activity
20192023
most citedTime-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

3 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Generalization bound for estimating causal effects from observational network data

Ruichu Cai, Zeqin Yang, Weilin Chen +2

Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analys…

cs.LG20223 cited

Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

Zijian Li, Ruichu Cai, Jiawei Chen +4

Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-serie…

eess.IV20211 cited

Weighing Features of Lung and Heart Regions for Thoracic Disease Classification

Jiansheng Fang, Yanwu Xu, Yitian Zhao +3

Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, t…

cs.IR20211 cited

Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval

Jiansheng Fang, Huazhu Fu, Dan Zeng +3

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a quer…

eess.IV2019

Attention Guided Network for Retinal Image Segmentation

Shihao Zhang, Huazhu Fu, Yuguang Yan +5

Learning structural information is critical for producing an ideal result in retinal image segmentation. Recently, convolutional neural networks have shown a powerful ability to ex…