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 ...

A dynamic graph variational autoencoder (DGVAE) is developed to learn the generative spatiotemporal features of the dynamic graph. The DGVAE incorporates a new edge propagation graph convolutional ...

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...

This approach integrates fuzzy theory with autoencoder models to effectively manage noise and outliers in data. The FAE method introduces a feature selection layer that approximates discrete feature ...