News

IEEE16d
BiAtt-GVAE: Molecular design for specific target via graph variational autoencoder based on bi-channel interactive attention network
The interactive attention block operates on the protein-ligand graph, facilitating the interactive learning of ligand atoms and protein amino acid features. We consider various properties to learn the ...
IEEE19d
An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing
This article introduces the autoencoder graph ensemble model (AEGEM), a novel ensemble-based framework designed to enhance performance in both endmember extraction and abundance estimation. In the ...
GitHub27d
variational-autoencoder
This repository contains numerous applications of autoencoder neural networks. Projects include image denoising, detection of infected cells, and processing of the MNIST dataset. Each application ...
GitHub28d
A PyTorch implementation of a Variational Autoencoder trained on the MNIST dataset. The project demonstrates digit generation from random latent vectors and reconstruction of ...
This project implements a Variational Autoencoder (VAE) in PyTorch, trained on the MNIST dataset. It features a Gradio web app for generating new digits and reconstructing uploaded digit images.
C&EN17d
scGANSL: Graph Attention Network with Subspace Learning for scRNA-seq Data Clustering
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c00731. Comparison of model complexity between scGANSL and other ...
Frontiers25d
Erratum: Graph-informed convolutional autoencoder to classify brain responses during sleep
Due to a production error, there was an error regarding the affiliation for Somayeh Makouei. Instead of having affiliation 2, they should have affiliation 1 ...