Suchergebnisse - "Bayesian autoencoder"

Towards trustworthy cybersecurity operations using Bayesian Deep Learning to improve uncertainty quantification of anomaly detection von 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 …”
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Explaining Probabilistic Bayesian Neural Networks for Cybersecurity Intrusion Detection von 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 …”
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Variational Bayesian Autoencoder for Channel Compression and Feedback in Massive MIMO Systems von 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 …”
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Energy-Aware Anomaly Detection in Wind Turbine SCADA Systems von 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 …”
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Bayesian Autoencoders for Drift Detection in Industrial Environments von 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 …”
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Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection von 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, …”
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Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift von 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 …”
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