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Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance.
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Optimal brain damage
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Second order derivatives for network pruning: Optimal brain surgeon
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Very deep convolutional networks for large-scale image recognition
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Pointer sentinel mixture models
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
X. Dong, S. Chen, and S. J. Pan · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Runtime neural pruning
J. Lin, Y. Rao, J. Lu, and J. Zhou · 2017
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Bayesian compression for deep learning
C. Louizos, K. Ullrich, and M. Welling · 2017
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Variational dropout sparsifies deep neural networks
D. Molchanov, A. Ashukha, and D. Vetrov · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Compressing neural networks using the variational information bottleneck
B. Dai, C. Zhu, B. Guo, and D. Wipf · 2018
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AMC: AutoML for model compression and acceleration on mobile devices
Y. He, J. Lin, H. Wang, L.-J. Li, and S. Han · 2018
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Learning sparse neural networks through l 0 l_{0} regularization
C. Louizos, M. Welling, and D. P. Kingma · 2018
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Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. V. Le · 2019
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2019
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AutoPrune: Automatic network pruning by regularizing auxiliary parameters
X. Xiao, Z. Wang, and S. Rajasekaran · 2019
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Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
C. Yun, S. Sra, and A. Jadbabaie · 2019
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Deconstructing lottery tickets: Zeros, signs, and supermasks
H. Zhou, J. Lan, R. Liu, and J. Yosinki · 2019
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Fast sparse ConvNets
E. Elsen, M. Dukhan, T. Gale, and K. Simonyan · 2020
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Scalable training of artificial neural networks with adaptive sparse connec- tivity inspired by network science
D. C. Mocanu, E. Mocanu, P. Stone, P. H. Nguyen, M. Gibescu, and A. Liotta · 2018
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
M. Zhu and S. Gupta · 2018
Cited alongside, same era.
Data-dependent coresets for compressing neural networks with applications to generalization bounds
C. Baykal, L. Liebenwein, I. Gilitschenski, D. Feldman, and D. Rus · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2019
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The state of sparsity in deep neural networks
T. Gale, E. Elsen, and S. Hooker · 2019
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
N. Lee, T. Ajanthan, and P. H. S. Torr · 2019
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Rigging the lottery: Making all tickets winners
U. Evci, T. Gale, J. Menick, P. S. Castro, and E. Elsen · 2020
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Linear mode connectivity and the lottery ticket hypothesis
J. Frankle, G. K. Dziugaite, D. M. Roy, and M. Carbin · 2020
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Sparse GPU kernels for deep learning
T. Gale, M. Zaharia, C. Young, and E. Elsen · 2020
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Soft threshold weight reparametrization for learnable sparsity
A. Kusupati, V. Ramanujan, R. Somani, M. Wortsman, P. Jain, S. Kakade, and A. Farhadi · 2020
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A signal propagation perspective for pruning neural networks at initialization
N. Lee, T. Ajanthan, S. Gould, and P. H. S. Torr · 2020
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Dynamic model pruning with feedback
T. Lin, S. U. Stich, L. Barba, D. Dmitriev, and M. Jaggi · 2020
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Data-indenpendent neural pruning via coresets
B. Mussay, M. Osadchy, V. Braverman, S. Zhou, and D. Feldman · 2020
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Lookahead: A far-sighted alternative of magnitude-based pruning
S. Park, J. Lee, S. Mo, and J. Shin · 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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Movement pruning: Adaptive sparsity by fine-tuning
V. Sanh, T. Wolf, and A. M. Rush · 2020
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