Progressive skeletonization: trimming more fat from a network at initialization
Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (appro...
| Auteurs principaux: | , , , , , |
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| Format: | Conference item |
| Langue: | English |
| Publié: |
OpenReview
2020
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