Fetching the paper…
Reading the bibliography…
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs).
Optimal brain damage
Y. LeCun, J. S. Denker, and S. A. Solla · 1990
Earlier work this paper cites.
The penn treebank: Annotating predicate argument structure
M. Marcus, G. Kim, M. A. Marcinkiewicz, R. MacIntyre, A. Bies, M. Ferguson, K. Katz, and B. Schasberger · 1994
Earlier work this paper cites.
Learning the parts of objects by non-negative matrix factorization
D. D. Lee and H. S. Seung · 1999
Earlier work this paper cites.
Binary matrix factorization with applications
Z. Zhang, T. Li, C. H. Q. Ding, and X. Zhang · 2007
Earlier work this paper cites.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. aurelio Ranzato, A. Senior, P. Tucker, K. Yang, Q. V. Le, and A. Y. Ng · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Nimfa: A python library for nonnegative matrix factorization
M. Zitnik and B. Zupan · 2012
Earlier work this paper cites.
Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
Earlier work this paper cites.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Dynamic network surgery for efficient DNNs
Y. Guo, A. Yao, and Y. Chen · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Cited alongside, same era.
Cambricon-x: An accelerator for sparse neural networks
S. Zhang, Z. Du, L. Zhang, H. Lan, S. Liu, L. Li, Q. Guo, T. Chen, and Y. Chen · 2016
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
Cited alongside, same era.
Scalpel: Customizing DNN pruning to the underlying hardware parallelism
J. Yu, A. Lukefahr, D. Palframan, G. Dasika, R. Das, and S. Mahlke · 2017
Later among the works it cites.
To prune, or not to prune: exploring the efficacy of pruning for model compression
M. Zhu and S. Gupta · 2017
Later among the works it cites.
GroupReduce: Block-wise low-rank approximation for neural language model shrinking
P. Chen, S. Si, Y. Li, C. Chelba, and C.-J. Hsieh · 2018
Later among the works it cites.
Alternating multi-bit quantization for recurrent neural networks
C. Xu, J. Yao, Z. Lin, W. Ou, Y. Cao, Z. Wang, and H. Zha · 2018
Later among the works it cites.
Double Viterbi: Weight encoding for high compression ratio and fast on-chip reconstruction for deep neural network
D. Ahn, D. Lee, T. Kim, and J.-J. Kim · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Molchanov, A. Ashukha, and D. P. Vetrov · 2017
Cited alongside, same era.
Block-sparse recurrent neural networks
S. Narang, E. Undersander, and G. F. Diamos · 2017
Cited alongside, same era.
EIE: efficient inference engine on compressed deep neural network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally
Cited in the paper.
Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
S. Han, H. Mao, and W. J. Dally
Cited in the paper.
Viterbi-based pruning for sparse matrix with fixed and high index compression ratio
D. Lee, D. Ahn, T. Kim, P. I. Chuang, and J.-J. Kim
Cited in the paper.
Deeptwist: Learning model compression via occasional weight distortion
D. Lee, P. Kapoor, and B. Kim
Cited in the paper.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2019
Closest in time.
Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2019
Closest in time.