FUTURE PROOF

Theoretical and Algorithmic Foundations for Future Proof Information and Inference Systems

Coordinatore	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Switzerland [CH]
Totale costo	1˙824˙220 €
EC contributo	1˙824˙220 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2011-StG_20101014
Funding Scheme	ERC-SG
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-01-01   -   2016-12-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE  Organization address address: BATIMENT CE 3316 STATION 1 city: LAUSANNE postcode: 1015 contact info Titolo: Ms. Nome: Caroline Cognome: Vandevyver Email: send email Telefono: +41 21 693 4977 Fax: +41 21 693 5585	CH (LAUSANNE)	hostInstitution	1˙824˙220.00
2	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE  Organization address address: BATIMENT CE 3316 STATION 1 city: LAUSANNE postcode: 1015 contact info Titolo: Prof. Nome: Volkan Cognome: Cevher Email: send email Telefono: 41795953418 Fax: 41216937600	CH (LAUSANNE)	hostInstitution	1˙824˙220.00

Mappa

 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

 Obiettivo del progetto (Objective)

'A critical technological challenge for emerging information systems is to acquire, analyze and learn from the ever-increasing high-dimensional data produced by natural and man-made phenomena. Sampling, streaming, and recoding of even the most basic applications now produce a data deluge that severely stresses the available analog-to-digital converter, digital communication and storage resources, and easily swamps the back-end processing and learning systems.

Surprisingly, while the ambient data dimension is large in many problems, the relevant information therein typically resides in a much lower dimensional space. Viewed combinatorially and geometrically, natural constraints often cause data to cluster along low-dimensional structures, such as unions-of-subspaces or manifolds, having a few degrees of freedom relative to their size. This powerful notion suggests the potential for developing highly efficient methods for processing and learning by capturing and exploiting the inherent model, or data’s “information level.”

To this end, we seek to revolutionize scientific and practical modi operandi of data acquisition and learning by developing a new optimization and analysis framework based on the nascent low-dimensional models with broad applications—from inverse problems to analog-to-information conversion, and from automated representation learning to statistical regression. We attack the curse of dimensionality in specific ways, not only by relying on the blessing of dimensionality via concentration-of-measures, but also by exploiting geometric topologies and the diminishing returns (i.e., submodularity) within learning objectives. We believe only an approach such as ours can provide the theoretical scaffold for a future proof processing and learning framework that scales its operation to the problem’s information level, promising substantial reductions in hardware complexity, communication, storage, and computational resources.'