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The Strong Lottery Ticket Hypothesis (SLTH) stipulates the existence of a subnetwork within a sufficiently overparameterized (dense) neural network that -- when initialized randomly and without any training -- achieves the accuracy of a fully trained target network.
Exponentially small bounds on the expected optimum of the partition and subset sum problems
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A general theory of equivariant CNNs on homogeneous spaces
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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Proving the lottery ticket hypothesis: Pruning is all you need
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Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient
A. Pensia, S. Rajput, A. Nagle, H. Vishwakarma, and D. Papailiopoulos · 2020
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What’s hidden in a randomly weighted neural network?
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Deep graph kernels
P. Yanardag and S. Vishwanathan · 2020
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Towards strong pruning for lottery tickets with non-zero biases
J. Fischer and R. Burkholz · 2021
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Efficient equivariant network
L. He, Y. Chen, Y. Dong, Y. Wang, Z. Lin, et al · 2021
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On the existence of universal lottery tickets
R. Burkholz, N. Laha, R. Mukherjee, and A. Gotovos · 2022
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Logarithmic pruning is all you need
L. Orseau, M. Hutter, and O. Rivasplata · 2020
Cited alongside, same era.
Convolutional and residual networks provably contain lottery tickets
R. Burkholz
Cited in the paper.
Most activation functions can win the lottery without excessive depth
R. Burkholz
Cited in the paper.
Revisiting the random subset sum problem
A. da Cunha, F. d’Amore, F. Giroire, H. Lesfari, E. Natale, and L. Viennot
Cited in the paper.
Proving the strong lottery ticket hypothesis for convolutional neural networks
A. da Cunha, E. Natale, and L. Viennot
Cited in the paper.
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Finding nearly everything within random binary networks
K. Sreenivasan, S. Rajput, J.-Y. Sohn, and D. Papailiopoulos · 2022
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