Analysis of Transformer Model Applications

dc.contributor.authorCabrera-Bermejo, M. I.
dc.contributor.authorDel Jesus, M. J.
dc.contributor.authorRivera, A. J.
dc.contributor.authorElizondo, David
dc.contributor.authorCharte, F.
dc.contributor.authorPerez-Godoy, Maria Dolores
dc.date.accessioned2023-12-15T14:16:14Z
dc.date.available2023-12-15T14:16:14Z
dc.date.issued2023-08-29
dc.description.abstractSince the emergence of the Transformer, many variations of the original architecture have been created. Revisions and taxonomies have appeared that group these models from different points of view. However, no review studies the tasks faced according to the type of data used. In this paper, the modifications applied to Transformers to work with different input data (text, image, video, etc.) and to solve disparate problems are analysed. Building on the foundations of existing taxonomies, this work proposes a new one that relates input data types to applications. The study shows open challenges and can serve as a guideline for the development of Transformer networks for specific applications with different types of data by observing development trends.
dc.funderNo external funder
dc.identifier.citationCabrera-Bermejo, M.I., Del Jesus, M.J., Rivera, A.J., Elizondo, D., Charte, F., Pérez-Godoy, M.D. (2023) Analysis of Transformer Model Applications. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science, 14001. Springer, https://doi.org/10.1007/978-3-031-40725-3_20
dc.identifier.doihttps://doi.org/10.1007/978-3-031-40725-3_20
dc.identifier.isbn9783031407246
dc.identifier.urihttps://hdl.handle.net/2086/23400
dc.language.isoen
dc.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofseriesInternational Conference on Hybrid Artificial Intelligence Systems
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.titleAnalysis of Transformer Model Applications
dc.typeConference

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