Natural Parameter Networks are a sampling/variational inference free way for Bayesian Deep Learning, based on the paper [here]{https://arxiv.org/pdf/1611.00448.pdf}.
This repository implements the Gaussian variant of NPN for classification and extends this idea to recurrent architectures.
To run MNIST example
python main-mnist.py
To run LM example
python main-lm.py
You also need to download WikiText 2 data and put it in data/ folder. There should be 3 files train.txt, test.txt, valid.txt.
This has been done for course research project for 10-708 Probablistic Graphical Models at Carnegie Mellon University.
- Gaussian NPN for regression
- Implement other variants of NPN (Gamma NPN, Poisson NPN)