8 citations · 14 across the 6 of their papers we have counts for
7 papers
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
Wei Ju, Xiao Luo, Meng Qu +5
This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adop…
ARGO: Modeling Heterogeneity in E-commerce Recommendation
Daqing Wu, Xiao Luo, Zeyu Ma +3
Nowadays, E-commerce is increasingly integrated into our daily lives. Meanwhile, shopping process has also changed incrementally from one behavior (purchase) to multiple behaviors…
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation
Shengsen Wu, Liang Chen, Yihang Lou +4
In object re-identification (ReID), the development of deep learning techniques often involves model updates and deployment. It is unbearable to re-embedding and re-index with the…
DNA-GCN: Graph convolutional networks for predicting DNA-protein binding
Yuhang Guo, Xiao Luo, Liang Chen +1
Predicting DNA-protein binding is an important and classic problem in bioinformatics. Convolutional neural networks have outperformed conventional methods in modeling the sequence…
Deep Unsupervised Hashing by Distilled Smooth Guidance
Xiao Luo, Zeyu Ma, Daqing Wu +4
Hashing has been widely used in approximate nearest neighbor search for its storage and computational efficiency. Deep supervised hashing methods are not widely used because of the…
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
Xiao Luo, Daqing Wu, Yiyang Gu +7
Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing atten…
Graph Contrastive Clustering
Huasong Zhong, Jianlong Wu, Chong Chen +5
Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, thes…