时空序列预测之Memory In Memory: A Predictive Neural Network for Learning Higher-OrderNon-Stationarity
1. 写在前面 今天整理的这篇文章是2019年CVPR上的一篇paper， 依然来自于清华的团队，也是时空序列预测的一篇文章， 这篇文章依然是从一个新的视角–非平稳性的角度去看待到时空序列的预测学习， 前面提到的时空序列预测模型ConvLSTM、PredRNN、PredRNN++等虽然对时空序列有着比较大的建模能力， 但是捕捉时间序列的非平稳性的特征效果不是很好， 没有充分发掘出时间序列的非平稳特
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相关文章
- 1. 【时空序列预测第五篇】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. 【时空序列预测第十三篇】时空序列预测模型之Self-Attention ConvLSTM
- 6. Collaborative Memory Network for Recommendation Systems
- 7. Memory Network and Neural Turing Machine
- 8. 论文笔记之：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