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cs.CL2022★ 4 cited
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers
Zhewei Yao, Xiaoxia Wu, Conglong Li +4
Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohib…
cs.CL2022
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding
Connor Holmes, Minjia Zhang, Yuxiong He +1
In recent years, large pre-trained Transformer networks have demonstrated dramatic improvements in many natural language understanding tasks. However, the huge size of these models…