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Graph Attention

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Showing 451–500 of 1088 papers

TitleStatusHype
Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object DetectionCode1
MobileViG: Graph-Based Sparse Attention for Mobile Vision ApplicationsCode1
Sequential Attention Source Identification Based on Feature Representation—0
Enhancing Dialogue Generation via Dynamic Graph Knowledge AggregationCode1
Higher-order Graph Attention Network for Stock Selection with Joint Analysis—0
SENSEi: Input-Sensitive Compilation for Accelerating GNNs—0
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers—0
Task-Driven Graph Attention for Hierarchical Relational Object Navigation—0
Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Explainable and Position-Aware Learning in Digital Pathology—0
NFTs to MARS: Multi-Attention Recommender System for NFTs—0
Inferring dynamic regulatory interaction graphs from time series data with perturbations—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Origin-Destination Network Generation via Gravity-Guided GAN—0
Towards Deep Attention in Graph Neural Networks: Problems and RemediesCode1
JGAT: a joint spatio-temporal graph attention model for brain decodingCode0
GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network—0
HetReGAT-FC: heterogeneous residual graph attention network via feature completionCode0
Semantic tradeoff for heterogeneous graph embedding—0
4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks—0
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving—0
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design—0
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution—0
GVdoc: Graph-based Visual Document ClassificationCode0
Demystifying Oversmoothing in Attention-Based Graph Neural Networks—0
NAP: Neural 3D Articulation PriorCode1
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive ApplicationsCode0
RE^2: Region-Aware Relation Extraction from Visually Rich DocumentsCode0
GATology for Linguistics: What Syntactic Dependencies It Knows—0
Syntactic Knowledge via Graph Attention with BERT in Machine Translation—0
Distributed Learning over Networks with Graph-Attention-Based PersonalizationCode0
Enhancing Short-Term Wind Speed Forecasting using Graph Attention and Frequency-Enhanced Mechanisms—0
Fisher Information Embedding for Node and Graph LearningCode1
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks—0
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge AggregationCode0
Temporal and Heterogeneous Graph Neural Network for Financial Time Series PredictionCode1
Attention Based Feature Fusion For Multi-Agent Collaborative Perception—0
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-AttentionCode0
A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill RecognitionCode0
SMART: A Decision-Making Framework with Multi-modality Fusion for Autonomous Driving Based on Reinforcement LearningCode1
Bi-Level Attention Graph Neural NetworksCode1
Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention NetworksCode1
Gradient Derivation for Learnable Parameters in Graph Attention Networks—0
TC-GAT: Graph Attention Network for Temporal Causality Discovery—0
Graph Neural Network-Based Anomaly Detection for River Network SystemsCode1
Investigating Graph Structure Information for Entity Alignment with Dangling Cases—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection—0
LMExplainer: Grounding Knowledge and Explaining Language Models—0
GRAF: Graph Attention-aware Fusion NetworksCode1
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