IEEE

Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering

Abstract: In recent years, graph-based deep learning algorithms have attracted widespread attention in the field of consumer electronics. Still, most of the current graph neural networks are based on ...
IEEE

scDVAE:Single-Cell Data Clustering Based on Variational Autoencoder with Disentangled Latent Representations

Abstract: Single-cell RNA sequencing (scRNA-seq) technology enables the analysis of gene expression in individual cells, allowing for a deeper exploration of heterogeneity in organisms and complex ...
GitHub

Logan-Locke/galaxy-denoising-reconstructing-clustering

This project uses the Galaxy Zoo: Hubble (or GZ:H) dataset to de-noise, reconstruct, and cluster images of galaxies using their visual features via an autoencoder and contrastive learning. The dataset ...
International Monetary Fund

UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the ...
GitHub

improve-the-accurancy-of-telecom-passenger-flow-prediction

Telecommunication data is a valuable resource for understanding pedestrian mobility patterns. However, predicting pedestrian flow with high accuracy is challenging due to noise, high dimensionality, ...