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With the introduction of SNIP [arXiv:1810.02340v2], it has been demonstrated that modern neural networks can effectively be pruned before training.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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, and S. A. Solla · 1990
Earlier work this paper cites.
Generalization by weight-elimination with application to forecasting
A. S. Weigend, D. E. Rumelhart, and B. A. Huberman · 1991
Earlier work this paper cites.
Optimal brain surgeon: Extensions and performance comparisons
B. Hassibi, D. G. Stork, and G. Wolff · 1994
Earlier work this paper cites.
Mathematics for economic analysis
K. Sydsaeter and P. J. Hammond · 1995
Earlier work this paper cites.
Sparse bayesian learning and the relevance vector machine
M. E. Tipping · 2001
Earlier work this paper cites.
Iterative Methods for Sparse Linear Systems
Y. Saad · 2003
Earlier work this paper cites.
Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks
A. Buluç, J. T. Fineman, M. Frigo, J. R. Gilbert, and C. E. Leiserson · 2009
Earlier work this paper cites.
Handling sparsity via the horseshoe
C. M. Carvalho, N. G. Polson, and J. G. Scott · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Python 3 Reference Manual
G. Van Rossum and F. L. Drake · 2009
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.
Big neural networks waste capacity
Y. N. Dauphin and Y. Bengio · 2013
Earlier work this paper cites.
Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, M. A. Ranzato, and N. de Freitas · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
A scale elasticity measure for directional distance function and its dual: Theory and dea estimation
V. Zelenyuk · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
J. Ba and R. Caruana · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally · 2015
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.
Variational dropout and the local reparameterization trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
Earlier work this paper cites.
Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
Earlier work this paper cites.
Optimization Methods for Large-Scale Machine Learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2016
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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 · 2016
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Deep residual learning for image recognition
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.
Pruning Filters for Efficient ConvNets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2016
Cited alongside, same era.
Pruning Convolutional Neural Networks for Resource Efficient Inference
Stabilizing the Lottery Ticket Hypothesis
J. Frankle, G. Karolina Dziugaite, D. M. Roy, and M. Carbin · 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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Learning sparse networks using targeted dropout
A. N. Gomez, I. Zhang, K. Swersky, Y. Gal, and G. E. Hinton · 2019
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SNIP: Single-shot network pruning based on connection sensitivity
N. Lee, T. Ajanthan, and P. Torr · 2019
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Single-shot channel pruning based on alternating direction method of multipliers
C. Li, Z. Wang, X. Wang, and H. Qi · 2019
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P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 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.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 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.
Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
Cited alongside, same era.
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AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
N. Liu, X. Ma, Z. Xu, Y. Wang, J. Tang, and J. Ye · 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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Sparsefool: a few pixels make a big difference
A. Modas, S.-M. Moosavi-Dezfooli, and P. Frossard · 2019
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
A. Morcos, H. Yu, M. Paganini, and Y. Tian · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 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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AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
J. Yu and T. Huang · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask
H. Zhou, J. Lan, R. Liu, and J. Yosinski · 2019
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Pruning untrained neural networks: Principles and analysis
S. Hayou, J.-F. Ton, A. Doucet, and Y. W. Teh · 2020
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Imagenette, 2019
J. Howard · 2020
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2020
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 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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Data parallelism in training sparse neural networks
N. Lee, P. H. Torr, and M. Jaggi · 2020
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Z. Li, Y. Gong, X. Ma, S. Liu, M. Sun, Z. Zhan, Z. Kong, G. Yuan, and Y. Wang · 2020
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Proving the lottery ticket hypothesis: Pruning is all you need
E. Malach, G. Yehudai, S. Shalev-Shwartz, and O. Shamir · 2020
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Adversarial attacks and defenses in deep learning
K. Ren, T. Zheng, Z. Qin, and X. Liu · 2020
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On pruning adversarially robust neural networks
V. Sehwag, S. Wang, P. Mittal, and S. Jana · 2020
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Picking winning tickets before training by preserving gradient flow
C. Wang, G. Zhang, and R. Grosse · 2020
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Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures
H. Yang, W. Wen, and H. Li · 2020
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Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp
H. Yu, S. Edunov, Y. Tian, and A. S. Morcos · 2020
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