Long-term Forecasting using Higher Order Tensor RNNs

By Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue. 2017

95 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. By Lei Deng
2. Guoqi Li
3. Yuan Xie
4. Luping P. Shi
5. Shanghang Zhang
6. Song Han
7. Hui Xiong
8. Jianxin Li

Citations in the corpus

• Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey (2020, methodology): “Compared to CNNs, the works on RNNs [122]–[124], [138] are fewer perhaps due to the complicated gate structure of LSTM/GRU and the less popularity and standardization of RNNs in current AI tasks when compared to CNNs.”
• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (2020, methodology): “However, existing methods are designed under limited problem setting, like predicting 48 points or less (Hochreiter and Schmidhuber 1997; Li et al. 2018; Yu et al. 2017; Liu et al. 2019; Qin et al. 2017; Wen et al. 2017).”