Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"Graph Attention Network"”.
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Accelerating graph attention network inference on CPUs with layer fusion
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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From GNNs to sparse transformers: graph-based architectures for multi-hop question answering
… Sparse Transformers [7] have surpassed Graph Neural Networks (GNNs) as the state-of-the-art architecture for MHQA. Noting that the Transformer [4] is a particular message passing GNN, in this work we perform an architectural analysis and evaluation to investigate why the Transformer …
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Learning NP-hard problems on networks using Geometric Deep Learning
… from a wide range of domains can be modeled as networks, i.e., a set of nodes that represent the entities of the system and a set of links between nodes that represent relations among them. For instance, social networks can be used to describe how people interact with each other, computer …
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Graph neural network approaches and real-time unsupervised learning for anomaly detection in vehicular networks
… or impaired, remaining leading causes. Vehicular networks, as a core component of intelligent transportation systems (ITS), enable real-time vehicle–infrastructure communication through Cooperative Awareness Messages (CAMs), offering opportunities to detect anomalies in both driving behavior and …
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Network-aware Multi-agent Reinforcement Learning for Adaptive Navigation of Vehicles in a Dynamic Road Network
Traffic congestion in urban road networks is a condition characterized by slower speeds, longer trip times, increased air pollution, and driver frustration. Traffic congestion can be attributed to a volume of traffic that generates demand for space greater than the available street capacity. A …
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GNN-Enhanced Hierarchical Federated Learning in Device-to-Device Networks
… of intelligent devices and advanced wireless networks has resulted in an explosive growth of data generated at the network edge, creating new opportunities for data-driven services while posing fundamental challenges in privacy preservation, communication efficiency, and adaptability. …
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Graph Neural Networks for Multi-Robot Coordination
… in investigating machine learning (especially graph neural network) based approaches to find the trade-off between optimality and complexity by offloading online computation into an offline training process. Yet, learning-based methods also yield the need for sim-to-real systems and solutions …
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Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications
… within NLP applications. By leveraging tree- and graph-based neural networks, this study pioneers a holistic approach that augments language understanding and processing capabilities. Through the fusion of structural and semantic-driven insights, this work tries to explore various NLP applications …