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The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network.
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
Yu, H.; Edunov, S.; Tian, Y.; and Morcos, A. S. 2020 · 1906
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Optimal Brain Damage
Cun, Y. L.; Denker, J. S.; and Solla, S. A. 1990 · 1990
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Optimal Brain Surgeon: Extensions and Performance Comparisons
Hassibi, B.; Stork, D. G.; Wolff, G.; and Watanabe, T. 1993 · 1993
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
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MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
Earlier work this paper cites.
Generative Adversarial Networks
Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A. C.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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Learning both Weights and Connections for Efficient Neural Network
Han, S.; Pool, J.; Tran, J.; and Dally, W. J. 2015 · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Deep Learning Face Attributes in the Wild
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2015 · 2015
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A.; Metz, L.; and Chintala, S. 2015 · 2015
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DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow
Han, S.; Pool, J.; Narang, S.; Mao, H.; Tang, S.; Elsen, E.; Catanzaro, B.; Tran, J.; and Dally, W. 2016 · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Improving Variational Inference with Inverse Autoregressive Flow
Kingma, D. P.; Salimans, T.; Jozefowicz, R.; Chen, X.; Sutskever, I.; and Welling, M. 2016 · 2016
Cited alongside, same era.
Pruning Filters for Efficient ConvNets
Li, H.; Kadav, A.; Durdanovic, I.; Samet, H.; and Graf, H. P. 2016 · 2016
Cited alongside, same era.
Learning Structured Sparsity in Deep Neural Networks
Wen, W.; Wu, C.; Wang, Y.; Chen, Y.; and Li, H. 2016 · 2016
Cited alongside, same era.
Wasserstein GAN
Arjovsky, M.; Chintala, S.; and Bottou, L. 2017 · 2017
Cited alongside, same era.
Improved Training of Wasserstein GANs
Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; and Courville, A. 2017 · 2017
Cited alongside, same era.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
Cited alongside, same era.
Compressing GANs using Knowledge Distillation
Aguinaldo, A.; Chiang, P.-Y.; Gain, A.; Patil, A.; Pearson, K.; and Feizi, S. 2019 · 2019
Later among the works it cites.
Cerebras wafer scale engine: An introduction, 2019
Cerebras. 2019 · 2019
Later among the works it cites.
Evaluating Lottery Tickets Under Distributional Shifts
Desai, S.; Zhan, H.; and Aly, A. 2019 · 2019
Later among the works it cites.
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Frankle, J.; Dziugaite, G. K.; Roy, D. M.; and Carbin, M. 2019 · 2019
Later among the works it cites.
SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
Lee, N.; Ajanthan, T.; and Torr, P. 2019 · 2019
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Sparse Transfer Learning via Winning Lottery Tickets
Mehta, R. 2019 · 2019
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M. M.; Mohamed, S.; and Lerchner, A. 2017 · 2017
Cited alongside, same era.
Learning Efficient Convolutional Networks through Network Slimming
Liu, Z.; Li, J.; Shen, Z.; Huang, G.; Yan, S.; and Zhang, C. 2017 · 2017
Cited alongside, same era.
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2018 · 2018
Cited alongside, same era.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J.; and Carbin, M. 2018 · 2018
Cited alongside, same era.
LIT: Block-wise Intermediate Representation Training for Model Compression
Koratana, A.; Kang, D.; Bailis, P.; and Zaharia, M. 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.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Morcos, A. S.; Yu, H.; Paganini, M.; and Tian, Y. 2019 · 2019
Later among the works it cites.
Generating Diverse High-Fidelity Images with VQ-VAE-2
Razavi, A.; van den Oord, A.; and Vinyals, O. 2019 · 2019
Later among the works it cites.
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Zhou, H.; Lan, J.; Liu, R.; and Yosinski, J. 2019 · 2019
Later among the works it cites.
Pruning Neural Networks at Initialization: Why are We Missing the Mark?
Frankle, J.; Dziugaite, G. K.; Roy, D. M.; and Carbin, M. 2020 · 2020
Closest in time.
The Early Phase of Neural Network Training
Frankle, J.; Schwab, D. J.; and Morcos, A. S. 2020 · 2020
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Nvidia a100 tensor core gpu architecture, 2020
NVIDIA. 2020 · 2020
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Picking Winning Tickets Before Training by Preserving Gradient Flow
Wang, C.; Zhang, G.; and Grosse, R. 2020 · 2020
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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. 2020 · 2020
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