Encoding the Latent Posterior of Bayesian Neural Networks for Uncertainty Quantification
Encoding the Latent Posterior of Bayesian Neural Networks for Uncertainty Quantification
Bayesian Neural Networks (BNNs) have long been considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. While they could capture more accurately the posterior distribution of the network parameters, most BNN approaches are either limited to small networks or rely on …