporal and spatial budgets during optimization. AI

IMPACT Refines understanding of optimization dynamics, potentially leading to more stable and efficient deep learning model training.

RANK_REASON Academic paper detailing a new theoretical formulation for optimizer stability in deep learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• directional Hessian
• gradient-alignment score
• gradient descent
• Hessian
• Hugging Face
• learning rate
• Optimizer

• paper
• other

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