论文标题
动手贝叶斯神经网络 - 深度学习用户的教程
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users
论文作者
论文摘要
现代深度学习方法构成了难以置信的强大工具，可以解决无数具有挑战性的问题。但是，由于深度学习方法作为黑匣子运行，因此与其预测相关的不确定性通常具有挑战性。贝叶斯统计提供了一种形式主义，以了解和量化与深神经网络预测相关的不确定性。本教程概述了相关文献和设计，实施，训练，使用和评估贝叶斯神经网络的完整工具集，即使用贝叶斯方法训练的随机人工神经网络。
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.
