论文标题
分段线性神经网络和深度学习
Piecewise Linear Neural Networks and Deep Learning
论文作者
论文摘要
作为一种强大的建模方法，分段线性神经网络（PWLNNS）已在各个领域都被证明是成功的，最近在深度学习中。为了应用PWLNN方法，长期以来已经研究了表示和学习。 1977年，规范表示率先通过增量设计学到的浅pwlnns的作品，但禁止使用大规模数据的应用。 2010年，纠正的线性单元（RELU）主张PWLNN在深度学习中的普遍性。从那以后，PWLNN已成功地应用于广泛的任务并实现了有利的表现。在此引物中，我们通过将作品分组为浅网络和深层网络来系统地介绍PWLNNS的方法。首先，由详细示例构建了不同的PWLNN表示模型。借助PWLNN，提出了学习数据的学习算法的演变，并且基本理论分析遵循深入的理解。然后，将代表性应用与讨论和前景一起引入。
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, the Rectified Linear Unit (ReLU) advocated the prevalence of PWLNNs in deep learning. Ever since, PWLNNs have been successfully applied to extensive tasks and achieved advantageous performances. In this Primer, we systematically introduce the methodology of PWLNNs by grouping the works into shallow and deep networks. Firstly, 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.
