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 ...

The efficacy of the proposed VAE-BiLSTM method is evaluated using python programming and tensorflow library on the data traces taken from the Ausgrid solar generation dataset. Moreover, a comparative ...

Variational Autoencoders (VAE) on MNIST By stuyai, taught and made by Otzar Jaffe This project demonstrates the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras on the ...

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.

Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data which consist of an encoder and decoder.

The Variational Autoencoder model is a neural network that provides collaborative filtering based on implicit feedback, specifically, it provides product recommendations based on user and item ...