Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
這篇文章來大致介紹一下ConvLSTM的基本原理和應用場景。個人認爲有時候對於一個算法改進很大程度上會受到應用場景的啓示，比如現在要說的這篇。不知道論文作者當時想到這個idea時是不是也是這樣。 1.論文的核心思想 先來想象一下這麼一個應用場景：根據某個城市歷史的降雨量數據，來預測在接下來的某個時間t降雨量的多少。根據作者的思路： a 先將城市劃分成個格子，每個格子代表一個小的區域（例如） ； b
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相關文章
- 1. Convolutional LSTM Network: A Machine Learning Approach For Precipitation Nowcasting
- 2. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting論文閱讀
- 3. 【網絡架構】Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- 4. 遞歸網絡之Convolutional LSTM Network: A Machine LearningApproach for Precipitation Nowcasting
- 5. 《All convolutional neural networks for radar-based precipitation nowcasting》論文解讀
- 6. Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM
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- 8. A Tour of Machine Learning Algorithms
- 9. A Discriminative Feature Learning Approach for Deep Face Recognition
- 10. Deep Visual Foresight for Planning Robot Motion