【論文閱讀】Geographically and temporally weighted neural networks for ground-level PM2.5
Abstract： 氣溶膠光學厚度AOD數據(aerosol optical depth)和地面觀測站數據構成了對PM2.5的可靠量測，一般的 AOD-PM2.5關係都是使用線性模型進行模擬的。使用深度學習的方法可以模擬非線性關係，但是沒有考慮空間因素。本實驗使用中國地區的satellite AOD products、NDVI data 、meteorological factors 和stati
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相關文章
- 1. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 2. 論文閱讀：Deep Neural Networks for Object Detection
- 3. 論文閱讀-Fast Algorithms for Convolutional Neural Networks
- 4. 論文閱讀《Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks》
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- 7. 論文閱讀：Pointwise Convolutional Neural Networks
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- 9. 論文閱讀：Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 10. 論文《Matching Networks for One Shot Learning》閱讀