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English(EN) YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations

YingLan：新型多模态海洋基础模型已开发

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了YingLan，这是一种新颖的多模态基础模型，旨在统一来自不同海洋学数据源的表征学习。该模型集成了物理属性条件动态编码和时空增量耦合（STIC），以处理异构数据结构和时空依赖性。YingLan使用MindSpore实现，并在华为Ascend 910B NPU上训练，在海表盐度预测任务中显示出有希望的结果。 AI

影响 该模型有望推动复杂环境数据的统一表征学习，从而改善海洋学的预测和分析。

排序理由 该集群包含一篇详细介绍海洋学数据新型多模态基础模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

YingLan：新型多模态海洋基础模型已开发

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该集群包含一篇详细介绍海洋学数据新型多模态基础模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sen Jia, Siqi Wei, Junyu Wang, Zijun Lv, Pan Liu, Meng Xu, Xiyou Fu, Yuxi Sun, Qiang Liu, Shuyu Zhang, Lin Mu ·

    YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations

    arXiv:2610.29313v1 Announce Type: new Abstract: Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristics across satellite imagery, gridded oceanographic fields, and buoy observations.…

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