no code implementations • 12 May 2023 • Bo Jiang, Fei Xu, Ziyan Zhang, Jin Tang, Feiping Nie
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.
no code implementations • 7 Oct 2022 • Ziyan Zhang, Junhao Shen, Dongwei Yao, Feng Wu
In order to increase the prediction accuracy of the online vehicle velocity prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused with traffic information was presented for the multiple scenarios.
2 code implementations • 31 Jul 2022 • Peng Xia, Yuechi Zhou, Ziyan Zhang, Zecheng Tang, Juntao Li
In view of the poor robustness of existing Chinese grammatical error correction models on attack test sets and large model parameters, this paper uses the method of knowledge distillation to compress model parameters and improve the anti-attack ability of the model.
no code implementations • 26 Apr 2022 • Ziyan Zhang, Bo Jiang, Bin Luo
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning.
no code implementations • MM 2021 • Bo Jiang, Pengfei Sun, Ziyan Zhang, Jin Tang, Bin Luo
Also, GAMnet exploits sparse GM optimization as correspondence solver which is differentiable and can also incorporate discrete one-to-one matching constraints approximately in natural in the final matching prediction.
Ranked #4 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
no code implementations • 29 Sep 2021 • Bo Jiang, Ziyan Zhang, Bin Luo
Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\tilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w. r. t graph structural attacks and noises.
no code implementations • 23 Mar 2020 • Bo Jiang, Ziyan Zhang
To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning.
no code implementations • 26 Apr 2019 • Bo Jiang, Ziyan Zhang, Bin Luo
Given an input graph $\textbf{A}$, LatGCR aims to generate a flexible latent graph $\widetilde{\textbf{A}}$ for graph convolutional representation which obviously enhances the representation capacity and also performs robustly w. r. t graph structural attacks and noises.
Ranked #26 on
Node Classification
on Cora
no code implementations • 22 Jan 2019 • Bo Jiang, Ziyan Zhang, Jin Tang, Bin Luo
In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.
no code implementations • 25 Nov 2018 • Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang
Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.