Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

TL;DR

This tutorial introduces Bayesian Neural Networks, providing deep learning practitioners with essential tools and methods to incorporate uncertainty quantification into neural network models, enhancing their reliability and interpretability.

Contribution

It offers a comprehensive overview and practical guide for designing, implementing, and evaluating Bayesian Neural Networks, bridging the gap between Bayesian statistics and deep learning.

Findings

Provides a complete toolset for Bayesian Neural Networks

Demonstrates how to quantify uncertainty in deep learning models

Synthesizes relevant literature and practical techniques

Abstract

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions. This tutorial provides an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian Neural Networks, i.e. Stochastic Artificial Neural Networks trained using Bayesian methods.