  We discuss the use of machine learning techniques in effectively
nonparametric modelling of generalised parton distributions (GPDs) in view of
their future extraction from experimental data. Current parameterisations of
GPDs suffer from model dependency that lessens their impact on phenomenology
and brings unknown systematics to the estimation of quantities like Mellin
moments. The new strategy presented in this study allows to describe GPDs in a
way fulfilling theory-driven constraints, keeping model dependency to a
minimum. Getting a better grip on the control of systematic effects, our work
will help the GPD phenomenology to achieve its maturity in the precision era
commenced by the new generation of experiments.
