3 citations · 5 across the 5 of their papers we have counts for
5 papers
Patch Is Not All You Need
Changzhen Li, Jie Zhang, Yang Wei +3
Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input e…
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…
In-Order Chart-Based Constituent Parsing
Yang Wei, Yuanbin Wu, Man Lan
We propose a novel in-order chart-based model for constituent parsing. Compared with previous CKY-style and top-down models, our model gains advantages from in-order traversal of a…
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…
A Span-based Linearization for Constituent Trees
Yang Wei, Yuanbin Wu, Man Lan
We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all s…