Recent works on Bayesian neural networks (BNNs) have highlighted the nee...
The "cold posterior effect" (CPE) in Bayesian deep learning describes th...
The existence of adversarial examples poses a real danger when deep neur...
Variational Bayesian Inference is a popular methodology for approximatin...
During the past five years the Bayesian deep learning community has deve...
Ensembles of models have been empirically shown to improve predictive
pe...
We establish a theoretical link between adversarial training and operato...
We investigate conditions under which test statistics exist that can rel...
We propose a novel data-dependent structured gradient regularizer to inc...
Deep generative models based on Generative Adversarial Networks (GANs) h...