Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent. With this adaptive activation function, we are able to improve upon deep neural network architectures composed of static rectified linear units, achieving state-of-theart performance on CIFAR-10 (7.51%), CIFAR-100 (30.83%), and a benchmark from high-energy physics involving Higgs boson decay modes.
Recently, in REF the authors tackle the problem from a different angle using piecewise linear activation functions that are learned independently for each neuron using gradient descent.
Learning Activation Functions to Improve Deep Neural Networks
ICLR 2014
arXiv: Neural and Evolutionary Computing
Computer Science Mathematics