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"Sparsity in Deep Learning: Pruning and growth for efficient inference and ..."

Torsten Hoefler et al. (2021)

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DOI: —

access: open

type: Journal Article

metadata version: 2022-01-31

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  - https://dblp.org/rec/journals/jmlr/HoeflerABDP21
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22: 241:1-241:124 (2021)

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