Search Results - "bayesian autoencoder"

Towards trustworthy cybersecurity operations using Bayesian Deep Learning to improve uncertainty quantification of anomaly detection by Yang, Tengfei, Qiao, Yuansong, Lee, Brian

“…Uncertainty quantification of cybersecurity anomaly detection results provides critical guidance for decision makers on whether or not to accept the results…”
Get full text

Explaining Probabilistic Bayesian Neural Networks for Cybersecurity Intrusion Detection by Yang, Tengfei, Qiao, Yuansong, Lee, Brian

“…The probabilistic Bayesian neural network(BNN) is good at providing trustworthy outcomes that is important, e.g. in intrusion detection. Due to the complex of…”
Get full text

Variational Bayesian Autoencoder for Channel Compression and Feedback in Massive MIMO Systems by Zheng, Xuanyu, Bi, Yuanyuan, Guo, Huayan, Lau, Vincent

“…In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive…”
Get full text

Energy-Aware Anomaly Detection in Wind Turbine SCADA Systems by Ciocan, Angela Voinea, Hamrioui, Sofiane, Lorenz, Pascal, Courboulay, Vincent, Ciocan, Adrian

“…This paper presents a hybrid AI-based framework for robust anomaly detection in wind turbine SCADA systems, combining Bayesian Autoencoders with Transformer…”
Get full text

Bayesian Autoencoders for Drift Detection in Industrial Environments by Yong, Bang Xiang, Fathy, Yasmin, Brintrup, Alexandra

“…Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive…”
Get full text

Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection by Yong, Bang Xiang, Brintrup, Alexandra

“…Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection, AEs still lack a way to express uncertainty in their predictions,…”
Get full text

Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift by Yong, Bang Xiang, Brintrup, Alexandra

“…This paper aims to improve the explainability of autoencoder (AE) predictions by proposing two novel explanation methods based on the mean and epistemic…”
Get full text