no code implementations • 18 Aug 2023 • Rui Ding, Jielong Yang, Feng Ji, Xionghu Zhong, Linbo Xie
To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction.
1 code implementation • 29 Apr 2023 • Feng Ji, See Hian Lee, Hanyang Meng, Kai Zhao, Jielong Yang, Wee Peng Tay
We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution.
no code implementations • 7 Apr 2023 • Feng Ji, See Hian Lee, Kai Zhao, Wee Peng Tay, Jielong Yang
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP).
no code implementations • 28 Feb 2023 • Bohao Qu, Xiaofeng Cao, Jielong Yang, Hechang Chen, Chang Yi, Ivor W. Tsang, Yew-Soon Ong
To resolve this problem, this paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment, in which a policy dispersion scheme is designed for seeking diverse policy representation.
no code implementations • 8 Oct 2022 • Yixiang Shan, Jielong Yang, Xing Liu, Yixing Gao, Hechang Chen, Shuzhi Sam Ge
Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations.
1 code implementation • 25 Jun 2019 • Jielong Yang, Wee Peng Tay
An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations.
no code implementations • 25 Feb 2019 • Feng Ji, Jielong Yang, Qiang Zhang, Wee Peng Tay
In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data.
no code implementations • 8 Jun 2018 • Jielong Yang, Junshan Wang, Wee Peng Tay
We incorporate knowledge of the agents' social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents' reliabilities, communities, and the event states.