5 citations · 8 across the 6 of their papers we have counts for
6 papers
Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction
Jinhong Wang, Yi Cheng, Jintai Chen +3
Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc.…
Robust Image Ordinal Regression with Controllable Image Generation
Yi Cheng, Haochao Ying, Renjun Hu +5
Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very com…
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Jiahuan Yan, Jintai Chen, Yixuan Wu +2
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogenei…
Robust Training of Graph Neural Networks via Noise Governance
Siyi Qian, Haochao Ying, Renjun Hu +4
Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-exp…
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
Yao Wan, Zhou Zhao, Min Yang +4
Code summarization provides a high level natural language description of the function performed by code, as it can benefit the software maintenance, code categorization and retriev…
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training
Yao Wan, Wenqiang Yan, Jianwei Gao +3
Dialogue Act (DA) classification is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. C…