時空序列預測之Memory In Memory: A Predictive Neural Network for Learning Higher-OrderNon-Stationarity
1. 寫在前面 今天整理的這篇文章是2019年CVPR上的一篇paper， 依然來自於清華的團隊，也是時空序列預測的一篇文章， 這篇文章依然是從一個新的視角–非平穩性的角度去看待到時空序列的預測學習， 前面提到的時空序列預測模型ConvLSTM、PredRNN、PredRNN++等雖然對時空序列有着比較大的建模能力， 但是捕捉時間序列的非平穩性的特徵效果不是很好， 沒有充分發掘出時間序列的非平穩特
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相關文章
- 1. 【時空序列預測第五篇】Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity
- 2. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
- 3. Neural Network with Memory
- 4. Attention and Memory in Deep Learning and NLP
- 5. 【時空序列預測第十三篇】時空序列預測模型之Self-Attention ConvLSTM
- 6. Collaborative Memory Network for Recommendation Systems
- 7. Memory Network and Neural Turing Machine
- 8. 論文筆記之：Hybrid computing using a neural network with dynamic external memory
- 9. Local and Remote Memory: Memory in a Linux NUMA System
- 10. Spark - A Fault-Tolerant Abstraction for In-Memory Cluster Computing