Papers with compact modules

1 papers
BERT-of-Theseus: Compressing BERT by Progressive Module Replacing (2020.emnlp-main)

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Challenge: a novel approach to compress neural networks by progressive module replacement is proposed . a number of techniques have been proposed to compress pretraining and fine-tuning models .
Approach: They propose a model compression approach that divides BERT into modules and builds their compact substitutes.
Outcome: The proposed approach outperforms existing knowledge distillation approaches on GLUE benchmark . it is based on a model that divides the original BERT into several modules and builds their substitutes .

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