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Automated deep neural network architecture design has received a significantamount of recent attention. However, this attention has not been equally sharedby one of the fundamental building blocks of a deep neural network, theneurons. In this study, we propose PolyNeuron, a novel automatic neurondiscovery approach based on learned polyharmonic spline activations. Morespecifically, PolyNeuron revolves around learning polyharmonic splines,characterized by a set of control points, that represent the activationfunctions of the neurons in a deep neural network. A relaxed variant ofPolyNeuron, which we term PolyNeuron-R, loosens the constraints imposed byPolyNeuron to reduce the computational complexity for discovering the neuronactivation functions in an automated manner. Experiments show both PolyNeuronand PolyNeuron-R lead to networks that have improved or comparable performanceon multiple network architectures (LeNet-5 and ResNet-20) using differentdatasets (MNIST and CIFAR10). As such, automatic neuron discovery approachessuch as PolyNeuron is a worthy direction to explore.

PolyNeuron: Automatic Neuron Discovery via Learned Polyharmonic Spline Activations