  We present a Machine Learning based approach to the cross section and
asymmetries for deeply virtual Compton scattering from an unpolarized proton
target using both an unpolarized and polarized electron beam. Machine learning
methods are needed to study and eventually interpret the outcome of deeply
virtual exclusive experiments since these reactions are characterized by a
complex final state with a larger number of kinematic variables and
observables, exponentially increasing the difficulty of quantitative analyses.
Our deep neural network (FemtoNet) uncovers emergent features in the data and
learns an accurate approximation of the cross section that outperforms standard
baselines. FemtoNet reveals that the predictions in the unpolarized case
systematically show a smaller relative median error than the polarized that can
be ascribed to the presence of the Bethe Heitler process. It also suggests that
the $t$ dependence can be more easily extrapolated than for the other
variables, namely the skewness, $\xi$ and four-momentum transfer, $Q^2$. Our
approach is fully scalable and will be capable of handling larger data sets as
they are released from future experiments.
