We propose a novel theoretical framework, DOI, for divergence-based Out-of-Distribution indicators (instead of traditional likelihood-based) in deep generative models.
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters when compared to their dense counterparts.
Using powerful posterior distributions is a popular technique in variational inference.
Using powerful posterior distributions is a popular approach to achieving better variational inference.
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.