Dates : 2023 – 2027 Identifier: ANR-23-PEIA-0008
Sharp Theoretical and Algorithmic Principles for Frugal ML
SHARP will address the major challenge of designing, analyzing and deploying a new generation of intrinsically frugal models (neural or not) able to achieve the versatility and performance of today’s best models while requiring only a vanishing fraction of the resources currently needed. This will be achieved by the constitution of a strong task force able to cover an integrated pipeline, from theoretical foundations to flagship AI domains such as computer vision and natural language processing. With foundational advances towards stronger principles, smaller models, smaller datasets, SHARP will allow tomorrow’s best AI systems to run on yesterday’s devices, somewhat providing a cure against obsolescence.
Funded partners
- LIP (ENS de Lyon, Univ. Claude Bernard Lyon 1, CNRS, Inria), coordinator
- LAMSADE (Paris-Dauphine and PSL University, CNRS)
- LIGM (École des Ponts ParisTech, Univ Gustave Eiffel, CNRS)
- GENESIS (Inria & University College London)
- IRISA (CNRS, Univ Rennes, Inria, INSA)
- List (Université Paris Saclay, CEA)
- ISIR (Sorbonne University, CNRS)
Project web site: https://project.inria.fr/sharp/
