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The Symbiosis of Deep Learning and Differential Equations
Luca Celotti · Kelly Buchanan · Jorge Ortiz · Patrick Kidger · Stefano Massaroli · Michael Poli · Lily Hu · Ermal Rrapaj · Martin Magill · Thorsteinn Jonsson · Animesh Garg · Murtadha Aldeer

Tue Dec 14 03:45 AM -- 08:59 PM (PST) @
Event URL: https://dl-de.github.io/ »

Deep learning can solve differential equations, and differential equations can model deep learning. What have we learned and where to next?

The focus of this workshop is on the interplay between deep learning (DL) and differential equations (DEs). In recent years, there has been a rapid increase of machine learning applications in computational sciences, with some of the most impressive results at the interface of DL and DEs. These successes have widespread implications, as DEs are among the most well-understood tools for the mathematical analysis of scientific knowledge, and they are fundamental building blocks for mathematical models in engineering, finance, and the natural sciences. This relationship is mutually beneficial. DL techniques have been used in a variety of ways to dramatically enhance the effectiveness of DE solvers and computer simulations. Conversely, DEs have also been used as mathematical models of the neural architectures and training algorithms arising in DL.

This workshop will aim to bring together researchers from each discipline to encourage intellectual exchanges and cultivate relationships between the two communities. The scope of the workshop will include important topics at the intersection of DL and DEs.

Author Information

Luca Celotti (Université de Sherbrooke)
Kelly Buchanan (Columbia University)
Jorge Ortiz (Rutgers University)
Patrick Kidger (University of Oxford)
Stefano Massaroli (The University of Tokyo)
Michael Poli (Stanford University)

My work spans topics in deep learning, dynamical systems, variational inference and numerical methods. I am broadly interested in ensuring the successes achieved by deep learning methods in computer vision and natural language are extended to other engineering domains.

Lily Hu (Google Research)
Ermal Rrapaj (University of California, Berkeley)
Martin Magill (University of Ontario Institute of Technology)
Thorsteinn Jonsson (EthicalAI)
Animesh Garg (University of Toronto, Nvidia, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

Murtadha Aldeer (Rutgers- The State University of New Jersey (All Campuses))

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