ABSTRACT: Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships, controlling redundant information, and improving ...

2.1 Improvement of the objective function of the VAE network Inspired ... At the same time, the loss function of the variational autoencoder (VAE) is improved. By adding a hyperparameter β to the ...

of differently parameterized electrical machine topologies at the same time by mapping a high-dimensional integrated design parameters in a lower-dimensional latent space using a variational ...

To address this issue, we propose an importance-weighted sampling enhanced Variational Autoencoder (VAE ... propose a new training strategy for VAE by enhancing it with the objective function of IWAE.

A variational autoencoder (VAE) is a deep neural system that can be ... file of UCI digits data into memory as a two-dimensional array using the NumPy loadtxt() function. The pixel values are ...

The demo sets up training parameters for the batch size (10), number of epochs to train (100), loss function (mean squared ... efforts to complement an autoencoder with an advanced type of autoencoder ...