A Variational Autoencoder (VAE) is a generative model that combines principles from deep learning and probabilistic modeling. This project trains a VAE to generate Fashion MNIST images and visualize ...

An Autoencoder is a type of neural network that learns to compress data (a process called encoding) into a lower-dimensional representation and then reconstruct it back to its original form (a process ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...

Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, current VAE-based SE ...

This paper addresses the challenge of blind non-linear equalization using a variational autoencoder (VAE) with a second-order Volterra channel model. The VAE framework’s costfunction, the evidence ...

The VAE s are trained on a very large and diverse dataset that includes multiple decades of historical time series across many currencies. This allows the autoencoder market models for interest rates ...