Search Results - "Bayesian autoencoder"

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

“… In this work we investigate the use of Bayesian Autoencoder (BAE) models for uncertainty quantification in anomaly detection…”
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Explaining Probabilistic Bayesian Neural Networks for Cybersecurity Intrusion Detection by Yang, Tengfei, Qiao, Yuansong, Lee, Brian

“… For enhance the explainability of BNN model concerning uncertainty quantification, this paper proposes a Bayesian explanatory model that accounts for uncertainties inherent in Bayesian Autoencoder…”
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Least Square Variational Bayesian Autoencoder with Regularization by Ramachandra, Gautam

“…In recent years Variation Autoencoders have become one of the most popular unsupervised learning of complicated distributions.Variational Autoencoder (VAE)…”
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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 architectures…”
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Bayesian Autoencoders for Drift Detection in Industrial Environments by Yong, Bang Xiang, Fathy, Yasmin, Brintrup, Alexandra

“… To this end, we first propose the development of Bayesian Autoencoders to quantify epistemic and aleatoric uncertainties…”
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Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection by Yong, Bang Xiang, Brintrup, Alexandra

“… Therefore, in this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty, comprising epistemic and aleatoric uncertainties…”
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Revisiting Bayesian Autoencoders With MCMC by Chandra, Rohitash, Jain, Mahir, Maharana, Manavendra, Krivitsky, Pavel N.

“… This paper presents Bayesian autoencoders powered by MCMC sampling implemented using parallel computing and Langevin-gradient proposal distribution…”
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Two-step hybrid collaborative filtering using deep variational Bayesian autoencoders by Nahta, Ravi, Meena, Yogesh Kumar, Gopalani, Dinesh, Chauhan, Ganpat Singh

“… the latent vector representations when users or items are added to the underlying dataset. To address these issues, we propose a two-step hybrid variational Bayesian autoencoder to characterize the uncertainty of predicted ratings…”
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Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift by Yong, Bang Xiang, Brintrup, Alexandra

“…, the Bayesian autoencoder (BAE). These formulations contrast the conventional post-hoc explanation methods for AEs, which incur additional modelling effort and implementations…”
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Fast and precise single-cell data analysis using a hierarchical autoencoder by Tran, Duc, Nguyen, Hung, Tran, Bang, La Vecchia, Carlo, Luu, Hung N., Nguyen, Tin

“… The second module is a stacked Bayesian autoencoder that projects the data onto a low-dimensional space (compressed…”
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Revisiting Bayesian Autoencoders with MCMC by Chandra, Rohitash, Jain, Mahir, Maharana, Manavendra, Krivitsky, Pavel N

“… This paper presents Bayesian autoencoders powered by MCMC sampling implemented using parallel computing and Langevin-gradient proposal distribution…”
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Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes by Tran, Ba-Hien, Shahbaba, Babak, Mandt, Stephan, Filippone, Maurizio

“…We present a fully Bayesian autoencoder model that treats both local latent variables and global decoder parameters in a Bayesian fashion…”
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Bayesian Autoencoders for Drift Detection in Industrial Environments by Bang, Xiang Yong, Fathy, Yasmin, Brintrup, Alexandra

“… To this end, we first propose the development of Bayesian Autoencoders to quantify epistemic and aleatoric uncertainties…”
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Model Selection for Bayesian Autoencoders by Ba-Hien Tran, Rossi, Simone, Milios, Dimitrios, Michiardi, Pietro, Bonilla, Edwin V, Filippone, Maurizio

“…We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs…”
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Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes by Ba-Hien Tran, Shahbaba, Babak, Mandt, Stephan, Filippone, Maurizio

“… To address this issue, we propose a novel Sparse Gaussian Process Bayesian Autoencoder (SGPBAE) model in which we impose fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder…”
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Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection by Bang, Xiang Yong, Brintrup, Alexandra

“… Therefore, in this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty, comprising epistemic and aleatoric uncertainties…”
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Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning by Bang, Xiang Yong, Brintrup, Alexandra

“… of log-likelihood estimate, which naturally arise from the probabilistic formulation of the AE called Bayesian Autoencoders (BAE…”
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Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants by L Mars Gao, Kutz, J Nathan

“…Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under \(\ell_1\) constraints allows joint discoveries of governing…”
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Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection by Bang, Xiang Yong, Pearce, Tim, Brintrup, Alexandra

“…After an autoencoder (AE) has learnt to reconstruct one dataset, it might be expected that the likelihood on an out-of-distribution (OOD) input would be low…”
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