https://www.dagstuhl.de/21061

07. – 12. Februar 2021, Dagstuhl-Seminar 21061

Differential Equations and Continuous-Time Deep Learning

Organisatoren

David Duvenaud (University of Toronto, CA)
Markus O. Heinonen (Aalto University, FI)
Michael Schober (Bosch Center for AI – Renningen, DE)
Max Welling (University of Amsterdam, NL)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Jutka Gasiorowski zu administrativen Fragen

Michael Gerke zu wissenschaftlichen Fragen

Dokumente

Programm des Dagstuhl-Seminars (Hochladen)

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Motivation

Deep models have revolutionised machine learning due to their remarkable ability to iteratively construct more and more refined representations of data over the layers. Perhaps unsurprisingly very deep learning architectures have recently been shown to converge to differential equation models, which are ubiquitous in sciences but so far overlooked in machine learning. This striking connection opens new avenues of theory and practise of continuous-time machine learning inspired by physical sciences. Simultaneously neural networks have started to emerge as powerful alternatives to cumbersome mechanistic dynamical systems. Finally, deep learning models in conjecture with stochastic gradient optimization has been used to numerically solve high-dimensional partial differential equations. Thus, we have entered a new era of continuous-time modelling in machine learning.

This change in perspective is currently gaining interest rapidly across domains and provides an excellent and topical opportunity to bring together experts in dynamical systems, computational science, machine learning and the relevant scientific domains to lay solid foundations of these efforts. On the other hand, as the scientific communities, events and outlets are significantly disjoint, it is key to organize an interdisciplinary event and establish novel communication channels to ensure the distribution of relevant knowledge.

Over the course of this Dagstuhl Seminar, we want to establish strong contacts, communication and collaboration of the different research communities. Let’s have an exchange of each community’s best practices, known pitfalls and tricks-of-the-trade. We will try to identify the most important open questions and avenues forward to foster interdisciplinary research. To this end, this seminar will feature not only individual contributed talks, but also general discussions and “collaboration bazaars”, for which participants will have the possibility to pitch ideas for break-out project sessions to each other. In the break-out sessions, participants may discuss open problems, joint research obstacles, or community building work.

Motivation text license
  Creative Commons BY 3.0 DE
  David Duvenaud, Markus O. Heinonen, Michael Schober, and Max Welling

Classification

  • Machine Learning
  • Numerical Analysis

Keywords

  • Deep learning
  • Differential equations
  • Numerics
  • Statistics
  • Dynamical systems

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

Dagstuhl's Impact

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Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.