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We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters.
Matching pursuits with time-frequency dictionaries
S. G. Mallat and Z. Zhang · 1993
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Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, and N. De Freitas · 2013
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EIE: Efficient inference engine on compressed deep neural network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Gradient descent provably optimizes over-parameterized neural networks
S. S. Du, X. Zhai, B. Poczos, and A. Singh · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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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 · 2019
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Scaling laws for neural language models
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2020
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Deep learning: a statistical viewpoint
P. L. Bartlett, A. Montanari, and A. Rakhlin · 2021
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T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste · 2021
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