License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.8.20
URN: urn:nbn:de:0030-drops-177131
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17713/
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Duvenaud, David ; Heinonen, Markus ; Tiemann, Michael ; Welling, Max
Weitere Beteiligte (Hrsg. etc.): David Duvenaud and Markus Heinonen and Michael Tiemann and Max Welling

Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332)

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dagrep_v012_i008_p020_22332.pdf (3 MB)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 22332 "Differential Equations and Continuous-Time Deep Learning". Neural ordinary-differential equations and similar continuous model architectures have gained interest in recent years, due to the existence of a vast literature in calculus and numerical analysis. Thus, continuous models might lead to architectures with finer control over prior assumptions or theoretical understanding. In this seminar, we have sought to bring together researchers from traditionally disjoint areas - machine learning, numerical analysis, dynamical systems and their "consumers" - to try and develop a joint language about this novel modeling paradigm. Through talks & group discussions, we have identified common interests and we hope that this first seminar is but the first step on a joint journey.

BibTeX - Entry

@Article{duvenaud_et_al:DagRep.12.8.20,
  author =	{Duvenaud, David and Heinonen, Markus and Tiemann, Michael and Welling, Max},
  title =	{{Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332)}},
  pages =	{20--30},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{8},
  editor =	{Duvenaud, David and Heinonen, Markus and Tiemann, Michael and Welling, Max},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17713},
  URN =		{urn:nbn:de:0030-drops-177131},
  doi =		{10.4230/DagRep.12.8.20},
  annote =	{Keywords: deep learning, differential equations}
}

Keywords: deep learning, differential equations
Collection: DagRep, Volume 12, Issue 8
Issue Date: 2023
Date of publication: 02.03.2023

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