  We develop a robust method to extract the pole configuration of a given
partial-wave amplitude. In our approach, a deep neural network is constructed
where the statistical errors of the experimental data are taken into account.
The teaching dataset is constructed using a generic S-matrix parametrization,
ensuring that all the poles produced are independent of each other. The
inclusion of statistical error results into a noisy classification dataset
which we should solve using the curriculum method. As an application, we use
the elastic $\pi N$ amplitude in the $I(J^P)=1/2(1/2^{-})$ sector where $10^6$
amplitudes are produced by combining points in each error bar of the
experimental data. We fed the amplitudes to the trained deep neural network and
find that the enhancements in the $\pi N$ amplitude are caused by one pole in
each nearby unphysical sheet and at most two poles in the distant sheet.
Finally, we show that the extracted pole configurations are independent of the
way points in each error bar are drawn and combined, demonstrating the
statistical robustness of our method.
