Masked Bayesian neural networks: Theoretical guarantee and its posterior inference
Bayesian approaches for learning deep neural networks (BNN) have been received much
attention and successfully applied to various applications. Particularly, BNNs have the merit …
attention and successfully applied to various applications. Particularly, BNNs have the merit …
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
In this paper, we approach the problem of uncertainty quantification in deep learning
through a predictive framework, which captures uncertainty in model parameters by …
through a predictive framework, which captures uncertainty in model parameters by …
Optimizing a deep learning framework for accurate detection of the Earth's ionospheric plasma structures from all-sky airglow images
The ionosphere, part of the Earth's atmosphere, where different plasma
irregularities/structures are generated through various electrodynamical processes. Airglow …
irregularities/structures are generated through various electrodynamical processes. Airglow …
A Graybox Defense Through Bootstrap** Deep Neural Network
K Sullivan - 2022 - search.proquest.com
Building a robust deep neural network (DNN) framework turns out to be a very difficult task
as adaptive attacks are developed that break a robust DNN strategy. In this work we first …
as adaptive attacks are developed that break a robust DNN strategy. In this work we first …