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Graph Attention Networks for Speaker Verification [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 5 pages, 1 figure, 2 tables, accepted for presentation at ICASSP 2021 as a conference ...
Jee-weon Jung +3 more
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SEA: Graph Shell Attention in Graph Neural Networks
, 2023 A common issue in Graph Neural Networks (GNNs) is known as over-smoothing. By increasing the number of iterations within the message-passing of GNNs, the nodes' representations of the input graph align with each other and become indiscernible. Recently, it has been shown that increasing a model's complexity by integrating an attention mechanism yields ...
Christian M. M. Frey +2 more
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PointGAT: Graph attention networks for 3D object detection
Intelligent and Converged Networks, 2022 3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective
Haoran Zhou +3 more
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Relational Graph Attention Networks
CoRR, 2019 We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established benchmarks.
Dan Busbridge +3 more
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Predicting Propositional Satisfiability Based on Graph Attention Networks
International Journal of Computational Intelligence Systems, 2022 Boolean satisfiability problems (SAT) have very rich generic and domain-specific structures. How to capture these structural features in the embedding space and feed them to deep learning models is an important factor influencing the use of neural ...
Wenjing Chang, Hengkai Zhang, Junwei Luo
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Graph Attention Networks With Local Structure Awareness for Knowledge Graph Completion
IEEE Access, 2020 Graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion.
Kexi Ji, Bei Hui, Guangchun Luo
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Spectral signatures of reorganised brain networks in disorders of consciousness [PDF]
, 2014 Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to
Valdas Noreika (645566) +65 more
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Graph Convolutional Networks and Attention-Based Outlier Detection
IEEE Access, 2022 Outlier detection is a significant research direction in machine learning and has many applications in finance, network security, and other areas. Outlier detection of Euclidean datasets is a mainstream problem in outlier detection.
Rui Qiu +4 more
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, 2022 Dynamic graph neural networks (DGNNs) have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss, or ...
Liu, Chao +15 more
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Applied Sciences, 2021 In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. The skeleton data have been proved to be robust to the complex background, illumination changes, and ...
Chengming Liu +5 more
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