deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
D. Rügamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, N. Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, B. Bischl, C. Müller
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引用次数: 15

Abstract

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library \pkg{TensorFlow} for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as \pkg{mgcv}. The packages' modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models.
深度回归:半结构化深度分布回归的灵活神经网络框架
在本文中,我们描述了半结构化深度分布回归的实现,这是一种基于加性回归模型和深度网络相结合的学习条件分布的灵活框架。我们的实现包括(1)一个基于深度学习库\pkg{TensorFlow}的模块化神经网络构建系统,用于融合各种统计和深度学习方法,(2)一个正交化单元,允许不同子网的可解释组合,以及(3)建立此类模型所需的预处理步骤。该软件包允许通过公式界面以用户友好的方式定义模型,该界面受经典统计模型框架(如\pkg{mgcv})的启发。这些软件包的模块化设计和功能为复杂统计模型的可扩展估计以及深度学习和统计方法的组合提供了独特的资源。这允许最先进的预测性能,同时保留经典统计模型不可或缺的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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