Search Results for author:
Found 24 papers, 2 papers with code
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs.
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
In this paper, we propose a new hierarchical pooling operation, namely the Edge-Node Attention-based Differentiable Pooling (ENADPool), for GNNs to learn effective graph representations.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.
Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System
Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS.
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification.
Graph Representation Learning for Infrared and Visible Image Fusion
Then, GCNs are performed on the concatenate intra-modal NLss features of infrared and visible images, which can explore the cross-domain NLss of inter-modal to reconstruct the fused image.
Graph Representation Learning
Infrared And Visible Image Fusion
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
Both the jumps and the diffusion distances react to classification errors (i. e. they are learnable).
Ranked #1 on
Node Classification
on Chameleon
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
For pairwise graphs, the proposed AERK kernel is defined by computing a reproducing kernel based similarity between the quantum Shannon entropies of their each pair of aligned vertices.
Quantum Graph Learning: Frontiers and Outlook
However, facilitating quantum theory to enhance graph learning is in its infancy.
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification
In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification.
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification
In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs.
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective
The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
To address this problem, we introduce a lightweight but effective Global Matching Component (GMC) to grab global matching features.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
First, it incorporates the locational correspondence information between graphs into the kernel computation, and thus overcomes the shortcoming of ignoring structural correspondences arising in most R-convolution kernels.
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis
With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks.
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification.
Quantum-based subgraph convolutional neural networks
This new architecture captures both the global topological structure and the local connectivity structure within a graph.
Ranked #11 on
Graph Classification
on MUTAG
Fused Lasso for Feature Selection using Structural Information
To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples.
Learning Vertex Convolutional Networks for Graph Classification
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification.
Identifying The Most Informative Features Using A Structurally Interacting Elastic Net
With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation.
Graph Convolutional Neural Networks based on Quantum Vertex Saliency
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes.
Linear Differential Constraints for Photo-polarimetric Height Estimation
From a numerical point of view, we use a least-squares formulation of the discrete version of the problem.
Example-Based Modeling of Facial Texture From Deficient Data
We present an approach to modeling ear-to-ear, high-quality texture from one or more partial views of a face with possibly poor resolution and noise.
