Piecewise Linear Neural Networks and Deep Learning

As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the representation and the learning have long been studied. In 1977, the canonical representation pioneered the works of shallow PWLNNs learned by incremental designs, but the applications to large-scale data were prohibited. In 2010, rectified linear units (ReLU) advocated the prevalence of PWLNNs in deep learning. Ever since, PWLNNs have been successfully applied to many tasks and achieved excellent performance. In this Primer, we systematically introduce the methodology of PWLNNs by grouping the works into shallow and deep networks. First, different PWLNN representation models are constructed with elaborated examples. With PWLNNs, the evolution of learning algorithms for data is presented and fundamental theoretical analysis follows up for in-depth understandings. Then, representative applications are introduced together with discussions and outlooks. Piecewise linear neural networks (PWLNNs) are a powerful modelling method, particularly in deep learning. In this Primer, Tao et al. introduce the methodology and theoretical analysis of PWLNNs and some of their applications.