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Classification with Runge-Kutta networks and feature space augmentation

arXiv 2104.02369 ↗First posted 6 Apr 2021cs.LG math.DScited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 6 Apr 2021.

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References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2019Augmented Neural ODEscited by 396
  4. 2019Deep learning as optimal control problems: models and numerical methodscited by 33
  5. 2020Structure preserving deep learningcited by 30
  6. 2015Symplectic Runge-Kutta schemes for adjoint equations, automatic differentiation, optimal control and morecited by 4

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