A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…
An Introduction and exploration of Meta learning architecture specifically few-shot-learning. Our goal is to be able to classify new objects never seen it in the training data with very few examples.