Renormalized Mutual Information for Artificial Scientific Discovery

We derive a well-defined renormalized version of mutual information that allows us to estimate the dependence between continuous random variables in important case when one is deterministically dependent on other. This situation relevant for feature extraction, where goal produce low-dimensional effective description high-dimensional system. Our approach enables discovery collective physical systems, thus adding toolbox artificial scientific discovery, while also aiding analysis flow neural networks.