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UID:73@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20251113T140000
DTEND;TZID=Europe/Paris:20251113T150000
DTSTAMP:20260602T130205Z
URL:https://isdm.umontpellier.fr/events/deep-learning-for-species-recognit
 ion-under-high-uncertainty-application-to-jellyfish-images-2/
SUMMARY:Deep Learning for Species Recognition under High Uncertainty: Appli
 cation to jellyfish images
DESCRIPTION:Building 5\, 02.124\, Campus St Priest\nMachine Learning in Mon
 tpellier\, Theory &amp\; Practice - Matthieu de Castelbajac (Univ. Montpel
 lier)\n ‎ \n\nCitizen science records are a valuable source of biodivers
 ity data\, and even more essential to help track mobile marine species lik
 e jellyfish. However\, these records can be highly uncertain\, containing 
 many potential errors and biases.  They are typically validated by experts
 \, which is impractical at scale. Although deep learning methods for autom
 atic validation have shown promising results\, they fail to account for th
 e uncertainty present in both the input data and their predictions. Here\,
  we present a semi-automated method to support record validation at scale 
 while providing strong statistical guarantees\, including for highly uncer
 tain citizen science records.\n ‎ \n\nIA et Experts
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2025/07/ml-mtp.png
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