  Progress in designing channel codes has been driven by human ingenuity and,
fittingly, has been sporadic. Polar codes, developed on the foundation of
Arikan's polarization kernel, represent the latest breakthrough in coding
theory and have emerged as the state-of-the-art error-correction code for
short-to-medium block length regimes. In an effort to automate the invention of
good channel codes, especially in this regime, we explore a novel, non-linear
generalization of Polar codes, which we call DeepPolar codes. DeepPolar codes
extend the conventional Polar coding framework by utilizing a larger kernel
size and parameterizing these kernels and matched decoders through neural
networks. Our results demonstrate that these data-driven codes effectively
leverage the benefits of a larger kernel size, resulting in enhanced
reliability when compared to both existing neural codes and conventional Polar
codes.
