  This paper investigates decoder diversity architectures for short low-density
parity-check (LDPC) codes, based on recurrent neural network (RNN) models of
the belief-propagation (BP) algorithm. We propose a new approach to achieve
decoder diversity in the waterfall region, by specializing BP-RNN decoders to
specific classes of errors, with absorbing set support. We further combine our
approach with an ordered statistics decoding (OSD) post-processing step, which
effectively leverages the bit-error rate optimization deriving from the use of
the binary cross-entropy loss function. We show that a single specialized
BP-RNN decoder combines better than BP with the OSD post-processing step.
Moreover, combining OSD post-processing with the diversity brought by the use
of multiple BP-RNN decoders, provides an efficient way to bridge the gap to
maximum likelihood decoding.
