10 citations · 13 across the 9 of their papers we have counts for
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cs.LG2023★ 1 cited
Understanding Parameter Sharing in Transformers
Ye Lin, Mingxuan Wang, Zhexi Zhang +3
Parameter sharing has proven to be a parameter-efficient approach. Previous work on Transformers has focused on sharing parameters in different layers, which can improve the perfor…
cs.AI2023
MobileNMT: Enabling Translation in 15MB and 30ms
Ye Lin, Xiaohui Wang, Zhexi Zhang +3
Deploying NMT models on mobile devices is essential for privacy, low latency, and offline scenarios. For high model capacity, NMT models are rather large. Running these models on d…
cs.CL2023
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation
Ye Lin, Shuhan Zhou, Yanyang Li +3
For years the model performance in machine learning obeyed a power-law relationship with the model size. For the consideration of parameter efficiency, recent studies focus on incr…