A Highway Layer (paper) is a type of Neural Network layer that uses a gating mechanism to control the information flow through a layer. ^{1}** **Stacking multiple Highway Layers allows for training of very deep networks. Highway Layers work by learning a gating function that chooses which parts of the inputs to pass through and which parts to pass through a transformation function, such as a standard Affine Layer for example. The basic formulation of a Highway Layer is T * h(x) + (1 - T) * x, where T is the learned gating function with values between 0 and 1, h(x) is an arbitrary input transformation and x is the input. Note that all of these must have the same size.

**Sources**

“Deep Learning Glossary.”

*WildML*, 8 Sept. 2017, www.wildml.com/deep-learning-glossary/ (1)