… anomaly detection in IBD environments. In this article, a novel anomaly detection model based
on variational … neural network associated with a variational reparameterization scheme is …

… anomaly detection. We introduce a long short-term memory based variational autoencoder
(LSTM-VAE… Our LSTM-VAE-based detector reports an anomaly when a reconstruction-based …

… an anomaly detection mechanism that can tolerate a certain amount of anomalous samples.
2… models over clustering models in the context of anomaly detection for a sequential dataset. …

… the latent space at the anomaly detection stage, which brings new … variational autoencoder
generation adversarial networks (… In this paper, a LSTM-based VAE-GAN anomaly detection …

… In this paper, we propose a hybrid anomaly detection method that combines … variational
autoencoder (VAE) - with the temporal modelling ability of a long short-term memory RNN (LSTM)…

… Our popersed method is based on the architecture of the Variational AutoEncoder, and
uses LSTM to model the normal time series, so we briefly introduce VAE and LSTM in this part. …

… CONCLUSION AND FUTURE WORK In this paper, a long-short term memory based variational
autoencoder (LSTM-VAE) is proposed for anomaly detection for maritime systems. The …

… variational Bayes estimator. In particular, we study two decision criteria for anomaly detection…
Neural Network (RNN) as a backbone, represented by the sequence of RNN hidden states. …

… [19] applied a LSTM-based variational autoencoder to AD in robot assisted feeding data
and introduced a progress-based prior over the latent variables. Finally, Xu et al. [20] applied a …

… Then, we compare our model VAE-LSTM with 4 anomaly detection models with similar
architecture based on deep learning networN. We attempted to match the architectures and …