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 ...

Jang et al. [36] introduced a Convolutional Variational Autoencoder (CVAE) that models the variability in ECG patterns through learned latent distributions, facilitating clustering and anomaly ...

Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder-decoder ...

The traditional masked reconstruction model uses the autoencoder structure but cannot learn richer potential information and data structure. To this end, we propose to improve the latent space based ...

We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

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

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...