JavierAntoran/Bayesian-Neural-Networks

#artificialintelligence 

The project is written in python 2.7 and Pytorch 1.0.1. If CUDA is available, it will be used automatically. The models can also run on CPU as they are not excessively big. We carried out homoscedastic and heteroscedastic regression experiements on toy datasets, generated with (Gaussian Process ground truth), as well as on real data (six UCI datasets). The heteroscedastic notebooks contain both toy and UCI dataset experiments for a given (ModelName).