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The lottery ticket hypothesis states that sparse subnetworks exist in randomly initialized dense networks that can be trained to the same accuracy as the dense network they reside in.
The State of Sparsity in Deep Neural Networks
Gale, T., Elsen, E., and Hooker, S. (2019) · 1902
Earlier work this paper cites.
Sparse Transfer Learning via Winning Lottery Tickets
Mehta, R. (2019) · 1905
Earlier work this paper cites.
Sparse Networks from Scratch: Faster Training without Losing Performance
Dettmers, T. and Zettlemoyer, L. (2019) · 1907
Earlier work this paper cites.
What Do Compressed Deep Neural Networks Forget?
Hooker, S., Courville, A., Clark, G., Dauphin, Y., and Frome, A. (2020) · 1911
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2009) · 2009
Earlier work this paper cites.
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win
Evci, U., Ioannou, Y. A., Keskin, C., and Dauphin, Y. (2020b) · 2010
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K. (2018) · 2018
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P. (2018) · 2018
Cited alongside, same era.
Rethinking the Value of Network Pruning
Liu, Z., Sun, M., Zhou, T., Huang, G., and Darrell, T. (2018) · 2018
Cited alongside, same era.
An Empirical Model of Large-Batch Training
McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D. (2018) · 2018
Cited alongside, same era.
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks
You, H., Li, C., Xu, P., Fu, Y., Wang, Y., Chen, X., Baraniuk, R. G., Wang, Z., and Lin, Y. (2019) · 2019
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Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Zhou, H., Lan, J., Liu, R., and Yosinski, J. (2019) · 2019
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What is the State of Neural Network Pruning?
Blalock, D., Gonzalez Ortiz, J. J., Frankle, J., and Guttag, J. (2020) · 2020
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The Lottery Ticket Hypothesis for Pre-trained BERT Networks
Chen, T., Frankle, J., Chang, S., Liu, S., Zhang, Y., Wang, Z., and Carbin, M. (2020) · 2020
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Soft Threshold Weight Reparameterization for Learnable Sparsity
Kusupati, A., Ramanujan, V., Somani, R., Wortsman, M., Jain, P., Kakade, S., and Farhadi, A. (2020) · 2020
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Winning the Lottery with Continuous Sparsification
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J. and Carbin, M. (2019) · 2019
Cited alongside, same era.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Morcos, A., Yu, H., Paganini, M., and Tian, Y. (2019) · 2019
Cited alongside, same era.
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Renda, A., Frankle, J., and Carbin, M. (2019) · 2019
Cited alongside, same era.
Rigging the Lottery: Making All Tickets Winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E. (2020a)
Cited in the paper.
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M. (2020a)
Cited in the paper.
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M. (2020b)
Cited in the paper.
The Early Phase of Neural Network Training
Frankle, J., Schwab, D. J., and Morcos, A. S. (2020c)
Cited in the paper.
Savarese, P., Silva, H., and Maire, M. (2020) · 2020
Later among the works it cites.
Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., Yamins, D. L., and Ganguli, S. (2020) · 2020
Later among the works it cites.