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Found 7 papers, 1 papers with code
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
In this paper, we investigate the potential of sparse network structures to flexibly trade-off model storage costs and inference run time against predictive performance and uncertainty quantification ability.
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems
Approximate Bayesian deep learning methods hold significant promise for addressing several issues that occur when deploying deep learning components in intelligent systems, including mitigating the occurrence of over-confident errors and providing enhanced robustness to out of distribution examples.
Post-hoc loss-calibration for Bayesian neural networks
Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available.
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
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 work on the Bayesian Dark Knowledge framework.
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 robustness of deep neural network models under both Markov chain Monte Carlo (MCMC) and Bayesian Dark Knowledge (BDK) inference approximations.
Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty
Bayesian Dark Knowledge is a method for compressing the posterior predictive distribution of a neural network model into a more compact form.
