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TopologiCal ApproacH to Artificial NeurAL NetworKS

Project description

Understanding how artificial neural networks learn

Deep learning models achieve remarkable performance but remain difficult to interpret, limiting their trustworthy deployment. Funded by the Marie Skłodowska-Curie Actions programme, the CHALKS project addresses this by applying computational topology, the mathematical study of shape and structure, to analyse how neural networks process data. Drawing on tools including persistent homology and sheaf theory, the project pursues two complementary objectives: tracking how the topology of data representations evolves through a network's layers to understand the role of its inner transformations, and characterising the geometric and topological properties of network architectures and learned representations. By bridging algebraic topology and machine learning, CHALKS aims to provide mathematically grounded tools for neural network interpretability and architecture design.

Objective

The project TopologiCal ApproacH to Artificial NeurAL NetworKS (CHALKS) will focus on the study of Artificial Neural Networks architecture based on computational topology approaches and sheaf theory. More specifically, towards Explainability, we plan to study the data representations through the different hidden layers of ANNs and track the topological complexity evolution. Then, we want to characterize the needed architecture based on the topological complexity.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

UNIVERSIDAD DE SEVILLA
Net EU contribution
€ 262 599,84
Address
CALLE S. FERNANDO 4
41004 Sevilla
Spain
Region
Sur Andalucía Sevilla
Activity type
Higher or Secondary Education Establishments
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Total cost
No data

Partners (2)