activity
20182022
most citedIOT: Instance-wise Layer Reordering for Transformer Structures

3 citations · 11 across the 8 of their papers we have counts for

collaborators

15 papers

q-bio.BM20222 cited

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

Kaiyuan Gao, Lijun Wu, Jinhua Zhu +8

Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attrac…

cs.LG20221 cited

Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting

Lin Huang, Lijun Wu, Jia Zhang +2

Time series forecasting plays a key role in a variety of domains. In a lot of real-world scenarios, there exist multiple forecasting entities (e.g. power station in the solar syste…

cs.CV20221 cited

AF: Adaptive Focus Framework for Aerial Imagery Segmentation

Lin Huang, Qiyuan Dong, Lijun Wu +3

As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding. Besides common…

cs.LG2021

Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics

Shiqi Gong, Qi Meng, Yue Wang +4

Learning dynamics governed by differential equations is crucial for predicting and controlling the systems in science and engineering. Neural Ordinary Differential Equation (NODE),…

cs.CL20211 cited

UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost

Zhen Wu, Lijun Wu, Qi Meng +5

Transformer architecture achieves great success in abundant natural language processing tasks. The over-parameterization of the Transformer model has motivated plenty of works to a…

cs.CL20213 cited

IOT: Instance-wise Layer Reordering for Transformer Structures

Jinhua Zhu, Lijun Wu, Yingce Xia +5

With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and…