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• W2779573676 abstract "Long Short-Term Memory networks trained with gradient descent and back-propagation have received great success in various applications. However, point estimation of the weights of the networks is prone to over-fitting problems and lacks important uncertainty information associated with the estimation. However, exact Bayesian neural network methods are intractable and non-applicable for real-world applications. In this study, we propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights. Furthermore, we optimize the covariance of the noise distribution in the ensemble update step using maximum likelihood estimation. To assess the proposed algorithm, we apply it to outlier detection in five real-world events retrieved from the Twitter platform." @default.
• W2779573676 created "2018-01-05" @default.
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• W2779573676 date "2017-12-23" @default.
• W2779573676 modified "2023-09-25" @default.
• W2779573676 title "An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection" @default.
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• W2779573676 doi "https://doi.org/10.48550/arxiv.1712.08773" @default.
• W2779573676 hasPublicationYear "2017" @default.
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