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papersSEP 10 04:00 UTC

Efficient Leakage-Free Neural Architecture Search for Leave-One-Subject-Out Evaluation

A newly posted arXiv paper addresses the heavy compute cost of neural architecture search when performance must be measured with leave-one-subject-out evaluation, a protocol used to gauge how subject-based classifiers generalise to unseen individuals. Because a fully nested search would require training a separate architecture for every held-out subject, the authors propose a leakage-free procedure that avoids that expense. The method is aimed at subject-dependent classification settings where per-subject data splits are standard.