Comparative Study of Pruning Techniques in Recurrent Neural Networks

Author:

Choudhury Sagar,Rout Asis Kumar,Thaker Pragnesh,Mohan Biju R.

Publisher

Springer Nature Singapore

Reference17 articles.

1. Alford S et al (2018) Pruned and structurally sparse neural networks. CoRR abs/1810.00299. arXiv: 1810.00299

2. Zhu M, Gupta S (2017) To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878

3. Wen W, He Y, Rajbhandari S, Zhang M, Wang W, Liu F, Hu B, Chen Y, Li H (2017) Learning intrinsic sparse structures within long short-term memory. arXiv preprint arXiv:1709.05027

4. Furuya T, Suetake K, Taniguchi K, Kusumoto H, Saiin R, Daimon T (2021) Spectral pruning for recurrent neural networks. arXiv:2105.10832v1

5. Mao H et al (2017) Exploring the regularity of sparse structure in convolutional neural networks. arXiv preprint arXiv:1705.08922

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