6 citations · 14 across the 12 of their papers we have counts for
4 papers · 1 filter
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Ying Chen, Siwei Qiang, Mingming Ha +5
In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse sce…
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Youru Li, Zhenfeng Zhu, Xiaobo Guo +3
Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied…
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +5
Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of…
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
Wujiang Xu, Runzhong Wang, Xiaobo Guo +6
Video summarization intends to produce a concise video summary by effectively capturing and combining the most informative parts of the whole content. Existing approaches for video…