  We review recent developments of machine learning algorithms pertinent to the
inverse renormalization group, which was originally established as a generative
numerical method by Ron-Swendsen-Brandt via the implementation of compatible
Monte Carlo simulations. Inverse renormalization group methods enable the
iterative generation of configurations for increasing lattice size without the
critical slowing down effect. We discuss the construction of inverse
renormalization group transformations with the use of convolutional neural
networks and present applications in models of statistical mechanics, lattice
field theory, and disordered systems. We highlight the case of the
three-dimensional Edwards-Anderson spin glass, where the inverse
renormalization group can be employed to construct configurations for lattice
volumes that have not yet been accessed by dedicated supercomputers.
