Title: Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
Authors: Dan Hendrycks, Kevin Gimpel
TL;DR: A Competitor of ReLUs and ELUs with a Probabilistic Underpinning
Abstract:
We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. This stochastic regularizer is comparable to nonlinearities aided by dropout, but it removes the need for a traditional nonlinearity. The connection between the GELU and the stochastic regularizer suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.
Venue: Submitted to ICLR 2017