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Communication dans un congrès

Dora the explorer: Exploring Very Large Data with Interactive Deep Reinforcement Learning Authors' Copy

Abstract : We demonstrate dora the explorer, a system that guides users in finding items of interest in a very large data set. dora the explorer provides users with the full spectrum of exploration modes and is driven by Data Familiarity or Curiosity, as well as User Interventions. dora the explorer is able to handle data and search scenario complexity, i.e., the difficulty to find scattered/clustered individual records in the data set, and user ability to express what s/he needs. dora the explorer relies on Deep Reinforcement Learning that combines intrinsic (curiosity) and extrinsic (familiarity) rewards. dora's main goal is to support scientific discovery from data. We describe the system architecture and illustrate it with three demonstration scenarios on a 2.6 million galaxies SDSS, a large sky survey data set 1 .
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https://hal.archives-ouvertes.fr/hal-03379727
Contributeur : Sihem Amer-Yahia Connectez-vous pour contacter le contributeur
Soumis le : mardi 19 octobre 2021 - 15:29:46
Dernière modification le : vendredi 5 novembre 2021 - 03:48:49

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Aurélien Personnaz, Sihem Amer-Yahia, Laure Berti-Equille, Maximilian Fabricius, Srividya Subramanian. Dora the explorer: Exploring Very Large Data with Interactive Deep Reinforcement Learning Authors' Copy. 30th ACM International Conference on Information and Knowledge Management, Nov 2021, Queensland (on line), Australia. ⟨hal-03379727⟩

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