39 citations · 103 across the 8 of their papers we have counts for
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cs.CL2022★ 39 cited
A Fast Post-Training Pruning Framework for Transformers
Woosuk Kwon, Sehoon Kim, Michael W. Mahoney +3
Pruning is an effective way to reduce the huge inference cost of Transformer models. However, prior work on pruning Transformers requires retraining the models. This can add high t…
cs.CL2021★ 24 cited
Learned Token Pruning for Transformers
Sehoon Kim, Sheng Shen, David Thorsley +4
Deploying transformer models in practice is challenging due to their inference cost, which scales quadratically with input sequence length. To address this, we present a novel Lear…