Results 151 to 160 of about 1,157 (178)

Hypergraph analysis of neural networks

Physica D: Nonlinear Phenomena, 1989
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Jeffries, Clark, van den Driessche, P.
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Hypergraph Neural Networks for Hypergraph Matching

2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021
Xiaowei Liao, Yong Xu 0007, Haibin Ling
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HGNNv2: Stable Hypergraph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypergraph neural networks (HGNNs) are widely used models for analyzing higher-order relational data. HGNNs suffer from the rapid performance degradation with increasing layers. Hypergraph dynamic system (HDS) is a potential way to deal with this challenge.
Yue Gao 0002, Jielong Yan, Yifan Feng 0001, Xiangmin Han, Shihui Ying, Zongze Wu 0001, Han Hu 0003   +6 more
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Hypergraphs and Neural Networks

1991
It is certainly desirable to have mathematically rigorous knowledge of the attractors of neural network models. In fact, for those models to be used in content addressable memory (static memories) or robot control, one usually seeks some assurance that the only attractors are constant trajectories built into the model and in particular that no limit ...
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Knowledge-Embedded Hypergraph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypergraph Neural Networks (HGNNs) enhance graph-based modeling by representing complex relationships, with applications in brain network analysis, recommendation systems, and computer vision. However, conventional HGNNs often struggle with effective knowledge extraction and discriminative feature representation, leading to performance limitations ...
Yifan Feng, Yifan Zhang, Shaoyi Du, Shihui Ying, Zongze Wu, Yue Gao   +5 more
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Hypergraph Neural Network Hawkes Process

2022 International Joint Conference on Neural Networks (IJCNN), 2022
Zi-Hao Cheng, Jian-Wei Liu 0006, Ze Cao
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Hypergraph Neural Network for Emotion Recognition in Conversations

ACM Transactions on Asian and Low-Resource Language Information Processing
Modeling conversational context is an essential step for emotion recognition in conversations. Existing works still suffer from insufficient utilization of local context information and remote context information. This article designs a hypergraph neural network, namely HNN-ERC, to better utilize local and remote contextual information.
Cheng Zheng 0006, Haojie Xu, Xiao Sun 0003   +2 more
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Deep Hypergraph Neural Networks with Tight Framelets

Proceedings of the AAAI Conference on Artificial Intelligence
Hypergraphs provide a flexible framework for modeling high-order (complex) interactions among multiple entities, extending beyond traditional pairwise correlations in graph structures. However, deep hypergraph neural networks (HGNNs) often face the challenge of oversmoothing with increasing depth, similar to issues in graph neural networks (GNNs ...
Ming Li 0065, Yujie Fang, Yi Wang 0022, Han Feng, Yongchun Gu, Lu Bai 0001, Pietro Liò   +6 more
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HGNN+: General Hypergraph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Rongrong Ji, Yue Gao, Yifan Feng
exaly  

Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system

Measurement: Journal of the International Measurement Confederation, 2022
Hongkun Li, Zhang Kongliang
exaly