Search Results for author:
Found 30 papers, 2 papers with code
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.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
To this end, we commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.
Multi-Task Learning with Multi-Task Optimization
Multi-task learning solves multiple correlated tasks.
A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration
The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
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
M$^3$SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration
A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research.
Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines
Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review.
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.
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.
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.
DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks
Our experimental results show that the mean relative error (MRE) is 0. 9% with respect to time and 2. 8% with respect to memory for 29 classic models, which is much lower than the state-of-the-art works.
Writing Style Aware Document-level Event Extraction
This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
Learning Conjoint Attentions for Graph Neural Nets
Given the novel Conjoint Attention strategies, we then propose Graph conjoint attention networks (CATs) that can learn representations embedded with significant latent features deemed by the Conjoint Attentions.
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.
Graph Joint Attention Networks
In this paper, we propose Graph Joint Attention Networks (JATs) to address the aforementioned challenge.
A Big Data Enabled Channel Model for 5G Wireless Communication Systems
This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling.
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.
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.
A Multi-Task Gradient Descent Method for Multi-Label Learning
Multi-label learning studies the problem where an instance is associated with a set of labels.
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.
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously.
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.
