Gradient Span Algorithms Show Predictable Progress in High-Dimensional ML

Researchers have demonstrated that 'gradient span algorithms' exhibit predictable behavior on scaled Gaussian random functions in high dimensions. This finding offers a theoretical explanation for the consistent cost curves observed across multiple training runs of large machine learning models, even with random initialization on complex landscapes. The predictable progress phenomenon is already leveraged by the automated machine learning (AutoML) community, reducing the need for repeated training with identical hyperparameters. AI

IMPACT Provides a theoretical basis for the efficiency of AutoML, potentially streamlining model development.

RANK_REASON The cluster contains an academic paper detailing a new theoretical finding in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• automated machine learning
• Felix Benninger
• Gaussian random functions
• machine learning

• paper
• model release

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