Before revision:  In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) . The model learns latent representations for all sequences in the dataset and detects anomalies that deviate from the overall distribution. Moreover, the model can estimate an underlying normal progression for each given sequence represented as occurrence probabilities of events along the sequence progression.
After revision:  In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression.