  Deep neural networks have recently achieved state-of-the-art results in many
machine learning problems, e.g., speech recognition or object recognition.
Hitherto, work on rectified linear units (ReLU) provides empirical and
theoretical evidence on performance increase of neural networks comparing to
typically used sigmoid activation function. In this paper, we investigate a new
manner of improving neural networks by introducing a bunch of copies of the
same neuron modeled by the generalized Kumaraswamy distribution. As a result,
we propose novel non-linear activation function which we refer to as
Kumaraswamy unit which is closely related to ReLU. In the experimental study
with MNIST image corpora we evaluate the Kumaraswamy unit applied to
single-layer (shallow) neural network and report a significant drop in test
classification error and test cross-entropy in comparison to sigmoid unit, ReLU
and Noisy ReLU.
