论文标题
一项有关最近提出的深度学习激活功能的调查
A survey on recently proposed activation functions for Deep Learning
论文作者
论文摘要
人工神经网络（ANN）通常称为神经网络，是一类机器学习算法，并获得了广泛的成功，受到人脑的生物结构的启发。神经网络本质上是强大的，因为它们可以从数据中学习复杂的功能近似值。这种概括能力能够影响涉及图像识别，语音识别，自然语言处理等的多学科领域。激活功能是神经网络的关键子组成部分。他们定义了给定一组输入的网络中节点的输出。这项调查讨论了神经网络中激活功能的主要概念，包括：简要介绍了深度神经网络，摘要是激活功能以及它们如何在神经网络，最常见的特性，不同类型的激活功能，激活功能所面临的一些挑战，局限性和替代解决方案中，以最终的评价结束。
Artificial neural networks (ANN), typically referred to as neural networks, are a class of Machine Learning algorithms and have achieved widespread success, having been inspired by the biological structure of the human brain. Neural networks are inherently powerful due to their ability to learn complex function approximations from data. This generalization ability has been able to impact multidisciplinary areas involving image recognition, speech recognition, natural language processing, and others. Activation functions are a crucial sub-component of neural networks. They define the output of a node in the network given a set of inputs. This survey discusses the main concepts of activation functions in neural networks, including; a brief introduction to deep neural networks, a summary of what are activation functions and how they are used in neural networks, their most common properties, the different types of activation functions, some of the challenges, limitations, and alternative solutions faced by activation functions, concluding with the final remarks.
