A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction閱讀筆記
介紹 這篇文章來自2017IJCAI。DA-LSTM是一個雙階段的基於注意力的encoder-decoder結構，之所以使用雙階段，是因爲作者受心理學選擇性注意的啓發。 Encoder-Decoder與注意力機制 Encoder-Decoder框架有一個最顯著的特徵就是它是一個End-to-End學習的算法，這樣的模型也被叫做 Sequence to Sequence learning。所謂編碼，
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相關文章
- 1. [IJCAI 2017]A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
- 2. （轉）LSTM NEURAL NETWORK FOR TIME SERIES PREDICTION
- 3. 論文筆記-雙向Attention：A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
- 4. 《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》閱讀筆記
- 5. 【閱讀筆記】《ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation》
- 6. Recurrent neural networks for multivariate time series with missing values文獻閱讀
- 7. 【閱讀筆記】《ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation》
- 8. 《Deep learning for time series classification a review》筆記
- 9. 《Using recurrent neural network models for early detection of heart failure onset》閱讀筆記
- 10. 【閱讀筆記】《ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks》