  Over the last years, Machine Learning (ML) methods have been successfully
applied to a wealth of problems in high-energy physics. For instance, in a
previous work we have reported that using ML techniques one can extract the
Multiparton Interactions (MPI) activity from minimum-bias pp data. Using the
available LHC data on transverse momentum spectra as a function of
multiplicity, we reported the average number of MPI ($\langle N_{\rm mpi}
\rangle$) for minimum-bias pp collisions at $\sqrt{s}=5.02$ and 13\,TeV. In
this work, we apply the same analysis to a new set of data. We report that
$\langle N_{\rm mpi} \rangle$ amounts to $3.98 \pm 1.01$ for minimum-bias pp
collisions at $\sqrt{s}=7$\,TeV. These complementary results suggest a modest
center-of-mass energy dependence of $\langle N_{\rm mpi} \rangle$. The study is
further extended aimed at extracting the multiplicity dependence of $\langle
N_{\rm mpi} \rangle$ for the three center-of-mass energies. We show that our
results qualitatively agree with existing ALICE measurements sensitive to MPI.
Namely, $\langle N_{\rm mpi} \rangle$ increases approximately linearly with the
charged-particle multiplicity. But, it deviates from the linear dependence at
large charged-particle multiplicities. The deviation from the linear trend can
be explained in terms of a bias towards harder processes given the multiplicity
selection at mid-pseudorapidity. The results reported in this paper provide
additional evidence of the presence of MPI in pp collisions, and they can be
useful for a better understanding of the heavy-ion-like behaviour observed in
pp data.
