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Found 18 papers, 3 papers with code
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world.
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning
The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing.
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
In this work, we exploit the concept of unlearnable examples to make images unusable to model training by generating and adding unlearnable noise into the original images.
HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
We synthesize new nodes based on samples from minority classes and their neighbors.
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness.
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks
Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks.
GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems
Existing research studies have been focusing on novel encoding and decoding structures via various neural network models to enhance the node embedding representation.
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs
Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow.
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Sleep stage classification is crucial for detecting patients' health conditions.
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data.
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and clustering.
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields.
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees
Specifically, we collect a sheer number of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated.
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification and recommendations tasks.
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL.
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning.
