论文标题
funcnn：使用广义输入空间适合深神经网络的R软件包
FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces
论文作者
论文摘要
当输入空间由标量变量组成时，神经网络在回归和分类问题方面表现出色。由于这种熟练程度，已经开发了几个受欢迎的软件包，使用户可以轻松拟合这些模型。但是，该方法排除了功能协变量的使用，到目前为止，没有软件允许用户使用此广义输入空间构建深度学习模型。据我们所知，功能性神经网络（Funcnn）库是任何编程语言中的第一个包装；该图书馆是为R开发的，是在Keras Architecture的顶部建造的。在整个本文中，引入了几种功能，为用户提供了一种途径，以轻松构建模型，生成预测和运行交叉验证。还提供了基础方法的摘要。最终的贡献是一个软件包，为数据问题提供了一组通用建模和诊断工具，其中同时存在功能和标量协变量。
Neural networks have excelled at regression and classification problems when the input space consists of scalar variables. As a result of this proficiency, several popular packages have been developed that allow users to easily fit these kinds of models. However, the methodology has excluded the use of functional covariates and to date, there exists no software that allows users to build deep learning models with this generalized input space. To the best of our knowledge, the functional neural network (FuncNN) library is the first such package in any programming language; the library has been developed for R and is built on top of the keras architecture. Throughout this paper, several functions are introduced that provide users an avenue to easily build models, generate predictions, and run cross-validations. A summary of the underlying methodology is also presented. The ultimate contribution is a package that provides a set of general modelling and diagnostic tools for data problems in which there exist both functional and scalar covariates.
