This repository contains the code for the paper Implicit Weight Uncertainty in Neural Networks (arXiv).
We interpret HyperNetworks within the framework of variational inference within implicit distributions. Our method, Bayes by Hypernet, is able to model a richer variational distribution than previous methods. Experiments show that it achieves comparable predictive performance on the MNIST classification task while providing higher predictive uncertainties compared to MC-Dropout and regular maximum likelihood training.
Following libraries were used for development:
future==0.16.0
jupyter==1.0.0
matplotlib==1.5.3
notebook==4.2.3
numpy==1.13.3
pandas==0.19.2
scipy==0.19.1
seaborn==0.7.1
tensorflow-gpu==1.4.0rc0
tqdm==4.11.2
The notebooks contain the code for the two experiments. toy_dataset.ipynb
contains the code for the toy regression. MNIST.ipynb
contains the code for the MNIST digit classification.
For discussion, suggestions or questions don't hesitate to contact [email protected] .