Corpus ID: 218581522

Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition

@article{Yin2020CompressingRN,
  title={Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition},
  author={Miao Yin and Siyu Liao and Xiao-Yang Liu and X. Wang and Bo Yuan},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.04366}
}
  • Miao Yin, Siyu Liao, +2 authors Bo Yuan
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although the state-of-the-art tensor decomposition approaches can provide good model compression performance, these existing methods are still suffering some inherent limitations, such as restricted representation capability and insufficient model complexity… CONTINUE READING
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