Objective
Massive data poses a fundamental challenge to learning algorithms, which is captured by the following computational dogma: The running time of an algorithm increases with the size of its input data. The available computational power, however, is growing slowly relative to data sizes. Hence, large-scale problems of interest require increasingly more time to solve.
Our recent research demonstrates that this dogma is false in general, and supports an emerging perspective: Data should be treated as a resource that can be traded off with other resources such as running time. For data acquisition and communications, we have also shown related sampling, energy, and circuit area trade-offs.
A detailed understanding of time-data and other analogous trade-offs, however, requires interdisciplinary studies that are currently in their infancy even for basic system models. Existing approaches are too specialized, and crucially, they only aim at establishing a trade-off, but not characterizing its optimality or its technological feasibility.
TIME-DATA will confront these challenges by building unified mathematical foundations on how we generate data via sampling, how we set up learning objectives that govern our fundamental goals, and how we optimize these goals to obtain numerical solutions. We will demonstrate our rigorous theory with task-specific, end-to-end trade-offs (e.g. samples, power, computation, and statistical precision) in broad domains, by not only building prototype analog-to-information conversion hardware, but also accelerating scientific and medical imaging, and engineering new tools of discovery in materials science.
Our goal of systematically understanding and expanding on this emerging perspective is ambitious: Our mathematical sampling framework, in tandem with new universal primal-dual algorithms and geometric estimators, are expected to change the way we treat data in information systems, promising substantial flexibility in the use of limited resources.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences mathematics pure mathematics arithmetics
- natural sciences mathematics applied mathematics game theory
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences data science data processing
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-COG - Consolidator Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2016-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1015 LAUSANNE
Switzerland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.