1 code implementation • Findings (EMNLP) 2021 • Junjie Wang, Yatai Ji, Jiaqi Sun, Yujiu Yang, Tetsuya Sakai
On the other hand, trilinear models such as the CTI model efficiently utilize the inter-modality information between answers, questions, and images, while ignoring intra-modality information.
no code implementations • 15 Apr 2024 • Jiayi Li, Ruilin Luo, Jiaqi Sun, Jing Xiao, Yujiu Yang
In this paper, we investigate three crucial processes relevant to real-world construction scenarios: (a) the verification process, which arises from the necessity and limitations of human verifiers; (b) the mining process, which identifies the most promising candidates for verification; and (c) the training process, which harnesses verified data for subsequent utilization; in order to achieve a transition toward more realistic challenges.
1 code implementation • 25 Sep 2023 • Jiayi Li, Ruilin Luo, Jiaqi Sun, Jing Xiao, Yujiu Yang
Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC).
Ranked #5 on Link Property Prediction on ogbl-biokg
1 code implementation • 10 Jun 2023 • Xuanzhou Liu, Lin Zhang, Jiaqi Sun, Yujiu Yang, Haiqin Yang
Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature.
1 code implementation • 23 May 2023 • Peng Xu, Lin Zhang, Xuanzhou Liu, Jiaqi Sun, Yue Zhao, Haiqin Yang, Bei Yu
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures.
1 code implementation • 10 May 2023 • Jiaqi Sun, Lin Zhang, Guangyi Chen, Kun Zhang, Peng Xu, Yujiu Yang
Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification.
1 code implementation • 15 Oct 2022 • Jiaqi Sun, Lin Zhang, Shenglin Zhao, Yujiu Yang
Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification.