sgpvae 0.1

Sparse Gaussian process variational autoencoders

Sparse Gaussian Process Variational Autoencoders

This repository contains the Python implementation of the SGP-VAE, introduced in our paper.

The main components of the repository are:

• sgpvae: the implementation of the SGP-VAE and partial inference networks.
• experiments: code for running the experiments detailed in the paper.
• data: code for installing the datasets used in the experiments.

Dependencies

This code is implemented in Python 3.8.

Contact

Please do feel free to use/extend this code for your own research. Indeed, the models in sgpvae are implemented with versatility in mind, so should be easily applied to a wide range of datasets. If you have any questions, or would like to report any issues, please open an issue on the issues tracker or contact me at [e-mail].