Introduction to Uncertainty Quantification for Deep Learning

A quick 20 min introduction to various UQ methods for Deep Learning:- - Why is UQ required for Deep Learning - Bayesian NN - Monte Carlo Dropout - MCMC - Variational Inference - Laplace Approximation - Deep Ensembles - Deep Evidence Regression Link to slides: https://github.com/ashish1610dhiman/UQ_deep_learning/blob/main/iuq_paper_ppt_ashish_dhiman.pdf Please let me know if I have some errors :) *sources cited at the end of slides