2000 character limit reached
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models
Published 1 Apr 2019 in stat.ML and cs.LG | (1904.00548v1)
Abstract: A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual attributes. The method can be trained with data sets that contain anomalies without any special pre-processing.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.