of Deep Learning

DFG-funded Priority Program 2298

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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.

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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

Best Thesis Award 2024

June 7, 2024
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DFG Slides

Applications for the FoDL Best Thesis Award 2024 are now open. For more information, please click the link down below.

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Guest Lecture

June 7, 2024
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Dr. Yury Korolev from the University of Bath will give a lecture on Vector-valued Barron spaces. He will talk about generalization results regarding infinite-dimensional neural networks.

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Poster Collection

April 15, 2024
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Call for ProposalsCoordinator Slides
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Poster for Phase 2 can now be submitted to Laura Thesing ( For the preparation and submission, please click the link above and scroll down.

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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
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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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