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SCALABIM

Financiado

Cerrado

Scalable Bayesian Methods for Machine Learning and Imaging

Machine learning seeks to automatize the processing of large complex datasets by adaptive computing, a core strategy to meet growing demands of science and applications. Typically, real-world problems are mapped to penalized estim... ver más

31/12/2016

EPFL

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-12-31 No tenemos la información de la convocatoria

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Información adicional privada

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

1 Participantes

EPFL

0.00€ | Lider

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Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE 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 seeks to automatize the processing of large complex datasets by adaptive computing, a core strategy to meet growing demands of science and applications. Typically, real-world problems are mapped to penalized estimation tasks (e.g., binary classification), which are solved by simple efficient algorithms. While successful so far, I believe this approach is too limited to realise the potential of adaptive computing. Most of the work, such as data selection, feature construction, model calibration and comparison, still has to be done by hand. Demands for automated decision-making (e.g., tuning data acquisition during an experiment) are not met. Such problems are naturally addressed by Bayesian reasoning about uncertain knowledge, which however remains infeasible in most large scale settings. The main goal of this proposal is to unite the strengths of penalized estimation and Bayesian decision-making, exploiting the former's advanced state of the art in order to implement substantial improvements coming with the latter in large scale applications. A major focus is on improving magnetic resonance imaging (MRI) by way of new Bayesian technology, driving robust nonlinear reconstruction from less data, and optimizing the acquisition through Bayesian experimental design, applications not previously attempted by machine learning. Far beyond the reach of present methodology, these goals demand a novel computational foundation for approximate Bayesian inference through numerical algorithmic reductions. This project will have high impact on probabilistic machine learning, raising the bar for scalable Bayesian computations. It will help to open up a whole new range of medical imaging applications for machine learning. Moreover, substantial impact on MRI reconstruction research is anticipated. There is strong recent interest in savings through compressive sensing, whose full potential is realised only by way of adaptive technology such as projected here.

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