Adversarial learning based node-edge graph attention networks for autism spectrum disorder identification
Graph neural networks (GNNs) have received increasing interest in the medical imaging
field given their powerful graph embedding ability to characterize the non-Euclidean …
field given their powerful graph embedding ability to characterize the non-Euclidean …
Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes
By representing the embedded components and their interactions in industrial systems as
nodes and edges in a graph, Graph Neural Networks (GNNs) have achieved outstanding …
nodes and edges in a graph, Graph Neural Networks (GNNs) have achieved outstanding …
Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge graph reasoning
Abstract Knowledge graph (KG) is a multi-relational data that has proven valuable for many
tasks including decision making and semantic search. In this paper, we present GTGAT …
tasks including decision making and semantic search. In this paper, we present GTGAT …
Deep learning framework for geological symbol detection on geological maps
Dynamic legend generation for geological maps aims to detect and identify geological map
symbols within the current viewshed and generate a corresponding real-time legend to help …
symbols within the current viewshed and generate a corresponding real-time legend to help …
Transformative Movie Discovery: Large Language Models for Recommendation and Genre Prediction
In the era of digital streaming platforms, personalized movie recommendations, and genre
prediction have become pivotal for enhancing user engagement and satisfaction. With the …
prediction have become pivotal for enhancing user engagement and satisfaction. With the …
[PDF][PDF] Model-based Sparse Communication in Multi-agent Reinforcement Learning
Learning to communicate efficiently is central to multi-agent reinforcement learning (MARL).
Existing methods often require agents to exchange messages intensively, which abuses …
Existing methods often require agents to exchange messages intensively, which abuses …
Forecasting interaction order on temporal graphs
Link prediction is a fundamental task for graph analysis and the topic has been studied
extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a …
extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a …
VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification
The increasing reliance of software projects on third-party libraries has raised concerns
about the security of these libraries due to hidden vulnerabilities. Managing these vul …
about the security of these libraries due to hidden vulnerabilities. Managing these vul …
Mtmd: multi-scale temporal memory learning and efficient debiasing framework for stock trend forecasting
Recently, machine learning methods have shown the prospects of stock trend forecasting.
However, the volatile and dynamic nature of the stock market makes it difficult to directly …
However, the volatile and dynamic nature of the stock market makes it difficult to directly …
Improving stock trend prediction with multi-granularity denoising contrastive learning
Stock trend prediction (STP) aims to predict the price fluctuation, which is critical in financial
trading. The existing STP approaches only use market data with the same granularity (such …
trading. The existing STP approaches only use market data with the same granularity (such …