《All convolutional neural networks for radar-based precipitation nowcasting》論文解讀
這篇文章於2019年1月在Procedia Computer Science上發表。它的貢獻在於選擇和採用了合適的數據預處理方法和損失函數，提出了網絡模型——Dozhdya.Net。 數據 在介紹模型之前首先說一下該實驗所採用的數據。數據名爲RY，其測量範圍爲 900 ∗ 900 k m 900*900km 900∗900km，它的空間和時間的分辨率分別爲 1 ∗ 1 k m 1*1km 1∗1k
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相關文章
- 1. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting論文閱讀
- 2. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- 3. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 4. 《Convolutional Neural Networks for Sentence Classification》論文研讀
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- 6. Convolutional LSTM Network: A Machine Learning Approach For Precipitation Nowcasting
- 7. 論文：3D Convolutional Neural Networks for Human Action Recognition
- 8. [論文解讀]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 9. 《SmaAt-UNet Precipitation Nowcasting using a Small Attention-UNet Architecture》論文解讀
- 10. 論文《Convolutional Neural Networks for Sentence Classification》總結