  In this paper, we propose majority voting neural networks for sparse signal
recovery in binary compressed sensing. The majority voting neural network is
composed of several independently trained feedforward neural networks employing
the sigmoid function as an activation function. Our empirical study shows that
a choice of a loss function used in training processes for the network is of
prime importance. We found a loss function suitable for sparse signal recovery,
which includes a cross entropy-like term and an $L_1$ regularized term. From
the experimental results, we observed that the majority voting neural network
achieves excellent recovery performance, which is approaching the optimal
performance as the number of component nets grows. The simple architecture of
the majority voting neural networks would be beneficial for both software and
hardware implementations.
