Project description
Bringing mathematical clarity to deep learning
Deep learning has transformed science and technology by teaching machines to recognise complex patterns in large datasets, driving advances in image recognition and scientific simulations. However, questions remain about how reliable, robust, and reproducible neural networks really are. It is essential to understand how these algorithms work and why they sometimes fail in order to use them effectively. Supported by the Marie Skłodowska-Curie Actions programme, the REMODEL project explores the mathematical foundations of deep learning. It will apply ideas from dynamical systems, geometry, and optimisation to test how neural networks behave. The project will also examine how neural networks can model dynamical systems and physical processes. Its results will improve our understanding of deep learning.
Objective
The subject of this proposal is “mathematical aspects of deep learning algorithms and their applications”. We will address several questions related to the mathematical foundations of neural networks and set up an interdisciplinary team to aidthe design of test problems and validate the research results obtained. The impact of neural networks and deep learning in recent years has been profound and unprecedented. But in the wake of the vast progress in this area, several questions and concerns have been raised about the robustness, reliability, accuracy, reproducibility and feasibility of neural networks.
It is widely recognised that the mathematical sciences, are a key enabling technology in many aspects of machine learning, not the least to resolve some of the above mentioned concerns. Mathematical language and formalism can bring morerigour and precision to the understanding of the deep learning methodology. Recently, deep learning methods have been applied to physical simulations, and to discover the underlying mathematical model. Most of the work in this area has been limited to proof-of-concept and has not been applied to practical problems. An alternative approach is to make use of reduced order modelling, and this can also be combined with machine learning methods.
The aim of this project is to understand, study, prove, and test the properties of deep learning algorithms using ideas from dynamical systems, geometry and optimisation. The research objectives are three-fold. The first pertains to understandingthe general properties of neural networks and their impact on a range of applications. The second is about the use of neural networks for investigating dynamical systems, and their applications to physical models. Finally we establish a new and complementary network of mathematicians from European and third countries for studying neural networks and the methods of deep learning with connections to a range of application areas through staff exchanges.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences mathematics applied mathematics dynamical systems
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences mathematics pure mathematics geometry
- natural sciences mathematics applied mathematics mathematical model
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff Exchanges
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2022-SE-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
7491 Trondheim
Norway
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.