87 citations · 390 across the 41 of their papers we have counts for
11 papers · 1 filter
Source Free Unsupervised Graph Domain Adaptation
Haitao Mao, Lun Du, Yujia Zheng +5
Graph Neural Networks (GNNs) have achieved great success on a variety of tasks with graph-structural data, among which node classification is an essential one. Unsupervised Graph D…
Neuron with Steady Response Leads to Better Generalization
Qiang Fu, Lun Du, Haitao Mao +4
Regularization can mitigate the generalization gap between training and inference by introducing inductive bias. Existing works have already proposed various inductive biases from…
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily
Lun Du, Xiaozhou Shi, Qiang Fu +4
Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine learning tasks. For node-level tasks, GNNs have strong power to model the homophily property of gra…
Neuron Campaign for Initialization Guided by Information Bottleneck Theory
Haitao Mao, Xu Chen, Qiang Fu +3
Initialization plays a critical role in the training of deep neural networks (DNN). Existing initialization strategies mainly focus on stabilizing the training process to mitigate…
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees
Ensheng Shi, Yanlin Wang, Lun Du +4
Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance. Recent studies show that syn…
On the Evaluation of Commit Message Generation Models: An Experimental Study
Wei Tao, Yanlin Wang, Ensheng Shi +5
Commit messages are natural language descriptions of code changes, which are important for program understanding and maintenance. However, writing commit messages manually is time-…
Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search
Lun Du, Xiaozhou Shi, Yanlin Wang +3
Recently, deep learning methods have become mainstream in code search since they do better at capturing semantic correlations between code snippets and search queries and have prom…
CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network
Yanlin Wang, Ensheng Shi, Lun Du +5
Source code summaries are short natural language descriptions of code snippets that help developers better understand and maintain source code. There has been a surge of work on au…
On the Evaluation of Neural Code Summarization
Ensheng Shi, Yanlin Wang, Lun Du +5
Source code summaries are important for program comprehension and maintenance. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep…
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
Lun Du, Fei Gao, Xu Chen +5
Tabular data are ubiquitous for the widespread applications of tables and hence have attracted the attention of researchers to extract underlying information. One of the critical p…
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
Lun Du, Xu Chen, Fei Gao +4
Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, the noisy information behind th…