Title: Differential Equation Networks. Abstract: Most deep neural networks use simple, fixed activation functions, such
as sigmoids or rectified linear units, regardless of domain or
network structure. We introduce differential equation networks, an
improvement to modern neural networks in which each neuron learns the
particular nonlinear activation function that it requires. We show
that enabling each neuron with the ability to learn its own activation
function results in a more compact network capable of achieving
comperable, if not superior performance when compared to much larger
networks. We
also showcase the capability of a differential equation neuron to
learn behaviors, such as oscillation, currently only obtainable by a
large group of neurons. The ability of
differential equation networks to essentially compress a large neural network, without loss of overall performance
makes them suitable for on-device applications, where predictions must
be computed locally. Our experimental evaluation of real-world and toy
datasets show that differential equation networks outperform fixed activatoin networks in several areas.