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Efficient algorithms for sustainable machine learning

This project will develop and integrate the latest optimization and statistical advances into a new generation of resource-efficient algorithms for large-scale machine learning. State-of-the-art machine learning methods provide... ver más

31/10/2025

UNIGE

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITA DEGLI STUDI DI GENOVA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-03-21

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2018-COG: ERC Consolidator Grant

I+D

Cerrada hace 6 años

0% 100%

2 Participantes

UNIGE

1.98M€ | Lider

APPENTRA SOLUTIONS

249.85K€ | Participante

Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 79 meses Fecha Inicio: 2019-03-21
Fecha Fin: 2025-10-31

Líder del proyecto

UNIVERSITA DEGLI STUDI DI GENOVA

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto This project will develop and integrate the latest optimization and statistical advances into a new generation of resource-efficient algorithms for large-scale machine learning. State-of-the-art machine learning methods provide impressive results, opening new perspectives for science, technology, and society. However, they rely on massive computational resources to process huge manually annotated data-sets. The corresponding costs in terms of energy consumption and human efforts are not sustainable. This project builds on the idea that improving efficiency is a key to scale the ambitions and applicability of machine learning. Achieving efficiency requires overcoming the traditional boundaries between statistics and computations, to develop new theory and algorithms. Within a multidisciplinary approach, we will establish a new regularization theory of efficient machine learning. We will develop models that incorporate budgeted computations, and numerical solutions with resources tailored to the statistically accuracy allowed by the data. Theoretical advances will provide the foundations for novel and sound algorithmic solutions. Close collaborations in diverse applied fields will ensure that our research results and solutions will be apt and immediately applicable to real world scenarios. The new algorithms developed in the project will contribute to boost the possibilities of Artificial Intelligence, modeling and decision making in a world of data with ever-increasing size and complexity.

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