Zobrazeno 1 - 10
of 794
pro vyhledávání: '"Graph attention networks"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract As one of the most important senses in human beings, touch can also help robots better perceive and adapt to complex environmental information, improving their autonomous decision-making and execution capabilities. Compared to other percepti
Externí odkaz:
https://doaj.org/article/a5112a2a9b12423cb6bf5048faf0fe6c
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Aspect-based sentiment analysis (ABSA) is a challenging task due to the presence of multiple aspect words with different sentiment polarities in a sentence. Recently, pre-trained language models like BERT have been widely used as context enc
Externí odkaz:
https://doaj.org/article/5d074dce7029422697431680bcd46843
Publikováno v:
Heliyon, Vol 10, Iss 16, Pp e35938- (2024)
In previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted the interconnections among nodes within the graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements that
Externí odkaz:
https://doaj.org/article/b6d14e9dd9f5419b8dc59525a5e2be6a
Publikováno v:
Frontiers in Microbiology, Vol 15 (2024)
IntroductionAccumulating evidence shows that human health and disease are closely related to the microbes in the human body.MethodsIn this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE
Externí odkaz:
https://doaj.org/article/9a9828a7feb240f9a18a21e0e0608281
Publikováno v:
PeerJ Computer Science, Vol 10, p e2200 (2024)
The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to
Externí odkaz:
https://doaj.org/article/754288c9bff74b6b870065462b53e927
Publikováno v:
Electronic Research Archive, Vol 32, Iss 4, Pp 2310-2322 (2024)
Graph convolution networks (GCN) have demonstrated success in learning graph structures; however, they are limited in inductive tasks. Graph attention networks (GAT) were proposed to address the limitations of GCN and have shown high performance in g
Externí odkaz:
https://doaj.org/article/5332fa983831429cbab5e0dff4d21a33
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-16 (2024)
Abstract Background In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experime
Externí odkaz:
https://doaj.org/article/133c0ab8619d40e8b88eaebcea780ded
Publikováno v:
IEEE Access, Vol 12, Pp 179119-179129 (2024)
This study pioneers the application of Graph Attention Networks (GAT) and Graph Neural Networks (GNN), to the emulation of MEMS silicon beams under external loadings, representing a significant advancement in MEMS simulation. The novel augmented grap
Externí odkaz:
https://doaj.org/article/e401c92d9d1d4c82b3c84e30c2a61a74
Publikováno v:
IEEE Access, Vol 12, Pp 166923-166935 (2024)
In this paper, a Mahalanobis Distance-based Graph Attention Network for graph classification, is proposed. In contrast to traditional Graph Attention Networks, the proposed approach learns the covariances between node features so as to determine the
Externí odkaz:
https://doaj.org/article/59dbef8321d74b2f8542b0e578925078
Publikováno v:
IEEE Open Journal of the Computer Society, Vol 5, Pp 684-693 (2024)
Despite the recent progress in emotion recognition, state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this article we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-langu
Externí odkaz:
https://doaj.org/article/42bd9d1f55eb489b89c3b5b09c73388a