Results 11 to 20 of about 9,987 (195)

On multistage learning a hidden hypergraph [PDF]

2016 IEEE International Symposium on Information Theory (ISIT), 2016
5 pages, IEEE ...
D'yachkov, A. G., Vorobyev, I. V., Polyanskii, N. A., Shchukin, V. Yu.   +3 more
openaire   +3 more sources

Learning Hypergraph-regularized Attribute Predictors [PDF]

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
This is an attribute learning paper accepted by CVPR ...
Huang, Sheng, Elhoseiny, Mohamed, Elgammal, Ahmed, Yang, Dan   +3 more
openaire   +3 more sources

Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. [PDF]

Adv Sci (Weinh), 2023
Jiang Y, Wang R, Feng J, Jin J, Liang S, Li Z, Yu Y, Ma A, Su R, Zou Q, Ma Q, Wei L.   +11 more
europepmc   +2 more sources

Hypergraph learning for identification of COVID-19 with CT imaging. [PDF]

Med Image Anal, 2021
Di D, Shi F, Yan F, Xia L, Mo Z, Ding Z, Shan F, Song B, Li S, Wei Y, Shao Y, Han M, Gao Y, Sui H, Gao Y, Shen D.   +15 more
europepmc   +2 more sources

Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data. [PDF]

IEEE Trans Med Imaging, 2020
Xiao L, Wang J, Kassani PH, Zhang Y, Bai Y, Stephen JM, Wilson TW, Calhoun VD, Wang YP.   +8 more
europepmc   +2 more sources

Prediction of Graduation Development Based on Hypergraph Contrastive Learning With Imbalanced Sampling

IEEE Access, 2023
With the increasingly competitive job market, the employment issue for college graduates has received more and more attention. Predicting graduation development can help students understand their suitable graduation development, thus easing the pressure ...
Yong Ouyang, Tuo Feng, Rong Gao, Yubin Xu, Jinghang Liu   +4 more
doaj   +1 more source

Multi-order hypergraph convolutional networks integrated with self-supervised learning

Complex & Intelligent Systems, 2023
Hypergraphs, as a powerful representation of information, effectively and naturally depict complex and non-pair-wise relationships in the real world. Hypergraph representation learning is useful for exploring complex relationships implicit in hypergraphs.
Jiahao Huang, Fangyuan Lei, Jianjian Jiang, Xi Zeng, Ruijun Ma, Qingyun Dai   +5 more
doaj   +1 more source

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Data Science and Engineering, 2023
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li, Man-Sheng Chen, Yuefang Gao, Chang-Dong Wang   +3 more
doaj   +1 more source

Semisupervised Hypergraph Discriminant Learning for Dimensionality Reduction of Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples.
Fulin Luo, Tan Guo, Zhiping Lin, Jinchang Ren, Xiaocheng Zhou   +4 more
doaj   +1 more source

Dynamic Hypergraph Structure Learning [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
In recent years, hypergraph modeling has shown its superiority on correlation formulation among samples and has wide applications in classification, retrieval, and other tasks. In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure.
Zizhao Zhang, Haojie Lin, Yue Gao
openaire   +1 more source