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 ...

How does a Variational Autoencoder work? First, an encoder network turns the input samples x into two parameters in a latent space, which we will note z_mean and z_log_sigma . Then, we randomly sample ...

This code is associated with the following paper: Xiaopeng Li and James She. Relational Variational Autoencoder for Link Prediction with Multimedia Data. ACM International Conference on Multimedia ...

Defining a Variational Autoencoder The demo code that defines a VAE that corresponds Figure 2 is presented in Listing 2. The __init__() method defines the five neural network layers used by the system ...