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Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources.
Skeletonization: A technique for trimming the fat from a network via relevance assessment
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New frameworks for offline and streaming coreset constructions
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
H. Hu, R. Peng, Y. Tai, and C. Tang · 2016
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Fast convnets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
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Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2016
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
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Towards understanding the role of over-parametrization in generalization of neural networks
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Coresets for monotonic functions with applications to deep learning
E. Tolochinsky and D. Feldman · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
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Pruning convolutional neural networks for resource efficient transfer learning
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2016
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J. M. Phillips · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
X. Dong, S. Chen, and S. Pan · 2017
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Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
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Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
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Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
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NISP: pruning networks using neuron importance score propagation
R. Yu, A. Li, C. Chen, J. Lai, V. I. Morariu, X. Han, M. Gao, C. Lin, and L. S. Davis · 2018
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Discrimination-aware channel pruning for deep neural networks
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu · 2018
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Discrimination-aware channel pruning for deep neural networks
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J.-H. Zhu · 2018
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A convergence theory for deep learning via over-parameterization
Z. Allen-Zhu, Y. Li, and Z. Song · 2019
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Neural architecture search: A survey
T. Elsken, J. H. Metzen, and F. Hutter · 2019
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The state of sparsity in deep neural networks
T. Gale, E. Elsen, and S. Hooker · 2019
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Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2019
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Collaborative channel pruning for deep networks
H. Peng, J. Wu, S. Chen, and J. Huang · 2019
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Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
Z. You, K. Yan, J. Ye, M. Ma, and P. Wang · 2019
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What does a pruned deep neural network forgets?
S. Hooker, A. Courville, Y. Dauphin, and A. Frome · 2020
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Provable filter pruning for efficient neural networks
L. Liebenwein, C. Baykal, H. Lang, D. Feldman, and D. Rus · 2020
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Comparing fine-tuning and rewinding in neural network pruning
A. Renda, J. Frankle, and M. Carbin · 2020
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Comparing fine-tuning and rewinding in neural network pruning
A. Renda, J. Frankle, and M. Carbin · 2020
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