  A major contributing factor to the recent advances in deep neural networks is
structural units that let sensory information and gradients to propagate
easily. Gating is one such structure that acts as a flow control. Gates are
employed in many recent state-of-the-art recurrent models such as LSTM and GRU,
and feedforward models such as Residual Nets and Highway Networks. This enables
learning in very deep networks with hundred layers and helps achieve
record-breaking results in vision (e.g., ImageNet with Residual Nets) and NLP
(e.g., machine translation with GRU). However, there is limited work in
analysing the role of gating in the learning process. In this paper, we propose
a flexible $p$-norm gating scheme, which allows user-controllable flow and as a
consequence, improve the learning speed. This scheme subsumes other existing
gating schemes, including those in GRU, Highway Networks and Residual Nets as
special cases. Experiments on large sequence and vector datasets demonstrate
that the proposed gating scheme helps improve the learning speed significantly
without extra overhead.

Machine Learning;Machine Learning;Neural and Evolutionary Computing