Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils
Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric groups, and the uncertainty introduced by image degradation. Traditional morphometric methods suffer from high structural overlap across sexes, poor cross-population generalizability, and subjective feature engineering. This study presents an uncertainty-aware deep learning framework for sex attribution in prehistoric hand stencils that explicitly models, propagates, and aggreg
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- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Decoding the Past: An Uncertainty-Aware Deep Learning Framew” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Karel Becerra →
“Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils”
- LinkedLinked via arxiv author · 85%Boris Mederos →
“Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils”
- LinkedLinked via arxiv author · 85%Dean Snow →
“Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils”
- LinkedLinked via arxiv author · 85%Ramón A. Mollineda →
“Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils”