19IJCAI Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Pre
本文提出multi-group Encoder-Decoder networks (MGED-Net) 模型融合多種輸入特徵，解決預測一天後預測給定站點，給定污染物種類（PM2.5）的污染程度的問題。 難點：時空數據都存在，面臨異構數據融合的問題；需要進行較長時間（一天）的預測。針對這些難點，本文提出了一個多組數據的Encoder-Decoder模型，主要貢獻有三點。 將空間網格的天氣數據（包括歷
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相關文章
- 1. metapath2vec: Scalable Representation Learning for Heterogeneous Networks
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