1 code implementation • 15 Oct 2022 • Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs.
1 code implementation • NAACL 2022 • Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi
In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.
no code implementations • Findings (NAACL) 2022 • Juncheng Liu, Zequn Sun, Bryan Hooi, Yiwei Wang, Dayiheng Liu, Baosong Yang, Xiaokui Xiao, Muhao Chen
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.
1 code implementation • NeurIPS 2021 • Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao
Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN), to efficiently capture very long-range dependencies.
1 code implementation • 28 Oct 2021 • Jinhui Yuan, Xinqi Li, Cheng Cheng, Juncheng Liu, Ran Guo, Shenghang Cai, Chi Yao, Fei Yang, Xiaodong Yi, Chuan Wu, Haoran Zhang, Jie Zhao
Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model.
1 code implementation • 17 May 2021 • Xinjian Luo, Xiaokui Xiao, Yuncheng Wu, Juncheng Liu, Beng Chin Ooi
InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye.
1 code implementation • 13 Dec 2020 • Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao
We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification.
no code implementations • 10 Aug 2020 • Juncheng Liu, Steven Mills, Brendan McCane
Our network compresses a voxel grid of any size down to a very small latent space in an autoencoder-like network.
no code implementations • CVPR 2017 • Juncheng Liu, Zhouhui Lian, Yi Wang, Jianguo Xiao
This validates the superiority of our IKNDA against the state of the art in novelty detection for large-scale data.