publication . Preprint . 2020

DP-Net: Dynamic Programming Guided Deep Neural Network Compression

Yang, Dingcheng; Yu, Wenjian; Zhou, Ao; Mu, Haoyuan; Yao, Gary; Wang, Xiaoyi;
Open Access English
  • Published: 21 Mar 2020
Abstract
Comment: 7pages, 4 figures
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Related Organizations
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24 references, page 1 of 2

[1] Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Proc. NIPS, 1990, pp. 598-605.

[2] S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in Proc. NIPS, 2015, pp. 1135- 1143.

[3] G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.

[4] I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks,” in Proc. NIPS, 2016, pp. 4107-4115. [OpenAIRE]

[5] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.

[6] W. Chen, J. Wilson, S. Tyree, K. Weinberger, and Y. Chen, “Compressing neural networks with the hashing trick,” in Proc. ICML, 2015, pp. 2285-2294.

[7] Y. Gong, L. Liu, M. Yang, and L. Bourdev, “Compressing deep convolutional networks using vector quantization,” arXiv preprint arXiv:1412.6115, 2014.

[8] S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,” in Proc. ICLR, 2016.

[9] W. Chen, J. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen, “Compressing convolutional neural networks in the frequency domain,” in Proc. ACM SIGKDD, 2016, pp. 1475-1484.

[10] S. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Information Theory, vol. 28, no. 2, pp. 129-137, 1982.

[11] M. Mahajan, P. Nimbhorkar, and K. Varadarajan, “The planar k-means problem is NP-hard,” in International Workshop on Algorithms and Computation. Springer, 2009, pp. 274-285.

[12] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proc. ICLR, 2014.

[13] H. Zhang, Y. Yu, J. Jiao, E. Xing, L. El Ghaoui, and M. Jordan, “Theoretically principled trade-off between robustness and accuracy,” in Proc. ICML, 2019, pp. 7472-7482.

[14] J. Wu, Y. Wang, Z. Wu, Z. Wang, A. Veeraraghavan, and Y. Lin, “Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions,” in Proc. ICML, 2018, pp. 5363-5372.

[15] K. Ullrich, E. Meeds, and M. Welling, “Soft weight-sharing for neural network compression,” in Proc. ICLR, 2017. [OpenAIRE]

24 references, page 1 of 2
Abstract
Comment: 7pages, 4 figures
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
Related Organizations
Download from
24 references, page 1 of 2

[1] Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Proc. NIPS, 1990, pp. 598-605.

[2] S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in Proc. NIPS, 2015, pp. 1135- 1143.

[3] G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.

[4] I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks,” in Proc. NIPS, 2016, pp. 4107-4115. [OpenAIRE]

[5] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.

[6] W. Chen, J. Wilson, S. Tyree, K. Weinberger, and Y. Chen, “Compressing neural networks with the hashing trick,” in Proc. ICML, 2015, pp. 2285-2294.

[7] Y. Gong, L. Liu, M. Yang, and L. Bourdev, “Compressing deep convolutional networks using vector quantization,” arXiv preprint arXiv:1412.6115, 2014.

[8] S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,” in Proc. ICLR, 2016.

[9] W. Chen, J. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen, “Compressing convolutional neural networks in the frequency domain,” in Proc. ACM SIGKDD, 2016, pp. 1475-1484.

[10] S. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Information Theory, vol. 28, no. 2, pp. 129-137, 1982.

[11] M. Mahajan, P. Nimbhorkar, and K. Varadarajan, “The planar k-means problem is NP-hard,” in International Workshop on Algorithms and Computation. Springer, 2009, pp. 274-285.

[12] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proc. ICLR, 2014.

[13] H. Zhang, Y. Yu, J. Jiao, E. Xing, L. El Ghaoui, and M. Jordan, “Theoretically principled trade-off between robustness and accuracy,” in Proc. ICML, 2019, pp. 7472-7482.

[14] J. Wu, Y. Wang, Z. Wu, Z. Wang, A. Veeraraghavan, and Y. Lin, “Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions,” in Proc. ICML, 2018, pp. 5363-5372.

[15] K. Ullrich, E. Meeds, and M. Welling, “Soft weight-sharing for neural network compression,” in Proc. ICLR, 2017. [OpenAIRE]

24 references, page 1 of 2
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