Results 51 to 60 of about 5,865 (183)
Noise-robust classification with hypergraph neural network
Indonesian Journal of Electrical Engineering and Computer Science, 2021 <p>This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets
Dang, Nguyen Trinh Vu +2 more
openaire +3 more sources
Multiview Hypergraph Fusion Network for Change Detection in High-Resolution Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingCurrently, convolutional neural networks and transformers have been the dominant paradigms for change detection (CD) thanks to their powerful local and global feature extraction capabilities. However, with the improvement of resolution, spatial, spectral,
Xue Zhao +5 more
doaj +1 more source
Link Prediction Method Based on Hypergraph Neural Network [PDF]
Jisuanji gongchengWith the rapid development of information technology, link prediction has been widely applied in various fields. Current link prediction methods are based on subgraph extraction.
CHEN Liang, ZHAO Ying, SHI Shenghui, YIN Ling
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Detecting Communities in Tripartite Hypergraphs
, 2010 In social tagging systems, also known as folksonomies, users collaboratively manage tags to annotate resources. Naturally, social tagging systems can be modeled as a tripartite hypergraph, where there are three different types of nodes, namely users ...
Liu, Xin, Murata, Tsuyoshi
core +1 more source
Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data‐ and Cognitive Scientists
WIREs Cognitive Science, Volume 17, Issue 2, March/April 2026.Cognitive network science helps organize associative knowledge—that is, the connections between concepts. These connections play a key role in cognitive processes such as language understanding and context interpretation, even though they are not obvious in language use.
Edith Haim, Massimo Stella
wiley +1 more source
Hypergraph Learning with Line Expansion
, 2020 Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Abdelzaher, Tarek +3 more
core
Predicting Multi-actor collaborations using Hypergraphs [PDF]
, 2014 Social networks are now ubiquitous and most of them contain interactions involving multiple actors (groups) like author collaborations, teams or emails in an organizations, etc.
Chandra, Abhishek +2 more
core
Efficient Parallel Translating Embedding For Knowledge Graphs
, 2018 Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the ...
Abadi Martín +7 more
core +1 more source
Lightweight Hybrid Wafer Defect Pattern Network Based on Feedforward Efficient Attention
CAAI Transactions on Intelligence Technology, Volume 11, Issue 1, Page 149-166, February 2026.ABSTRACT With the increase of semiconductor integration density, in order to cope with the increase of wafer defect complexity and types, especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects, this study proposes a lightweight model for wafer defect detection called LightWMNet.
Zhiqiang Hu, Yiquan Wu
wiley +1 more source
Hyper-Ordinal Pattern: Measuring High-Order Connection Relationship in Brain Disease Networks
IEEE Transactions on Neural Systems and Rehabilitation EngineeringBrain hyper-networks as a kind of hypergraph for brain network analysis, describing the high-order interactions among brain regions, have been extensively utilized in research on brain diseases such as mild cognitive impairment (MCI) and Alzheimer’
Tianyu Du +3 more
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