Improving KAN with CDF normalization to quantiles

In ML there is usually used normalization by subtracting the mean and dividing by standard deviation - I haven't seen by CDF in ML (?, they are popular in finance for copulas: https://en.wikipedia.org/wiki/Copula_(statistics) ), which provides more uniform distributions, allowing for better description with smaller models, what seems beneficial for generalization (e.g. description with low degree polynomials in this arXiv).

For which tasks CDF/EDF normalization could be beneficial in ML? Any reasons it seems unknown in ML?

Any other interesting nonstandard normalizations?