Hierarchical Recurrent Neural Networks for Long-Term Dependencies

@inproceedings{Hihi1995HierarchicalRN,
  title={Hierarchical Recurrent Neural Networks for Long-Term Dependencies},
  author={Salah El Hihi and Yoshua Bengio},
  booktitle={NIPS},
  year={1995}
}
We have already shown that extracting long-term dependencies from sequential data is difficult, both for determimstic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/output hidden Markov models (IOHMMs). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper, we propose to use a more general type of a… CONTINUE READING

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