80 citations · 83 across the 3 of their papers we have counts for
3 papers
cs.CL2021
LightSeq2: Accelerated Training for Transformer-based Models on GPUs
Xiaohui Wang, Yang Wei, Ying Xiong +5
Transformer-based neural models are used in many AI applications. Training these models is expensive, as it takes huge GPU resources and long duration. It is challenging because ty…
cs.MS2020★ 3 cited
LightSeq: A High Performance Inference Library for Transformers
Xiaohui Wang, Ying Xiong, Yang Wei +2
Transformer, BERT and their variants have achieved great success in natural language processing. Since Transformer models are huge in size, serving these models is a challenge for…
physics.med-ph2018★ 80 cited
Image Domain Dual Material Decomposition for Dual-Energy CT using Butterfly Network
Wenkun Zhang, Hanming Zhang, Linyuan Wang +5
Dual-energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnif…