Results 91 to 100 of about 5,998 (189)

Provable Bounds for Learning Some Deep Representations

, 2013
We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an $n$ node multilayer neural net that has degree at most $n^{\gamma}$ for some $\gamma
Arora, Sanjeev, Bhaskara, Aditya, Ge, Rong, Ma, Tengyu   +3 more
core  

STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis

Aerospace
Accurate fault diagnosis in aerospace transmission systems is essential for ensuring equipment reliability and operational safety, especially for aero-engine bearings.
Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai   +4 more
doaj   +1 more source

Generalization Performance of Hypergraph Neural Networks

Proceedings of the ACM on Web Conference 2025
Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us.
Yifan Wang, Gonzalo R. Arce, Guangmo Tong   +2 more
openaire   +2 more sources

Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention

Applied Sciences
Hypergraph analysis extends traditional graph theory by enabling the study of complex, many-to-many relationships in networks, offering powerful tools for understanding brain connectivity.
Alexander N. Pisarchik, Natalia Peña Serrano, Walter Escalante Puente de la Vega, Rider Jaimes-Reátegui   +3 more
doaj   +1 more source

Multivariate Time Series Anomaly Detection Using Directed Hypergraph Neural Networks

Applied Artificial Intelligence
Multivariate time series anomaly detection is a challenging problem because there can be a number of complex relationships between variables in multivariate time series. Although graph neural networks have been shown to be effective in capturing variable-
Tae Wook Ha, Myoung Ho Kim
doaj   +1 more source

Distributed constrained combinatorial optimization leveraging hypergraph neural networks

Nature Machine Intelligence
Scalable addressing of high dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel application of graph neural networks for solving quadratic-cost combinatorial optimization problems. However, effective utilization of models such as graph neural
Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar   +4 more
openaire   +2 more sources

Dual convolutional network based on hypergraph and multilevel feature fusion for road extraction from high-resolution remote sensing images

International Journal of Digital Earth
Road extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based
BoWen Li, XiangHong Tang, Rang Xiao, JianGuang Lu, YuHao Wang   +4 more
doaj   +1 more source

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

IET Image Processing
Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify.
Junze Wang, Wenjun Zhang, Dandan Li, Chao Li, Weipeng Jing   +4 more
doaj   +1 more source

Hyperedge Interaction-aware Hypergraph Neural Network

Hypergraphs provide an effective modeling approach for modeling high-order relationships in many real-world datasets. To capture such complex relationships, several hypergraph neural networks have been proposed for learning hypergraph structure, which propagate information from nodes to hyperedges and then from hyperedges back to nodes.
Ye, Rongping, Pei, Xiaobing, Yang, Haoran, Wang, Ruiqi   +3 more
openaire   +2 more sources

ERP Insights and Truncated SVD in Conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis

International Journal of Computational Intelligence Systems
Multi-modal EEG data analysis requires sophisticated methods for accurate prediction in the critical area of cognitive depression study in neuroscience.
N. Banupriya, Neelakandan Subramani, M. Prakash, S. Velmurugan   +3 more
doaj   +1 more source