Uncertainty Quantification In Multivariable Regression For Material Property Prediction With Bayesian Neural Networks

This article discusses the problem of uncertainty quantification in multivariable regression tasks and provides an overview of conventional machine learning and deep learning frameworks for addressing this issue. It specifically focuses on methods that provide uncertainty quantification for predicted values, such as quantile regression and Bayesian neural networks.