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              <p>Le paradigme de l’apprentissage supervisé est limité par le coût - et parfois l’impraticabilité - de la collecte de données et de l’étiquetage dans de multiples domaines. L'apprentissage auto-supervisé, un paradigme qui exploite la structure de données non étiquetées pour créer des problèmes d'apprentissage qui peuvent être résolus avec des approches supervisées standard, s'est révélé très prometteur en tant qu'approche de pré-entraînement ou d'apprentissage de traits caractéristiques dans des domaines tels que la vision par ordinateur et le traitement de séries temporelles. Dans ce travail, nous présentons des stratégies d'auto-supervision pouvant être utilisées pour apprendre des représentations informatives à partir de séries temporelles multivariées. Une approche fructueuse consiste à prédire si des fenêtres temporelles sont échantillonnées dans le même contexte temporel ou non. Comme le démontre une tâche cliniquement pertinente (classification des stades du sommeil) et avec deux jeux de données d'électroencéphalographie, notre approche surpasse une approche purement supervisée dans des régimes de données faibles, tout en capturant des informations physiologiques importantes sans accès aux étiquettes.</p>
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