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 Autoencoder is a specific type of Autoencoder. In which, the hidden representation (encoded vector) is forced to be a Normal distribution. As the result, by randomly sampling a vector in ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...

We propose a variational autoencoder which encodes and decodes directly to and from these parse trees, ensuring the generated outputs are always valid. Surprisingly, we show that not only does our ...

Learn how to select the optimal hyperparameters for variational autoencoders (VAEs), such as the latent dimension, the learning rate, the beta coefficient, and the network architecture.

A method for explaining a deep learning model prediction is proposed. It uses a combination of the standard autoencoder and the variational autoencoder. The standard autoencoder is exploited to ...