Deep learning for irregularly and regularly missing data reconstruction

Abstract 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 miss...

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Autores principales: Xintao Chai, Hanming Gu, Feng Li, Hongyou Duan, Xiaobo Hu, Kai Lin
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/d4a830846aec4e548fbeee9a348402f9
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