Metadata-Version: 2.1
Name: sgpvae
Version: 0.1
Summary: Sparse Gaussian process variational autoencoders
Home-page: https://github.com/MattAshman/sgpvae
Author: Matthew Ashman
Author-email: mca39@cam.ac.uk
License: UNKNOWN
Description: # Sparse Gaussian Process Variational Autoencoders
        
        This repository contains the Python implementation of the SGP-VAE, introduced in our [paper](https://openreview.net/forum?id=czv8Ac3Kg7l).
        
        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 <mca39@cam.ac.uk>.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >3.6
Description-Content-Type: text/markdown
