Výsledky vyhledávání - Graph convolutional and variational autoencoder*

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Autoři: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Zdroj: Reliability Engineering and System Safety. 267

Témata: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Přístupová URL adresa: https://research.chalmers.se/publication/549377

Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

Autoři: Mishra, Aditya Samin, Ahnaf Mozib Etemad, Ali a další

Zdroj: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Témata: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/abs/2501.16626

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Autoři: Murshedul Arifeen Andrei Petrovski Md Junayed Hasan a další

Zdroj: Machines, Vol 12, Iss 12, p 894 (2024)

Témata: solar array, photovoltaic array, fault detection, fault diagnosis, graph convolutional network, variational autoencoder, Mechanical engineering and machinery, TJ1-1570

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2075-1702/12/12/894; https://doaj.org/toc/2075-1702

Přístupová URL adresa: https://doaj.org/article/a68e9bb8f0bd42d6b8c9842c6321237e

Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting

Autoři: Liang, Guojun, Postdoktor Tiwari, Prayag Nowaczyk, Sławomir a další

Zdroj: IEEE Transactions on Neural Networks and Learning Systems. 33(5):9524-9537

Témata: Diffusion process, graph neural networks (GNNs), spatiotemporal forecasting, variational graph autoencoders (VGAEs)

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-55718
https://doi.org/10.1109/tnnls.2024.3415149

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data

Autoři: Seung-Hwan Choi Dawn An Inho Lee a další

Zdroj: Mathematics, Vol 12, Iss 23, p 3750 (2024)

Témata: anomaly detection, graph convolutional network (GCN), variational autoencoder (VAE), statistical feature, probability-based anomaly score, Mathematics, QA1-939

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/23/3750; https://doaj.org/toc/2227-7390

Přístupová URL adresa: https://doaj.org/article/c1d52f19b28848b1becfec82e190f2fb

A Guassian Mixture Variational Graph Autoencoder for Node Classification

Autoři: Mohadeseh Ghayekhlou Ahmad Nikabadi

Zdroj: AUT Journal of Modeling and Simulation, Vol 56, Iss 1, Pp 103-116 (2024)

Témata: graph convolutional networks (gcns), variational graph autoencoders (vgaes), gaussian mixture model (gmm), Engineering (General). Civil engineering (General), TA1-2040, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: https://miscj.aut.ac.ir/article_5523_0524f123e5f48ab1ce264793e2624b5a.pdf; https://doaj.org/toc/2588-2953; https://doaj.org/toc/2588-2961

Přístupová URL adresa: https://doaj.org/article/e693f22aa6c74c72968952bf1df4181f

Scalable Graph Convolutional Variational Autoencoders

Autoři: Unyi, Dániel Gyires-Tóth, Bálint

Zdroj: 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI). :467-472

Témata: QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0503 education

Popis souboru: text

Přístupová URL adresa: https://dblp.uni-trier.de/db/conf/saci/saci2021.html#UnyiG21
https://ieeexplore.ieee.org/document/9465579/

Improving Variational Graph Autoencoders With Multi-Order Graph Convolutions

Autoři: Lining Yuan Ping Jiang Zhu Wen a další

Zdroj: IEEE Access, Vol 12, Pp 46919-46929 (2024)

Témata: Variational graph autoencoders, graph convolutional networks, multi-order neighborhood, high-order information, graph representation learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10477408/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/025d2e8d9cb746fd8a85da1c8047095c

Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest

Autoři: Bo Wang Fangjian Ma Xiaoxin Du a další

Zdroj: Frontiers in Microbiology, Vol 15 (2024)

Témata: microbe–drug association prediction, variational autoencoder, modified graph convolutional neural network, graph attention network, random forest classifier, computational prediction model, Microbiology, QR1-502

Popis souboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fmicb.2024.1394302/full; https://doaj.org/toc/1664-302X

Přístupová URL adresa: https://doaj.org/article/5e963e31a3a540a0a7d0d21975537bff

Synergistic Integration of Image-Text Modalities in Telemedicine: VAE-Infused Graph Convolutional Networks for Diagnostic Decision-Making

Autoři: Yirui Kuang Hui Jin Jiayu Yang a další

Zdroj: IEEE Access, Vol 13, Pp 63093-63107 (2025)

Témata: Telemedicine, multimodal fusion, graph neural networks, variational autoencoders, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10955270/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/9c3b4c47a53c4b2a87210659c816f623

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

Autoři: Jawahar, Pratik Aarrestad, Thea Chernyavskaya, Nadezda a další

Témata: Data Management and Data Science, Information and Computing Sciences, Information Systems, anomaly detection, variational auto encoder, normalizing flow, Large Hadron Collider, new physics beyond standard model, graph convolutional network, convolutional neural net, Data management and data science, Information systems

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/9d87s2q3
https://escholarship.org/content/qt9d87s2q3/qt9d87s2q3.pdf

Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks

Autoři: Zhenguo Xu Jun Li Irene Moulitsas a další

Zdroj: Big Data and Cognitive Computing, Vol 9, Iss 4, p 101 (2025)

Témata: key node identification, community detection, link prediction, small-world network, graph attention networks, variational graph autoencoder, Technology

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2504-2289/9/4/101; https://doaj.org/toc/2504-2289

Přístupová URL adresa: https://doaj.org/article/e8e61df9dc424a059558d2f5b7f862d0

Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

Autoři: Zhang Kun Li Hongren Wang Xin a další

Zdroj: IEEE Access, Vol 12, Pp 104818-104832 (2024)

Témata: Gas turbine, fault diagnosis, MG-VAE, noise robustness, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10613765/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/c986c7c9e0e34b09bbf948eb168f8205

GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data

Autoři: Zeyu Fu Chunlin Chen Song Wang a další

Zdroj: BMC Genomics, Vol 26, Iss 1, Pp 1-29 (2025)

Témata: Graph attention networks, Neural ordinary differential equation, Variational autoencoders, Clustering, ScRNA-seq, ScATAC-seq, Biotechnology, TP248.13-248.65, Genetics, QH426-470

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1471-2164

Přístupová URL adresa: https://doaj.org/article/e02e430dd2cc4cf5a385ba4b16829290

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

Autoři: Aabid A. Mir Megat F. Zuhairi Shahrulniza Musa a další

Zdroj: IEEE Access, Vol 12, Pp 161697-161717 (2024)

Témata: Anomaly detection, complex networks, deep learning, graph neural networks, heterogeneous networks, network security, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10723293/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/205888aa585544ec8d0e7f0c49e14058

Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion

Autoři: Bourached, Anthony Gray, Robert Guan, Xiaodong a další

Témata: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Mathematics - Probability, text

URL: http://arxiv.org/abs/2111.12602

scVGAE: A Novel Approach using ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation

Autoři: Inoue, Yoshitaka

Témata: Quantitative Biology - Genomics, Computer Science - Computational Engineering, Finance, and Science, text

URL: http://arxiv.org/abs/2403.08959

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction.

Autoři: Xuan, Ping Fan, Mengsi Cui, Hui a další

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

Témata: PROTEIN-protein interactions, CONVOLUTIONAL neural networks, PROTEIN drugs

VGE: Gene-Disease Association by Variational Graph Embedding

Autoři: Peng Han Xiangliang Zhang

Zdroj: International Journal of Crowd Science, Vol 8, Iss 2, Pp 95-99 (2024)

Témata: disease-gene association, graph convolutional network (gcn), variational antoencoder (vae), Technology, Engineering (General). Civil engineering (General), TA1-2040

Popis souboru: electronic resource

Relation: https://www.sciopen.com/article/10.26599/IJCS.2024.9100004; https://doaj.org/toc/2398-7294

Přístupová URL adresa: https://doaj.org/article/433f523c1b194c51bb094eaeb00d9154

Graph variational autoencoder with affinity propagation for community-aware anomaly detection in attributed networks.

Autoři: Cao, Zhijie Yang, Chengkun Fan, Xiaoqing a další

Zdroj: Applied Soft Computing. Jan2026:Part D, Vol. 186, pN.PAG-N.PAG. 1p.

Témata: Anomaly detection (Computer security), Clustering algorithms, Representations of graphs, Computer networks, Principal components analysis