Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

SEQUOIA

Financiado

Cerrado

Robust algorithms for learning from modern data

Machine learning is needed and used everywhere, from science to industry, with a growing impact on many disciplines. While first successes were due at least in part to simple supervised learning algorithms used primarily as black... ver más

31/08/2023

INRIA

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-08-31

ERC-2016-COG: ERC Consolidator Grant

I+D

Cerrada hace 8 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

INRIA

2.00M€ | Lider

ORMAZABAL CORPORATE TECHNOLO...

217.49K€ | Participante

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

PID2019-106827GB-I00 APRENDIZAJE AUTOMATICO: INTREPRETABILDAD, ALGORITMOS AVANZAD... 97K€ Cerrado

JDC2023-051006-I Optimization algorithms for machine learning and other high-... 72K€ Cerrado

PRE2020-092608 APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE 99K€ Cerrado

ECOLE Experience based Computation Learning to Optimise 2M€ Cerrado

TIN2015-65069-C2-1-R ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA... 131K€ Cerrado

TIN2017-85887-C2-1-P ALGORITMOS HIBRIDOS COMBINANDO APRENDIZAJE AUTOMATICO Y META... 72K€ Cerrado

Duración del proyecto: 77 meses Fecha Inicio: 2017-03-16
Fecha Fin: 2023-08-31

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Machine learning is needed and used everywhere, from science to industry, with a growing impact on many disciplines. While first successes were due at least in part to simple supervised learning algorithms used primarily as black boxes on medium-scale problems, modern data pose new challenges. Scalability is an important issue of course: with large amounts of data, many current problems far exceed the capabilities of existing algorithms despite sophisticated computing architectures. But beyond this, the core classical model of supervised machine learning, with the usual assumptions of independent and identically distributed data, or well-defined features, outputs and loss functions, has reached its theoretical and practical limits. Given this new setting, existing optimization-based algorithms are not adapted. The main objective of this proposal is to push the frontiers of supervised machine learning, in terms of (a) scalability to data with massive numbers of observations, features, and tasks, (b) adaptability to modern computing environments, in particular for parallel and distributed processing, (c) provable adaptivity and robustness to problem and hardware specifications, and (d) robustness to non-convexities inherent in machine learning problems. To achieve the expected breakthroughs, we will design a novel generation of learning algorithms amenable to a tight convergence analysis with realistic assumptions and efficient implementations. They will help transition machine learning algorithms towards the same wide-spread robust use as numerical linear algebra libraries. Outcomes of the research described in this proposal will include algorithms that come with strong convergence guarantees and are well-tested on real-life benchmarks coming from computer vision, bioinformatics, audio processing and natural language processing. For both distributed and non-distributed settings, we will release open-source software, adapted to widely available computing platforms.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores