Interpretable Compositional Networks (ICN), a variant of neural networks, that allows the user to get interpretable results, unlike regular artificial neural networks. An ICN is a glass-box producing functions composition that scale with the size of the input, allowing a learning phase on relatively small spaces.
This presentation covers the different Julia packages and paradigms involved, a set of use-case, current limitations, future developments, and hopefully possible collaborations.