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Updated: Jul 24, 2025

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探索变压器和图形卷积网络用于人类移动性建模.

Riccardo Corrias1, Martin Gjoreski1, Marc Langheinrich1

  • 1Computer Systems Institute, Faculty of Informatics, Università della Svizzera italiana (USI), 6900 Lugano, Switzerland.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

使用通用变压器 (GPT) 和图形卷积网络 (GCN) 的新人工智能模型显示出预测人类下一个位置的流动性的前景. 虽然在稀疏数据上的专业模型略高于它们的表现,但在密集数据集上的表现表明了先进的移动性估计的未来潜力.

关键词:
深度学习是一种深度学习.图表 卷积网络 卷积网络机器学习是机器学习.移动性的建模模型.变压器 变压器

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