New ML evaluation metric prioritizes computational effort over accuracy

A new research paper proposes a paradigm shift in evaluating machine learning models, moving beyond maximum accuracy to consider computational effort. The proposed metric, based on the number of gradient descent steps required to reach a target accuracy, is framed as a novel form of Automated Machine Learning (AutoML). Experiments across 11 models and five datasets suggest that large learning rates optimize this effort metric, promoting generalization and reducing training time. The study also identifies distinct strategies for achieving lower accuracy targets versus performance limits, recommending single runs for the former and multiple short restarts for the latter. AI

IMPACT This research could shift how ML models are benchmarked, prioritizing efficiency and computational cost alongside accuracy.

RANK_REASON Research paper introducing a new evaluation metric for machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

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• genetic programming
• gradient descent
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• machine learning
• superconvergence

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