  In this paper we introduce Neural Network Coding(NNC), a data-driven approach
to joint source and network coding. In NNC, the encoders at each source and
intermediate node, as well as the decoder at each destination node, are neural
networks which are all trained jointly for the task of communicating correlated
sources through a network of noisy point-to-point links. The NNC scheme is
application-specific and makes use of a training set of data, instead of making
assumptions on the source statistics. In addition, it can adapt to any
arbitrary network topology and power constraint. We show empirically that, for
the task of transmitting MNIST images over a network, the NNC scheme shows
improvement over baseline schemes, especially in the low-SNR regime.
