Search Results for author: Lun Du

Found 17 papers, 6 papers with code

ECMG: Exemplar-based Commit Message Generation

no code implementations5 Mar 2022 Ensheng Shia, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun

The information retrieval-based methods reuse the commit messages of similar code diffs, while the neural-based methods learn the semantic connection between code diffs and commit messages.

Information Retrieval

HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction

no code implementations25 Feb 2022 Chongjian Yue, Lun Du, Qiang Fu, Wendong Bi, Hengyu Liu, Yu Gu, Di Yao

The Temporal Link Prediction task of WSDM Cup 2022 expects a single model that can work well on two kinds of temporal graphs simultaneously, which have quite different characteristics and data properties, to predict whether a link of a given type will occur between two given nodes within a given time span.

Link Prediction

Source Free Unsupervised Graph Domain Adaptation

no code implementations2 Dec 2021 Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, Dongmei Zhang

They utilize labels from the source domain as the supervision signal and are jointly trained on both the source graph and the target graph.

Domain Adaptation Node Classification

Neuron with Steady Response Leads to Better Generalization

no code implementations30 Nov 2021 Qiang Fu, Lun Du, Haitao Mao, Xu Chen, Wei Fang, Shi Han, Dongmei Zhang

Regularization can mitigate the generalization gap between training and inference by introducing inductive bias.

Benchmark Inductive Bias

GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily

1 code implementation29 Oct 2021 Lun Du, Xiaozhou Shi, Qiang Fu, Xiaojun Ma, Hengyu Liu, Shi Han, Dongmei Zhang

For node-level tasks, GNNs have strong power to model the homophily property of graphs (i. e., connected nodes are more similar) while their ability to capture the heterophily property is often doubtful.

Graph Attention

Neuron Campaign for Initialization Guided by Information Bottleneck Theory

1 code implementation14 Aug 2021 Haitao Mao, Xu Chen, Qiang Fu, Lun Du, Shi Han, Dongmei Zhang

Initialization plays a critical role in the training of deep neural networks (DNN).

On the Evaluation of Neural Code Summarization

1 code implementation15 Jul 2021 Ensheng Shi, Yanlin Wang, Lun Du, Junjie Chen, Shi Han, Hongyu Zhang, Dongmei Zhang, Hongbin Sun

To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.

Code Summarization Source Code Summarization

On the Evaluation of Commit Message Generation Models: An Experimental Study

1 code implementation12 Jul 2021 Wei Tao, Yanlin Wang, Ensheng Shi, Lun Du, Shi Han, Hongyu Zhang, Dongmei Zhang, Wenqiang Zhang

We find that: (1) Different variants of the BLEU metric are used in previous works, which affects the evaluation and understanding of existing methods.

Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search

1 code implementation10 Jul 2021 Lun Du, Xiaozhou Shi, Yanlin Wang, Ensheng Shi, Shi Han, Dongmei Zhang

On the other hand, as a specific query may focus on one or several perspectives, it is difficult for a single query representation module to represent different user intents.

Code Search Data Augmentation +1

TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data

no code implementations6 Jun 2021 Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Jiang Zhang, Shi Han, Dongmei Zhang

To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.

graph construction

Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective

no code implementations17 May 2021 Lun Du, Xu Chen, Fei Gao, Kunqing Xie, Shi Han, Dongmei Zhang

Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks.

Link Prediction Network Embedding +1

TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting

no code implementations30 Nov 2020 Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang, Yi Ren, Kaigui Bian, Kunqing Xie

Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.

DANE: Domain Adaptive Network Embedding

1 code implementation3 Jun 2019 Yizhou Zhang, Guojie Song, Lun Du, Shu-wen Yang, Yilun Jin

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.

Domain Adaptation Network Embedding

Tag2Vec: Learning Tag Representations in Tag Networks

no code implementations19 Apr 2019 Junshan Wang, Zhicong Lu, Guojie Song, Yue Fan, Lun Du, Wei. Lin

Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.

Network Embedding TAG

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