Deep Classifiers with Label Noise Modeling and Distance Awareness

no code implementations • 6 Oct 2021 • Vincent Fortuin, Mark Collier, Florian Wenzel, James Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications.

Out-of-Distribution Detection

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference

no code implementations • pproximateinference AABI Symposium 2021 • Erik Daxberger, Eric Nalisnick, James Allingham, Javier Antoran, José Miguel Hernández-Lobato

In particular, we develop a practical and scalable Bayesian deep learning method that first trains a point estimate, and then infers a full covariance Gaussian posterior approximation over a subnetwork.

Bayesian Inference