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The Lottery Ticket Hypothesis continues to have a profound practical impact on the quest for small scale deep neural networks that solve modern deep learning tasks at competitive performance.
Skeletonization: A technique for trimming the fat from a network via relevance assessment
Mozer, M. C. and Smolensky, P · 1989
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Generalization by weight-elimination with application to forecasting
Weigend, A., Rumelhart, D., and Huberman, B · 1991
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Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B. and Stork, D. G · 1992
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Exponentially small bounds on the expected optimum of the partition and subset sum problems
Lueker, G. S · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Srinivas, S. and Babu, R. V · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Dong, X., Chen, S., and Pan, S. J · 2017
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2017
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When and why are deep networks better than shallow ones?
Mhaskar, H., Liao, Q., and Poggio, T · 2017
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Pruning convolutional neural networks for resource efficient inference
Molchanov, P., Tyree, S., Karras, T., Aila, T., and Kautz, J · 2017
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Optimal approximation of continuous functions by very deep relu networks
Yarotsky, D · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M · 2019
Cited alongside, same era.
Snip: single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P. H. S · 2019
Cited alongside, same era.
Deconstructing lottery tickets: Zeros, signs, and the supermask
Zhou, H., Lan, J., Liu, R., and Yosinski, J · 2019
Cited alongside, same era.
Rigging the lottery: Making all tickets winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2020
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M · 2020
Cited alongside, same era.
A signal propagation perspective for pruning neural networks at initialization
Lee, N., Ajanthan, T., Gould, S., and Torr, P. H. S · 2020
Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., Yamins, D. L., and Ganguli, S · 2020
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Pruning via iterative ranking of sensitivity statistics, 2020
Verdenius, S., Stol, M., and Forré, P · 2020
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Picking winning tickets before training by preserving gradient flow
Wang, C., Zhang, G., and Grosse, R. B · 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
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Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network
Diffenderfer, J. and Kailkhura, B · 2021
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Towards strong pruning for lottery tickets with non-zero biases, 2021
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Cited alongside, same era.
Proving the lottery ticket hypothesis: Pruning is all you need
Malach, E., Yehudai, G., Shalev-Schwartz, S., and Shamir, O · 2020
Cited alongside, same era.
Logarithmic pruning is all you need
Orseau, L., Hutter, M., and Rivasplata, O · 2020
Cited alongside, same era.
Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient
Pensia, A., Rajput, S., Nagle, A., Vishwakarma, H., and Papailiopoulos, D · 2020
Cited alongside, same era.
What’s hidden in a randomly weighted neural network?
Ramanujan, V., Wortsman, M., Kembhavi, A., Farhadi, A., and Rastegari, M · 2020
Cited alongside, same era.
Comparing rewinding and fine-tuning in neural network pruning
Renda, A., Frankle, J., and Carbin, M · 2020
Cited alongside, same era.
Sanity-checking pruning methods: Random tickets can win the jackpot
Su, J., Chen, Y., Cai, T., Wu, T., Gao, R., Wang, L., and Lee, J. D · 2020
Cited alongside, same era.
Fischer, J. and Burkholz, R · 2021
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Pruning neural networks at initialization: Why are we missing the mark?
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M · 2021
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Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?
Ma, X., Yuan, G., Shen, X., Chen, T., Chen, X., Chen, X., Liu, N., Qin, M., Liu, S., Wang, Z., and Wang, Y · 2021
Later among the works it cites.
Most activation functions can win the lottery without excessive depth
Burkholz, R · 2022
Closest in time.
On the existence of universal lottery tickets
Burkholz, R., Laha, N., Mukherjee, R., and Gotovos, A · 2022
Closest in time.
Proving the lottery ticket hypothesis for convolutional neural networks
da Cunha, A., Natale, E., and Viennot, L · 2022
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Plant ’n’ seek: Can you find the winning ticket?
Fischer, J. and Burkholz, R · 2022
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