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IEEE15d
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 ...
C&EN16d
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 ...
IEEE18d
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 ...
GitHub26d
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 ...
C&EN29d
Transformation-Free Generation of a Quasi-Diabatic Representation from the State-Average Orbital-Optimized Variational Quantum Eigensolver
IT4Innovations, VSB─Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic ...