Search Results for author: Ziyan Zhang

Found 13 papers, 2 papers with code

Graph Learning-Convolutional Networks

no code implementations25 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.

graph construction Graph Learning

Multiple Graph Adversarial Learning

no code implementations22 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.

Robust Graph Data Learning via Latent Graph Convolutional Representation

no code implementations26 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.

Graph Learning Node Classification +1

Incomplete Graph Representation and Learning via Partial Graph Neural Networks

no code implementations23 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.

Attribute

Robust Graph Data Learning with Latent Graph Convolutional Representation

no code implementations29 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.

Graph Learning

GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network

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 #7 on Graph Matching on PASCAL VOC (matching accuracy metric)

Graph Matching

Unified GCNs: Towards Connecting GCNs with CNNs

no code implementations26 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.

Chinese grammatical error correction based on knowledge distillation

2 code implementations31 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.

Grammatical Error Correction Knowledge Distillation

Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion

no code implementations7 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.

AGFormer: Efficient Graph Representation with Anchor-Graph Transformer

no code implementations12 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.

VcT: Visual change Transformer for Remote Sensing Image Change Detection

1 code implementation17 Oct 2023 Bo Jiang, Zitian Wang, Xixi Wang, Ziyan Zhang, Lan Chen, Xiao Wang, Bin Luo

Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction.

Change Detection Representation Learning

Unifying Graph Contrastive Learning via Graph Message Augmentation

no code implementations8 Jan 2024 Ziyan Zhang, Bo Jiang, Jin Tang, Bin Luo

Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning.

Contrastive Learning Data Augmentation +2

Power Transformer Fault Prediction Based on Knowledge Graphs

no code implementations11 Feb 2024 Chao Wang, Zhuo Chen, Ziyan Zhang, Chiyi Li, Kai Song

In this paper, we address the challenge of learning with limited fault data for power transformers.

Knowledge Graphs

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