Results 141 to 150 of about 1,673 (179)

Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC. [PDF]

PLOS Digit Health
Song J, Nie Q, Wang S, An T, Li X, Liu Y, Wang H, Shi R, Wang L, Lin M, Pan X, Li X, Hou Q, Xu N, Guo X, Mao Y, Liu B, Qu X, Liu JH, Zhong WZ.   +19 more
europepmc   +1 more source

HYG-mol: An Interpretable Multimodal Hypergraph Framework for Molecular Property Prediction. [PDF]

Comput Struct Biotechnol J
Ma J, Yang Q, Zhang L, Liu H, Zheng Y.
europepmc   +1 more source

A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction. [PDF]

Commun Biol
Zhao Y, Chen Y, Yu Y, Liu X, Du J, Wen J, Sun Q, Wang R, Chen C.   +8 more
europepmc   +1 more source

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Hypergraph Learning: Methods and Practices

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance ...
Zizhao Zhang, Changqing Zou, Yue Gao
exaly   +3 more sources

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 0002   +5 more
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On the effect of hyperedge weights on hypergraph learning [PDF]

Image and Vision Computing, 2017
Hypergraph is a powerful representation in several computer vision, machine learning and pattern recognition problems. In the last decade, many researchers have been keen to develop different hypergraph models. In contrast, no much attention has been paid to the design of hyperedge weights.
Sheng Huang, Ahmed Elgammal
exaly   +3 more sources

Image Segmentation as Learning on Hypergraphs

2008 Seventh International Conference on Machine Learning and Applications, 2008
In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as the objects and background. Using the seed pixels, our method predicts the labels for all unlabeled pixels.
Lei Ding 0002, Alper Yilmaz 0001
openaire   +1 more source