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DLT SIGNED

Deep Learning Theory: Geometric Analysis of Capacity, Optimization, and Generalization for Improving Learning in Deep Neural Networks

Total Cost €

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EC-Contrib. €

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Partnership

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Project "DLT" data sheet

The following table provides information about the project.

Coordinator
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV 

Organization address
address: HOFGARTENSTRASSE 8
city: Munich
postcode: 80539
website: www.mpg.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙500˙000 €
 EC max contribution 1˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-07-01   to  2023-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV DE (Munich) coordinator 1˙500˙000.00

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 Project objective

Deep Learning is one of the most vibrant areas of contemporary machine learning and one of the most promising approaches to Artificial Intelligence. Deep Learning drives the latest systems for image, text, and audio processing, as well as an increasing number of new technologies. The goal of this project is to advance on key open problems in Deep Learning, specifically regarding the capacity, optimization, and regularization of these algorithms. The idea is to consolidate a theoretical basis that allows us to pin down the inner workings of the present success of Deep Learning and make it more widely applicable, in particular in situations with limited data and challenging problems in reinforcement learning. The approach is based on the geometry of neural networks and exploits innovative mathematics, drawing on information geometry and algebraic statistics. This is a quite timely and unique proposal which holds promise to vastly streamline the progress of Deep Learning into new frontiers.


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 Publications

year authors and title journal last update
List of publications.
2018 Lin, Alex Tong ; Li, Wuchen ; Osher, Stanley and Montúfar, Guido
Wasserstein proximal of GANs
published pages: , ISSN: , DOI:
2020-04-01
2018 Wuchen Li, Guido Montúfar
Natural gradient via optimal transport
published pages: 181-214, ISSN: 2511-2481, DOI: 10.1007/s41884-018-0015-3
Information Geometry 1/2 2020-04-01
2019 Ay, Nihat ; Rauh, Johannes and Montúfar, Guido
A continuity result for optimal memoryless planning in POMDPs
published pages: , ISSN: , DOI:
2020-04-01
2019 Lin, Alex Tong ; Dukler, Yonatan ; Li, Wuchen and Montúfar, Guido
Wasserstein diffusion Tikhonov regularization. Optimal Transport and Machine Learning
published pages: , ISSN: , DOI:
2020-04-01
2019 Dukler, Yonatan ; Li, Wuchen ; Lin, Alex Tong and Montúfar, Guido
Wasserstein of Wasserstein loss for learning generative models
published pages: , ISSN: , DOI:
Proceedings of the 36th international conference on machine learning, 9-15 June 2019, Long Beach, California, USA 2020-04-01

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The information about "DLT" are provided by the European Opendata Portal: CORDIS opendata.

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