Comparing Rewinding and Fine-tuning in Neural Network Pruning

By Alex Renda, Jonathan Frankle, Michael Carbin. 2019

21 citations recorded by the source corpus.

Subfields: Efficiency & Compression

How researchers see this paper

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In Efficiency & Compression

1. Jonathan Frankle
2. Michael Carbin
3. Nir Shavit
4. Jonathan S. Rosenfeld
5. Michael Crawshaw
6. Naomi Saphra
7. Thomas S. Huang
8. Jiahui Yu

Citations in the corpus

• On the Predictability of Pruning Across Scales (2020, methodology): “In practice, pruning can reduce the parameter-counts of contemporary models by 2x (Gordon et al., 2020) to 5x (Renda et al., 2020) with no increase in error.”