2018
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
ICML 2018oral
Bayesian neural networks with latent variables are scalable and flexible probabilistic models: they account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data. Using these models we show how to perform and u…