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CORaL

Financiado

Correlations-Oriented Representation Learning

Deep learning methods have propelled many of the recent remarkable achievements of artificial intelligence, yet their inner workings remain enigmatic. How do these methods represent the training examples in their multiple processi... ver más

30/09/2026

SISSA

173K€

Presupuesto del proyecto: 173K€

Líder del proyecto

SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVAN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-05-24

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 ExpectedOutcome:Project results are expected to contribute to the following outcomes:

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1 Participantes

SISSA

172.75K€ | Lider

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Duración del proyecto: 28 meses Fecha Inicio: 2024-05-24
Fecha Fin: 2026-09-30

Líder del proyecto

SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVAN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 173K€

Descripción del proyecto Deep learning methods have propelled many of the recent remarkable achievements of artificial intelligence, yet their inner workings remain enigmatic. How do these methods represent the training examples in their multiple processing layers? How are these representations used to make decisions on previously unseen data? The Correlation-Oriented Representation Learning (CORaL) project delves into the intricate relationship between data correlations and the hidden representations of deep neural networks. CORaL's core objectives encompass: i) characterizing the structure of data correlations in tasks where deep learning methods excel; ii) providing a theoretical description of the interplay between the learning dynamics of deep networks and data correlations; and iii) validating these theoretical insights on benchmark machine-learning datasets. The Researcher will approach these objectives with an effective mix of numerical experiments, scaling descriptions and rigorous arguments, inspired by statistical physics and fostered by the unique interdisciplinary scientific community of SISSA. By describing the impact of data correlations on representation learning, CORaL holds the potential to enhance the interpretability and efficiency of deep learning methods across diverse domains, while also enhancing our general understanding of the principles of learning from examples.

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