Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

A fairly common sub-problem in many machine learning and data science scenarios is the need to compute the similarity (or difference or distance) between two datasets. For example, if you select a ...

Detecting changes in asset co-movements is of great importance to financial practitioners, with numerous risk management benefits arising from the timely detection of breakdowns in historical ...