DagRep.12.8.20.pdf
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Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 12, Issue 8
Volume: Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-03-02
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David Duvenaud, Markus Heinonen, Michael Tiemann, and Max Welling. Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). In Dagstuhl Reports, Volume 12, Issue 8, pp. 20-30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.12.8.20
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.
Subject Classification
ACM Subject Classification
- Computing methodologies → Machine learning
- Computing methodologies → Philosophical/theoretical foundations of artificial intelligence
- Mathematics of computing → Differential equations
- Mathematics of computing → Solvers
Keywords
- deep learning
- differential equations
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