research
∙
02/08/2022
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
Bayesian methods hold significant promise for improving the uncertainty ...
share
research
∙
12/03/2021
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems
Approximate Bayesian deep learning methods hold significant promise for ...
research
∙
06/13/2021
Post-hoc loss-calibration for Bayesian neural networks
Bayesian decision theory provides an elegant framework for acting optima...
research
∙
07/08/2020
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
While deep learning methods continue to improve in predictive accuracy o...
research
∙
05/16/2020
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
In this paper, we present a general framework for distilling expectation...
research
∙
02/07/2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
In this paper, we consider the problem of assessing the adversarial robu...
research
∙
06/04/2019
