Deep learning for irregularly and regularly missing data reconstruction

Deep learning (DL) is a powerful tool for mining features from data, which can theoretically avoid assumptions (e.g., linear events) constraining conventional interpolation methods. Motivated by this and inspired by image-to-image translation, we applied DL to irregularly and regularly missing data...

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Detalles Bibliográficos
Autores principales: Chai, Xintao, Gu, Hanming, Li, Feng, Duan, Hongyou, Hu, Xiaobo, Lin, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040000/
https://www.ncbi.nlm.nih.gov/pubmed/32094366
http://dx.doi.org/10.1038/s41598-020-59801-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040000/
https://www.ncbi.nlm.nih.gov/pubmed/32094366
http://dx.doi.org/10.1038/s41598-020-59801-x

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