The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data. However, ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation.

This repository contains a Jupyter notebook implementing a Vanilla Variational Autoencoder (VAE) for image generation. The VAE is a powerful generative model that learns to encode images into a latent ...

Variational Autoencoder in tensorflow and pytorch Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more ...

This paper innovatively proposes a temporal–spatial pyramid variational autoencoder (TS-PVAE) model for the nonlinear temporal–spatial feature pyramid extraction from multirate data. This structure ...