Research paper details optimal Schatten-p norm usage in deep learning

A new research paper explores the optimal use of Schatten-p norms in deep learning, particularly in relation to optimizers like Muon. The study demonstrates that the effectiveness of these norms is dependent on the specific regime, with smaller Schatten-p geometries proving optimal in low-dimensional settings, including those relevant to Chinchilla scaling. This analysis also provides insights into why Muon-like methods favor large batches and offers a scaling rule for batch sizes across different values of p. AI

IMPACT Provides theoretical guidance on optimizing deep learning models, potentially improving training efficiency and performance.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical findings in deep learning optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• Chinchilla
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• Influence Flower
• Muon
• Schatten-p norm
• ScienceCast

• paper
• model release

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