Fetching the paper…
Reading the bibliography…
Structural pruning of neural network parameters reduces computation, energy, and memory transfer costs during inference.
A back-propagation algorithm with optimal use of hidden units
Y. Chauvin · 1989
Earlier work this paper cites.
Comparing biases for minimal network construction with back-propagation
S. J. Hanson and L. Y. Pratt · 1989
Earlier work this paper cites.
Skeletonization: A technique for trimming the fat from a network via relevance assessment
M. C. Mozer and P. Smolensky · 1989
Earlier work this paper cites.
Optimal brain damage
Y. LeCun, J. S. Denker, S. Solla, R. E. Howard, and L. D. Jackel · 1990
Earlier work this paper cites.
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
I. Daubechies, M. Defrise, and C. De Mol · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Elements of information theory
T. M. Cover and J. A. Thomas · 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.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Dsd: regularizing deep neural networks with dense-sparse-dense training flow
S. Han, J. Pool, S. Narang, H. Mao, S. Tang, E. Elsen, B. Catanzaro, J. Tran, and W. J. Dally · 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.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Fast convnets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
Pruning convolutional neural networks for resource efficient transfer learning
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
Later among the works it cites.
Structured bayesian pruning via log-normal multiplicative noise
K. Neklyudov, D. Molchanov, A. Ashukha, and D. P. Vetrov · 2017
Later among the works it cites.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Later among the works it cites.
NISP: Pruning networks using neuron importance score propagation
R. Yu, A. Li, C.-F. Chen, J.-H. Lai, V. I. Morariu, X. Han, M. Gao, C.-Y. Lin, and L. S. Davis · 2017
Later among the works it cites.
The lottery ticket hypothesis: Training pruned neural networks
J. Frankle and M. Carbin · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Data-driven sparse structure selection for deep neural networks
Z. Huang and N. Wang · 2017
Cited alongside, same era.
Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Cited alongside, same era.
Learning sparse neural networks through
C. Louizos, M. Welling, and D. P. Kingma · 2017
Cited alongside, same era.
Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
Cited alongside, same era.
A. Gordon, E. Eban, O. Nachum, B. Chen, H. Wu, T.-J. Yang, and E. Choi · 2018
Later among the works it cites.
Progressive deep neural networks acceleration via soft filter pruning
Y. He, X. Dong, G. Kang, Y. Fu, and Y. Yang · 2018
Later among the works it cites.
Adc: Automated deep compression and acceleration with reinforcement learning
Y. He and S. Han · 2018
Later among the works it cites.
Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2018
Later among the works it cites.
Faster gaze prediction with dense networks and fisher pruning
L. Theis, I. Korshunova, A. Tejani, and F. Huszár · 2018
Later among the works it cites.
Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
Later among the works it cites.