TAGI_HS_Inference_JMLR

This repository contains the code used to produce the results for the paper: Nguyen & Goulet (2022) Analytically Tractable Hidden-States Inference in Bayesian Neural Networks. Journal of Machine Learning Research

Go see our YouTube channel where you can find more information.

Reference

Tractable Approximate Gaussian Inference for Bayesian Neural Networks
Nguyen, L.H., and Goulet, J.-A.
Journal of Machine Learning Research, 2022, 21-0758, Volume 23, pp. 1-33. , [JMLR]

Installation

These instructions will get you a copy of the project up and running on your local machine for direct use, testing and development purposes.

Prerequisites

Matlab (version 2016a or higher) installed on Mac OSX or Windows

The Matlab Statistics and Machine Learning Toolbox is required.

Installing

1. Extract the ZIP file (or clone the git repository) in a folder you will be working from.
2. Add the TAGI_HS_Inference_JMLR/ folder and all the sub folders to your path in Matlab : e.g.
   • using the "Set Path" dialog in Matlab, or
   • by running the addpath function from the Matlab command window while adding all sub-folders

Built With

• Matlab - Coding

Authors

• Luong Ha Nguyen - Main Development - webpage
• James A-Goulet - webpage

Acknowledgments

The developpement of this code was financially supported by research grants from Hydro-Quebec, and the Natural Sciences and Engineering Research Council of Canada (NSERC)