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SOLARIS

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Large Scale Learning with Deep Kernel Machines

Machine learning has become a key part of scientific fields that produce a massive amount of data and that are in dire need of scalable tools to automatically make sense of it. Unfortunately, classical statistical modeling has oft... ver más

28/02/2022

INRIA

1M€

Presupuesto del proyecto: 1M€

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 2022-02-28

ERC-2016-STG: ERC Starting Grant

I+D

Cerrada hace 9 años

0% 100% 100%

Información adicional privada

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

2 Participantes

INRIA

1.50M€ | Lider

SAINT-GOBAIN CRISTALERIA

1.34M€ | Participante

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Duración del proyecto: 63 meses Fecha Inicio: 2016-11-28
Fecha Fin: 2022-02-28

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 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Machine learning has become a key part of scientific fields that produce a massive amount of data and that are in dire need of scalable tools to automatically make sense of it. Unfortunately, classical statistical modeling has often become impractical due to recent shifts in the amount of data to process, and in the high complexity and large size of models that are able to take advantage of massive data. The promise of SOLARIS is to invent a new generation of machine learning models that fulfill the current needs of large-scale data analysis: high scalability, ability to deal with huge-dimensional models, fast learning, easiness of use, and adaptivity to various data structures. To achieve the expected breakthroughs, our angle of attack consists of novel optimization techniques for solving large-scale problems and a new learning paradigm called deep kernel machine. This paradigm marries two schools of thought that have been considered so far to have little overlap: kernel methods and deep learning. The former is associated with a well-understood theory and methodology but lacks scalability, whereas the latter has obtained significant success on large-scale prediction problems, notably in computer vision. Deep kernel machines will lead to theoretical and practical breakthroughs in machine learning and related fields. For instance, convolutional neural networks were invented more than two decades ago and are today’s state of the art for image classification. Yet, theoretical foundations and principled methodology for these deep networks are nowhere to be found. The project will address such fundamental issues, and its results are expected to make deep networks simpler to design, easier to use, and faster to train. It will also leverage the ability of kernels to model invariance and work with a large class of structured data such as graphs and sequences, leading to a broad scope of applications with potentially groundbreaking advances in diverse scientific fields.

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