Results 141 to 150 of about 5,865 (183)

Cross-Scale Hypergraph Neural Networks with Inter-Intra Constraints for Mitosis Detection. [PDF]

Sensors (Basel)
Li J, Dong D, Zhan Y, Zhu G, Zhang H, Xie X, Yang L.   +6 more
europepmc   +1 more source

DHCLHAM: microbe-drug interaction prediction based on dual-hypergraph contrastive learning framework with hierarchical attention mechanism. [PDF]

Front Microbiol
Hu H, Nie C.
europepmc   +1 more source

ML-driven latency optimization for mobile edge computing in fiber-wireless access networks. [PDF]

MethodsX
Marmat A, Thankachan D.
europepmc   +1 more source

Dual chain dynamic hypergraph convolution network for 3D human pose estimation. [PDF]

Sci Rep
Han Q, Zhang S, Wang P.
europepmc   +1 more source

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Hypergraph Neural Network for Skeleton-Based Action Recognition

IEEE Transactions on Image Processing, 2021
Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent results. However, the existing methods only focus on the local physical connection between the joints, and
Xiaoke Hao, Jie Li, Yingchun Guo, Tao Jiang, Ming Yu   +4 more
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Mode Hypergraph Neural Network

IEEE Transactions on Neural Networks and Learning Systems
The hypergraph neural network (HGNN) is an emerging powerful tool for modeling and learning complex, high-order correlations among entities upon hypergraph structures. While existing HGNN-based approaches excel in modeling high-order correlations among data using hyperedges, they often have difficulties in distinguishing diverse semantics ( e.g ...
Shuyi Ji, Yifan Feng, Donglin Di, Shihui Ying, Yue Gao   +4 more
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Hypergraph Transformer Neural Networks

ACM Transactions on Knowledge Discovery from Data, 2023
Graph neural networks (GNNs) have been widely used for graph structure learning and achieved excellent performance in tasks such as node classification and link prediction. Real-world graph networks imply complex and various semantic information and are often referred to as heterogeneous information networks (HINs).
Mengran Li, Yong Zhang, Xiaoyong Li, Yuchen Zhang, Baocai Yin   +4 more
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Hypergraph Structure Learning for Hypergraph Neural Networks

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
Hypergraphs are natural and expressive modeling tools to encode high-order relationships among entities. Several variations of Hypergraph Neural Networks (HGNNs) are proposed to learn the node representations and complex relationships in the hypergraphs. Most current approaches assume that the input hypergraph structure accurately depicts the relations
Derun Cai, Moxian Song, Chenxi Sun, Baofeng Zhang, Shenda Hong, Hongyan Li   +5 more
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