Search Results for author:
Found 10 papers, 1 papers with code
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
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.
Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion
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.
Chinese grammatical error correction based on knowledge distillation
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.
Unified GCNs: Towards Connecting GCNs with CNNs
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning.
GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network
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)
Robust Graph Data Learning with Latent Graph Convolutional Representation
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.
Incomplete Graph Representation and Learning via Partial Graph Neural Networks
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.
Robust Graph Data Learning via Latent Graph Convolutional Representation
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 #28 on
Node Classification
on Cora
Multiple Graph Adversarial Learning
In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.
Graph Learning-Convolutional Networks
Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.
