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Graph Attention

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TitleStatusHype
Multimodal Reasoning with Multimodal Knowledge Graph—0
Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network—0
Multi-perspective Memory Enhanced Network for Identifying Key Nodes in Social Networks—0
Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)—0
Multiresolution Graph Attention Networks for Relevance Matching—0
Multi-scale speaker embedding-based graph attention networks for speaker diarisation—0
Multi-Sense Language Modelling—0
Multi-SIGATnet: A multimodal schizophrenia MRI classification algorithm using sparse interaction mechanisms and graph attention networks—0
Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs—0
Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction—0
Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models—0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
NABU - Multilingual Graph-based Neural RDF Verbalizer—0
Neighbor Overlay-Induced Graph Attention Network—0
Neighbour Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification—0
Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks—0
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation—0
NetSight: Graph Attention Based Traffic Forecasting in Computer Networks—0
Network Representation Learning for Biophysical Neural Network Analysis—0
Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach—0
Neural Stochastic Block Model & Scalable Community-Based Graph Learning—0
Neuroplastic graph attention networks for nuclei segmentation in histopathology images—0
NFTs to MARS: Multi-Attention Recommender System for NFTs—0
NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation—0
NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text Classification—0
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