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Recent network pruning methods focus on pruning models early-on in training.
Optimal Brain Damage
Y. LeCun, J. S. Denker, and S. A. Solla · 1990
Earlier work this paper cites.
Second Order Derivatives For Network Pruning: Optimal Brain Surgeon
B. Hassibi and D. G. Stork · 1993
Earlier work this paper cites.
Understanding the Difficulty of Training Deep Feedforward Neural Networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Pruning Filters For Efficient ConvNets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Earlier work this paper cites.
Learning Efficient Convolutional Networks Through Network Slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Earlier work this paper cites.
Pruning Convolutional Neural Networks For Resource Efficient Inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
Earlier work this paper cites.
Generalization in Deep Networks: The Role of Distance from Initialization
V. Nagarajan and Z. Kolter · 2017
Earlier work this paper cites.
SVCCA: Singular Vector Canonical Correlation Analysis For Deep Learning Dynamics and Interpretability
M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein · 2017
Earlier work this paper cites.
Soft Filter Pruning For Accelerating Deep Convolutional Neural Networks
Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang · 2018
Cited alongside, same era.
Faster Gaze Prediction with Dense Networks and Fisher Pruning
L. Theis, I. Korshunova, A. Tejani, and F. Huszár · 2018
Cited alongside, same era.
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
Cited alongside, same era.
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
X. Ding, G. Ding, X. Zhou, Y. Guo, J. Han, and J. Liu · 2019
Cited alongside, same era.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
J. Frankle and M. Carbin · 2019
Cited alongside, same era.
Rate Distortion For Model Compression: From Theory To Practice
W. Gao, Y. Liu, C. Wang, and S. Oh · 2019
Importance Estimation For Neural Network Pruning
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz · 2019
Later among the works it cites.
Fast Sparse ConvNets
E. Elsen, M. Dukhan, T. Gale, and K. Simonyan · 2020
Closest in time.
Pruning untrained neural networks: Principles and Analysis
S. Hayou, J. Ton, A. Doucet, and Y. W. Teh · 2020
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A Signal Propagation Perspective For Pruning Neural Networks at Initialization
N. Lee, T. Ajanthan, S. Gould, and P. Torr · 2020
Closest in time.
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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Picking Winning Tickets Before Training by Preserving Gradient Flow
C. Wang, G. Zhang, and R. Grosse · 2020
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Cited alongside, same era.
SNIP: Single-Shot Network Pruning Based on Connection Sensitivity
N. Lee, T. Ajanthan, and P. Torr · 2019
Cited alongside, same era.
ThiNet: Pruning CNN Filters For a Thinner Net
J. Luo, H. Zhang, H. Zhou, C. Xie, J. Wu, and W. Lin · 2019
Cited alongside, same era.
EIE: Efficient Inference Engine on Compressed Deep Neural Network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. Horowitz, and W. J. Dally
Cited in the paper.
Learning Both Weights and Connections For Efficient Neural Networks
S. Han, J. Pool, J. Tran, and W. J. Dally
Cited in the paper.
Closest in time.
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
M. Ye, C. Gong, L. Nie, D. Zhou, A. Klivans, and Q. Liu · 2020
Closest in time.
Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks
H. You, C. Li, P. Xu, Y. Fu, Y. Wang, X. Chen, R. G. Baraniuk, Z. Wang, and Y. Lin · 2020
Closest in time.