[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Learning with Importance Weighted Variational Inference: Asymptotics for Gradient Estimators of the VR-IWAE Bound
Several popular variational bounds involving importance weighting ideas have been
proposed to generalize and improve on the Evidence Lower BOund (ELBO) in the context of …
proposed to generalize and improve on the Evidence Lower BOund (ELBO) in the context of …
Neural operator variational inference based on regularized stein discrepancy for deep gaussian processes
Deep Gaussian process (DGP) models offer a powerful nonparametric approach for
Bayesian inference, but exact inference is typically intractable, motivating the use of various …
Bayesian inference, but exact inference is typically intractable, motivating the use of various …