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GraphCast: Learning skillful medium-range global weather forecasting

, , , , , , , , , , , , , , , , , and . (2022)cite arxiv:2212.12794Comment: GraphCast code and trained weights are available at: https://github.com/deepmind/graphcast.

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Dynamic monitoring of lively footbridges., , , and . exp.at, page 135-136. IEEE, (2015)Learning Discrete Representations via Information Maximizing Self-Augmented Training., , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 1558-1567. PMLR, (2017)Pre-training Graph Neural Networks., , , , , , and . CoRR, (2019)Strategies for Pre-training Graph Neural Networks, , , , , , and . (2019)cite arxiv:1905.12265Comment: Accepted as a spotlight to ICLR 2020.From Similarity to Superiority: Channel Clustering for Time Series Forecasting., , , , , , , and . CoRR, (2024)ContextGNN: Beyond Two-Tower Recommendation Systems., , , , , , , , , and 3 other author(s). CoRR, (2024)Worst-case Redundancy of Optimal Binary AIFV Codes and their Extended Codes., , and . CoRR, (2016)A Framework for Big Data Security Analysis and the Semantic Technology., , , , , and . ICITCS, page 1-4. IEEE Computer Society, (2016)GraphCast: Learning skillful medium-range global weather forecasting, , , , , , , , , and 8 other author(s). (2022)cite arxiv:2212.12794Comment: GraphCast code and trained weights are available at: https://github.com/deepmind/graphcast.Wilds: A benchmark of in-the-wild distribution shifts, , , , , , , , , and 1 other author(s). International Conference on Machine Learning, page 5637--5664. PMLR, (2021)