no code implementations • 15 Dec 2023 • Tianhao Peng, Wenjun Wu, Haitao Yuan, Zhifeng Bao, Zhao Pengrui, Xin Yu, Xuetao Lin, Yu Liang, Yanjun Pu
To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs.
Ranked #2 on Node Classification on Cornell
no code implementations • 15 Oct 2023 • Renyang Liu, Jun Zhao, Xing Chu, Yu Liang, Wei Zhou, Jing He
With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms.
1 code implementation • 14 Aug 2023 • Yu Liang, Shiliang Zhang, YaoWei Wang, Sheng Xiao, Kenli Li, Xiaoyu Wang
As a solution, backward-compatible training can be employed to avoid the necessity of updating old retrieval datasets.
1 code implementation • 30 Jul 2023 • Tianhao Peng, Yu Liang, Wenjun Wu, Jian Ren, Zhao Pengrui, Yanjun Pu
Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students.
no code implementations • 26 Jul 2022 • Honghao Huang, Jiajie Teng, Yu Liang, Chengyang Hu, Minghua Chen, Sigang Yang, Hongwei Chen
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition.
no code implementations • 17 Jul 2020 • Yu Liang, Arin Chaudhuri, Haoyu Wang
Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science.
no code implementations • 2 Oct 2019 • Dakila Ledesma, Yu Liang, Dalei Wu
This paper proposed a hierarchical visible autoencoder in the adaptive phantom limbs generation according to the kinetic behavior of functional body-parts, which are measured by heterogeneous kinetic sensors.