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Updated: Feb 6, 2026

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具有适应路由的多时间尺度表示,用于在时间转移下进行深度表式学习

Tianyu Wang1, Maite Zhang2, Mingxuan Lu3

  • 1Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China; Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.

Neural networks : the official journal of the International Neural Network Society
|February 4, 2026
PubMed
概括

使用路由尺度的时间抽象 (TARS) 通过解决时间变化来增强深度表式学习. 这种方法通过动态优先考虑相关的时间尺度,强有力的调整模型以适应不断变化的数据,改善现实世界数据集的性能.

关键词:
漂移感知路由的路由方式功能-时间融合.多次时间尺度表示.图表式学习是一种表式的学习.时间转移是时间转移.

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