  This work introduces a novel activation unit that can be efficiently employed
in deep neural nets (DNNs) and performs significantly better than the
traditional Rectified Linear Units (ReLU). The function developed is a two
parameter version of the specialized Richard's Curve and we call it Adaptive
Richard's Curve weighted Activation (ARiA). This function is non-monotonous,
analogous to the newly introduced Swish, however allows a precise control over
its non-monotonous convexity by varying the hyper-parameters. We first
demonstrate the mathematical significance of the two parameter ARiA followed by
its application to benchmark problems such as MNIST, CIFAR-10 and CIFAR-100,
where we compare the performance with ReLU and Swish units. Our results
illustrate a significantly superior performance on all these datasets, making
ARiA a potential replacement for ReLU and other activations in DNNs.
