An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection
This work addresses uncertainty estimation for LSTM networks in outlier detection, but it is incremental as it adapts existing methods to a specific domain.
The authors tackled the problem of uncertainty estimation in Long Short-Term Memory networks by proposing an approximate Bayesian method using the Ensemble Kalman Filter, which they applied to outlier detection on Twitter data across five real-world events.
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.