Theoretical
Foundations
of Deep Learning

DFG-funded Priority Program 2298

Learn More
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23

Projects

34

Prinicipal
Investigators

24

Universities from across Germany

Theoretical Foundations of Deep Learning

Towards a better understanding of deep learning

Parallel to the impressive success of deep learning in real-world applications ranging from autonomous driving to gaming intelligence and healthcare, deep learning-based methods are now also making a strong impact in science, replacing or complementing state-of-the-art classical model-based methods in solving mathematical problems such as inverse problems or partial differential equations.

However, despite the outstanding successes, most of the research on deep neural networks is empirically driven and their theoretical-mathematical foundations are largely lacking. The main goal of this priority program is to develop a comprehensive theoretical foundation of deep learning.

Map of Germany with markers at the locations of all the projects

Three complementary viewpoints

The statistical perspective, which views neural network training as a statistical learning problem and investigates expressivity, learning, optimization, and generalization,

The application perspective, which focuses on security, robustness, interpretability, and fairness

The mathematical-methodological perspective, which develops and theoretically analyzes novel Deep Learning-based approaches to solving inverse problems and partial differential equations.

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Many relevant fields

The research questions to be addressed in this priority program are to a large extent interdisciplinary in nature and can only be solved by a joint effort of mathematics and computer science.

Mathematical methods and concepts from all areas of mathematics are required, including algebraic geometry, analysis, stochastics, approximation theory, differential geometry, discrete mathematics, functional analysis, optimal control, optimization, and topology.

Statistics and theoretical computer science also play a fundamental role. In this sense, methods from mathematics, statistics and computer science form the core of this priority program.

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News & Info

Annual Meeting 2023

November 8, 2023
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DFG Slides
Call for ProposalsCoordinator Slides
DFG Slides
Application
Click here

The 2023 annual meeting was a valuable platform for showcasing the various research endeavors of the SPP. The beautiful atmosphere of Lake Starnberg provided an inspiring backdrop for the event, creating an ideal setting for the exchange of ideas and fruitful discussions.

Online Info Event

October 18, 2023
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DFG Slides
Call for ProposalsCoordinator Slides
DFG Slides
Application
Click here

An information session for phase 2 of the priority program took place on the 18th of October. Please click the links below to download the slides.

Call for Proposals

June 23, 2023
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DFG Slides
Call for ProposalsCoordinator Slides
DFG Slides
Application
Click here

If you are interested in joining Phase 2 of the Theoretical Foundations of Deep Learning Prioritiy Program, please click below to find out more about how to apply.

Events

PhD course

May 27, 2024

Outreach Lars Grüne

April 9, 2024

Annual Meeting 2023

November 8, 2023

Phase 2 Online Info Event

October 18, 2023

Workshop in Bayreuth

May 30, 2022

Virtual Kick-off Meeting

January 18, 2022

Junior Researcher Meetup

April 6, 2022

Annual Meeting 2022

November 20, 2022
See All Events

News from the AI community at LMU

Find the latest news and upcoming events from the various groups researching artificial intelligence and its applications at LMU Munich.

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