A signal propagation perspective for pruning neural networks at initialization
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and then removing redundant parameters while minimizing the impact on what is learned. Alternatively, a recent approach shows that pruning can be done at initializatio...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
International Conference on Learning Representations
2019
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