# Classification with Runge-Kutta networks and feature space augmentation

Source: [Classification with Runge-Kutta networks and feature space augmentation](<https://www.sota2.com/research/paper/68fa58871e209131ce9aef86>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T15:23:38.191Z

Authors: Elisa Giesecke, Axel Kr\\"oner

Published: 2021-04-06T08:54:30.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.02369>)

[PDF](<https://arxiv.org/pdf/2104.02369.pdf>)