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 ...
IEEE

Convolutional Autoencoder Based Feature Extraction and Clustering for Customer Load Analysis

Abstract: As the number of smart meters increases, compression of metering data becomes essential for data transmission, storing and processing perspectives. Specifically, feature extraction can be ...
Frontiers

Latent Profiling of Five-Dimensional Psychological Resilience Across Generations: A Deep Clustering and Behavioral Divergence Analysis in Pre-Conflict Iran

The final, formatted version of the article will be published soon. Psychological resilience is increasingly conceptualized as a multidimensional construct encompassing identity, emotional, cognitive, ...