Classification with Runge-Kutta networks and feature space augmentation

IF 1 Q3 Engineering

Journal of Computational Dynamics Pub Date : 2021-04-06 DOI:10.3934/jcd.2021018

E. Giesecke, Axel Kroner

引用次数: 0

Abstract

In this paper we combine an approach based on Runge-Kutta Nets considered in [Benning et al., J. Comput. Dynamics, 9, 2019] and a technique on augmenting the input space in [Dupont et al., NeurIPS, 2019] to obtain network architectures which show a better numerical performance for deep neural networks in point and image classification problems. The approach is illustrated with several examples implemented in PyTorch.

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龙格-库塔网络和特征空间增强分类

在本文中，我们结合了[Benning et al.， J. Comput]中考虑的基于龙格-库塔网的方法。[杜邦等人，NeurIPS, 2019]中的一种增强输入空间的技术，以获得在点和图像分类问题中表现出更好数值性能的深度神经网络网络架构。通过在PyTorch中实现的几个示例说明了该方法。

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 Computational Dynamics Engineering-Computational Mechanics

CiteScore

2.30

自引率

10.00%

发文量

期刊介绍： JCD is focused on the intersection of computation with deterministic and stochastic dynamics. The mission of the journal is to publish papers that explore new computational methods for analyzing dynamic problems or use novel dynamical methods to improve computation. The subject matter of JCD includes both fundamental mathematical contributions and applications to problems from science and engineering. A non-exhaustive list of topics includes * Computation of phase-space structures and bifurcations * Multi-time-scale methods * Structure-preserving integration * Nonlinear and stochastic model reduction * Set-valued numerical techniques * Network and distributed dynamics JCD includes both original research and survey papers that give a detailed and illuminating treatment of an important area of current interest. The editorial board of JCD consists of world-leading researchers from mathematics, engineering, and science, all of whom are experts in both computational methods and the theory of dynamical systems.