Results 21 to 30 of about 106,924 (261)
Phrase-level attention network for few-shot inverse relation classification in knowledge graph
, 2023 Relation classification aims to recognize semantic relation between two given entities mentioned in the given text. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops ...
Chunliu Dou +15 more
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Edge-Featured Graph Attention Network
CoRR, 2021 Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models.
Jun Chen, Haopeng Chen
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, 2021 Distant supervised relation extraction which is to extract heterogeneous relations from text data without manual annotation has been widely used in decision-making tasks such as question answering or recommendation system.
Li, Xuewei +5 more
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Dual-Attention Graph Convolutional Network [PDF]
, 2020 Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity.
Xueya Zhang +4 more
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Signed Graph Attention Networks [PDF]
, 2019 Accepted and to appear at ...
Junjie Huang +3 more
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Personalized Pagerank Graph Attention Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Published as a conference paper at ICASSP ...
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Graph Attention Networks with Positional Embeddings [PDF]
, 2021 Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighboring nodes having similar features and labels--, and therefore may not be at their full potential when dealing with non-homophilic graphs.
Liheng Ma +2 more
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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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, 2022 Graph neural networks (GNNs) can be effectively applied to solve many real-world problems across widely diverse fields. Their success is inseparable from the message-passing mechanisms evolving over the years.
Shan Ai +15 more
core +1 more source
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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