Suchergebnisse - "Graph convolutional autoencoders"

Graph Representation Learning Based on Multi-Channel Graph Convolutional Autoencoders

Autoren: YUAN Lining, HU Hao, LIU Zhao

Quelle: Jisuanji gongcheng, Vol 49, Iss 2, Pp 150-160,174 (2023)

Schlagwörter: graph representation learning, graph convolution network(gcn), autoencoder, node classification, node clustering, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20230219.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/f93170416c2c48b9bc4218a82c001ee4

3D Face Reprentation and Reconstruction with Multi-scale Graph Convolutional Autoencoders

Autoren: Cunkuan Yuan Yu-Kun Lai Kun Li et al.

Quelle: 2019 IEEE International Conference on Multimedia and Expo (ICME). :1558-1563

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/icmcs/icme2019.html#YuanLLLY19
https://orca.cardiff.ac.uk/121528/
https://ieeexplore.ieee.org/document/8784723/

Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods

Autoren: Michael Blumenstein Jing Jiang Yu Zheng et al.

Quelle: 2019 International Joint Conference on Neural Networks (IJCNN). :1-8

Deformable Shape Completion with Graph Convolutional Autoencoders

Autoren: Or Litany Ameesh Makadia Alexander M. Bronstein et al.

Quelle: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. :1886-1895

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://arxiv.org/pdf/1712.00268
http://arxiv.org/abs/1712.00268
https://ai.google/research/pubs/pub46516
https://openaccess.thecvf.com/content_cvpr_2018/papers/Litany_Deformable_Shape_Completion_CVPR_2018_paper.pdf
https://openaccess.thecvf.com/content_cvpr_2018/html/Litany_Deformable_Shape_Completion_CVPR_2018_paper.html
https://dblp.uni-trier.de/db/conf/cvpr/cvpr2018.html#LitanyBBM18
https://arxiv.org/abs/1712.00268
https://research.google.com/pubs/pub46516.html

Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series

Autoren: Eric Stefan Miele Fabrizio Bonacina Alessandro Corsini

Quelle: Energy and AI, Vol 8, Iss , Pp 100145- (2022)

Schlagwörter: Wind turbine, Condition monitoring, Deep anomaly detection, SCADA data, Graph Convolutional Autoencoder, Multivariate Time series, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666546822000076; https://doaj.org/toc/2666-5468

Zugangs-URL: https://doaj.org/article/24c38fb3bba94c47b4048e1cf1c518b2

Graph convolutional autoencoders with co-learning of graph structure and node attributes

Autoren: Kaixuan Yao Jiye Liang Jie Wang et al.

Quelle: Pattern Recognition. 121:108215

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/journals/pr/pr121.html#WangLYLW22
https://www.sciencedirect.com/science/article/pii/S0031320321003964

Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods

Autoren: Di Wu, MB

Index Begriffe: Conference Proceeding

URL: http://hdl.handle.net/10453/141217
The 2019 International Joint Conference on Neural Networks (IJCNN 2019)
2019 International Joint Conference on Neural Networks (IJCNN)
10.1109/IJCNN.2019.8852314

Deformable Shape Completion with Graph Convolutional Autoencoders

Autoren: Litany, Or Bronstein, Alex Bronstein, Michael et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/1712.00268

Graph Representation Learning Using Second-Order Graph Convolutional Autoencoders. (English)

Autoren: YUAN Lining JIANG Ping MO Jiaying et al.

Quelle: Journal of Computer Engineering & Applications; 5/15/2024, Vol. 60 Issue 10, p180-187, 8p

Schlagwörter: REPRESENTATIONS of graphs, DISTRIBUTION (Probability theory), TOPOLOGY

Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods

Autoren: Di Wu, MB

Dateibeschreibung: application/pdf

Relation: The 2019 International Joint Conference on Neural Networks (IJCNN 2019); 2019 International Joint Conference on Neural Networks (IJCNN); The 2019 International Joint Conference on Neural Networks (IJCNN 2019), 2019, 2019-July, pp. 1-8; http://hdl.handle.net/10453/141217

Verfügbarkeit: http://hdl.handle.net/10453/141217

3D face representation and reconstruction with multi-scale graph convolutional autoencoders

Autoren: Yuan, Cunkuan Li, Kun Lai, Yukun et al.

Quelle: IIEEE International Conference on Multimedia and Expo (ICME) 2019

Dateibeschreibung: application/pdf

Zugangs-URL: https://orca.cardiff.ac.uk/id/eprint/121528/1/3DFaceConvICME19.pdf

3D face representation and reconstruction with multi-scale graph convolutional autoencoders

Autoren: Yuan, Cunkuan Li, Kun Lai, Yukun et al.

Index Begriffe: Conference or Workshop Item, PeerReviewed

URL: https://orca.cardiff.ac.uk/121528/
https://orca.cardiff.ac.uk/121528

3D face representation and reconstruction with multi-scale graph convolutional autoencoders

Autoren: Yuan, Cunkuan Li, Kun Lai, Yukun et al.

Index Begriffe: Conference or Workshop Item, PeerReviewed

URL: https://orca.cardiff.ac.uk/id/eprint/121528/
https://orca.cardiff.ac.uk/id/eprint/121528

3D face representation and reconstruction with multi-scale graph convolutional autoencoders

Autoren: Yuan, Cunkuan Li, Kun Lai, Yukun et al.

Index Begriffe: Conference or Workshop Item, PeerReviewed

URL: https://orca.cardiff.ac.uk/id/eprint/121528/
https://orca.cardiff.ac.uk/id/eprint/121528

Multi-Slice Spatial Transcriptomics Data Integration Analysis with STG3Net

Autoren: Fang, Donghai Zhu, Fangfang Min, Wenwen

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2408.15246

Heterogeneous multi-scale neighbor topologies enhanced drug–disease association prediction.

Autoren: Xuan, Ping Meng, Xiangfeng Gao, Ling et al.

Quelle: Briefings in Bioinformatics; May2022, Vol. 23 Issue 3, p1-13, 13p

Schlagwörter: CONVOLUTIONAL neural networks, DRUG efficacy, TOPOLOGY

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction.

Autoren: Hu, Kaimiao Cui, Hui Zhang, Tiangang et al.

Quelle: Briefings in Bioinformatics; Mar2022, Vol. 23 Issue 2, p1-13, 13p

Schlagwörter: PROTEIN drugs, MODULAR coordination (Architecture), TOPOLOGY, LEARNING modules, DRUG development, CHARTS, diagrams, etc.

Prediction of drug–disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets.

Autoren: Gao, Ling Cui, Hui Zhang, Tiangang et al.

Quelle: Briefings in Bioinformatics; Jan2022, Vol. 23 Issue 1, p1-12, 12p

Schlagwörter: DRUG repositioning, DRUG development

Adversarial learning network for multi-source change detection in built-up areas: integrating VHR RGB and multispectral imagery.

Autoren: Oubara, Amel Wu, Falin Qu, Guoxin et al.

Quelle: International Journal of Digital Earth; Dec2025, Vol. 18 Issue 1, p1-25, 25p

Schlagwörter: MULTISPECTRAL imaging, REMOTE sensing, CITIES & towns, MACHINE learning, CHANGE-point problems, DEEP learning

A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder.

Autoren: Ni, Yanchun Jin, Qiyuan Hu, Rui

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6724, 27p

Schlagwörter: AUTOENCODERS, STRUCTURAL health monitoring, ONLINE monitoring systems, VIBRATION (Mechanics), SPATIOTEMPORAL processes, GRAPH neural networks, MACHINE learning, DEEP learning