Výsledky vyhledávání - Graph convolutional and variational autoencoders

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

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

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/

Design of an Improved Model for Blockchain Forensics Using Graph Convolutional Networks and Variational Autoencoders

Autoři: Sweta A. Bokade V.K. Sharma Bhushan M. Manjre

Zdroj: 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). :1-6

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: 0202 electrical engineering, electronic engineering, information engineering, Variational graph autoencoders, graph representation learning, graph convolutional networks, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, multi-order neighborhood, high-order information, TK1-9971

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

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

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

RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders

Autoři: Yifei Wang Pengju Ding Congjing Wang a další

Zdroj: IEEE Transactions on Neural Networks and Learning Systems. 36:7681-7695

Témata: RNA, Humans, RNA-Binding Proteins, Computational Biology, Proteins, Neural Networks, Computer, Autoencoder, Algorithms

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38709606

Enhanced Variational Graph Convolutional Networks with Multi-Scale Convolutions and Attention Mechanisms for Dynamic Network Analysis

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

Zdroj: Engineering, Technology & Applied Science Research. 15:19838-19847

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

Autoři: Hui Cui Toshiya Nakaguchi Ping Xuan a další

Zdroj: Briefings in Bioinformatics. 23

Témata: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Proteins, Drug Interactions, Neural Networks, Computer

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/34718408
https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab453/40907009/bbab453.pdf
https://pubmed.ncbi.nlm.nih.gov/34718408/

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

Physics-Guided Variational Graph Autoencoder For Air Quality Inference

Autoři: Rodrigo Bonet, Esther Deligiannis, Nikos FORMER_Multidimensional signal processing and communication a další

Přispěvatelé: Rodrigo Bonet, Esther Deligiannis, Nikos FORMER_Multidimensional signal processing and communication a další

Zdroj: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6940-6944

Přístupová URL adresa: https://biblio.vub.ac.be/vubir/physicsguided-variational-graph-autoencoder-for-air-quality-inference(e357924a-16e4-42b3-a400-06c1d5eb545f).html

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

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

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

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.

Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

Autoři: Jieyu Zhang Carl Yang Jiawei Han a další

Zdroj: Proceedings of the 13th International Conference on Web Search and Data Mining. :699-707

Témata: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Social and Information Networks, 02 engineering and technology, Machine Learning (cs.LG)

Přístupová URL adresa: https://dl.acm.org/doi/pdf/10.1145/3336191.3371829
http://arxiv.org/abs/1911.05465

Dirichlet Graph Variational Autoencoder

Autoři: Li, Jia Yu, Tomasyu Li, Jiajin a další

Témata: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2010.04408

DiffuScope: A diffusion-regularized autoencoder for spatial transcriptomic clustering.

Autoři: Shi, Hua Yi, Ding Cui, Yang a další

Zdroj: Comput Biol Chem ; ISSN:1476-928X ; Volume:120 ; Issue:Pt 2

Témata: Breast cancer, Diffusion Consistency Loss, Gastric cancer, Graph Convolutional Variational Autoencoders, Spatial transcriptomics

Relation: https://doi.org/10.1016/j.compbiolchem.2025.108746; https://pubmed.ncbi.nlm.nih.gov/41167001

Dostupnost: https://doi.org/10.1016/j.compbiolchem.2025.108746
https://pubmed.ncbi.nlm.nih.gov/41167001

Recommender systems based on variational autoencoders and graph convolutional neural networks

Autoři: Kang, Peng

Thesis Advisors: Xue Liu (Internal/Supervisor)

Témata: Computer Science

Relation: Pid: 161242

Dostupnost: https://escholarship.mcgill.ca/concern/theses/g445cg72w