AtmoDist: Self-supervised representation learning for atmospheric dynamics
Representation learning has proven to be a powerful methodology in a wide variety of machine-learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show h...
שמור ב:
| Principais autores: | , |
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| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
Cambridge University Press
2023-01-01
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| סדרה: | Environmental Data Science |
| נושאים: | |
| גישה מקוונת: | https://www.cambridge.org/core/product/identifier/S2634460223000018/type/journal_article |
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