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.
arXivGeneralization to Unseen IndividualsLeakage-Free Machine LearningLeave-One-Subject-Out EvaluationNeural Architecture SearchSubject-Dependent Classification
COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS
arXiv cs.AIEfficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation ↗SEP 10 04:00 UTC
arXiv cs.LGEfficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation ↗SEP 10 04:00 UTC