MS researcher recently introduced a new deep ( indeed very deep 🙂 ) NN model (PReLU Net)  and they push the state of art in ImageNet 2012 dataset from 6.66% (GoogLeNet) to 4.94% top-5 error rate.
In this work, they introduce an alternation of well-known ReLU activation function. They call it PReLu (Parametric Rectifier Linear Unit). The idea behind is to allow negative activations on the ReLU function with a control parameter which is also learned over the training phase. Therefore, PReLU allows negative activations and in the paper they argue and emprically show that PReLU is better to resolve diminishing gradient problem for very deep neural networks (> 13 layers) due to allowance of negative activations. That means more activations per layer, hence more gradient feedback at the backpropagation stage.