Suchergebnisse - "Graph Convolutional Variational Autoencoders"

Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

Autoren: Mishra, Aditya Samin, Ahnaf Mozib Etemad, Ali et al.

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

Schlagwörter: 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)

Zugangs-URL: http://arxiv.org/abs/2501.16626

Scalable Graph Convolutional Variational Autoencoders

Autoren: Unyi, Dániel Gyires-Tóth, Bálint

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

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

Dateibeschreibung: text

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

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

Autoren: Shi, Hua Yi, Ding Cui, Yang et al.

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

Schlagwörter: 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

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

New Cancer Research Data Have Been Reported by Researchers at University of Tokyo (DiffuScope: A diffusion-regularized autoencoder for spatial transcriptomic clustering).

Quelle: Clinical Oncology Week; 11/11/2025, p775-775, 1p

Schlagwörter: CANCER research, CLUSTERING algorithms, GENE expression profiling, SUPERVISED learning, BREAST cancer, STOMACH cancer

Firma/Körperschaft: TOKYO Daigaku

Hybrid Deep Learning for Fault Diagnosis in Photovoltaic Systems.

Autoren: Bougoffa, Mouaad Benmoussa, Samir Djeziri, Mohand et al.

Quelle: Machines; May2025, Vol. 13 Issue 5, p378, 18p

Schlagwörter: CLEAN energy, SUSTAINABILITY, PHOTOVOLTAIC power systems, ENERGY infrastructure, AUTOENCODERS, DEEP learning