10 citations · 13 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
The NiuTrans System for WNGT 2020 Efficiency Task
Chi Hu, Bei Li, Ye Lin +5
This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models \cite{wang-et…
The NiuTrans System for the WMT21 Efficiency Task
Chenglong Wang, Chi Hu, Yongyu Mu +8
This paper describes the NiuTrans system for the WMT21 translation efficiency task (http://statmt.org/wmt21/efficiency-task.html). Following last year's work, we explore various te…
Bag of Tricks for Optimizing Transformer Efficiency
Ye Lin, Yanyang Li, Tong Xiao +1
Improving Transformer efficiency has become increasingly attractive recently. A wide range of methods has been proposed, e.g., pruning, quantization, new architectures and etc. But…