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UID:191@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20251113T140000
DTEND;TZID=Europe/Paris:20251113T140000
DTSTAMP:20260825T130946Z
URL:https://isdm.umontpellier.fr/events/deep-learning-for-species-recognit
 ion-under-high-uncertainty-application-to-jellyfish-images/
SUMMARY:Deep Learning for Species Recognition under High Uncertainty: Appli
 cation to jellyfish images
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Matthieu de Castelbajac (Univ. 
 Montpellier)\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 automa
 tic validation have shown promising results\, they fail to account for the
  uncertainty present in both the input data and their predictions. Here\, 
 we present a semi-automated method to support record validation at scale w
 hile providing strong statistical guarantees\, including for highly uncert
 ain citizen science records.\n\nMachine Learning in Montpellier\, Theory &
 amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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 ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.124 Building 5:geo
 :0,0
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DTSTART:20251026T020000
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