4 citations · 14 across the 9 of their papers we have counts for
3 papers · 1 filter
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
Meet P. Vadera, Adam D. Cobb, Brian Jalaian +1
While deep learning methods continue to improve in predictive accuracy on a wide range of application domains, significant issues remain with other aspects of their performance inc…
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin
In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior w…
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
Meet P. Vadera, Satya Narayan Shukla, Brian Jalaian +1
In this paper, we consider the problem of assessing the adversarial robustness of deep neural network models under both Markov chain Monte Carlo (MCMC) and Bayesian Dark Knowledge…