Revisiting NARX Recurrent Neural Networks for Long-Term Dependencies
By Robert S. DiPietro, Nassir Navab, Gregory Hager. 2017
14 citations recorded by the source corpus.
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- Dhruv Madeka
- Ruofeng Wen
- Kari Torkkola
- Angelika Steger
- Balakrishnan Narayanaswamy
- Asier Mujika
- Florian Meier
- Bryan Lim
Citations in the corpus
- A Multi-Horizon Quantile Recurrent Forecaster (2017, background): “Another not as well-known type of recurrent net to solve the same long dependency issue is NARX RNN (DiPietro et al, 2017), which computes hidden state ht not only based on ht−1, but also a specific set of other past states, e.g. (ht−2, · · · , ht−D).”
- Fast-Slow Recurrent Neural Networks (2017, methodology): “For instance using RNN cells with good long-term memory, like EURNNs [21] or NARX RNNs [26, 8], for the Slow cell might boost the long-term memory of the FS-RNN”