  Several methods of estimating the mutual information of random variables have
been developed in recent years. They can prove valuable for novel approaches to
learning statistically independent features. In this paper, we use one of these
methods, a mutual information neural estimation (MINE) network, to present a
proof-of-concept of how a neural network can perform linear ICA. We minimize
the mutual information, as estimated by a MINE network, between the output
units of a differentiable encoder network. This is done by simple alternate
optimization of the two networks. The method is shown to get a qualitatively
equal solution to FastICA on blind-source-separation of noisy sources.
