Mathematics of deep learning

Overview

The Artificial Intelligence (AI) PEPR is the upstream component of the acceleration strategy decided by the French government to contribute to the recovery effort and prepare France’s future by meeting the economic, social and environmental challenges of artificial intelligence (second phase of the national AI strategy).

The operational implementation of PEPR IA is materialized through two types of projects: targeted projects, launched at the start of the program and which bring together research communities structured around well-defined themes, covering the PEPR IA axes, and projects selected via Calls for Projects (AAP), which aim to extend the structuring towards an existing but diffuse scientific community, to respond to well-defined problems, in addition to the objects treated within the framework of the targeted projects. This document describes the Call for Projects, which is specific to the “mathematical foundations” axis of PEPR IA, and which has a funding budget of €6M: it specifies the context and objectives of the call, its phasing, the themes and projects expected, the procedures for submitting proposals, the procedure for examining and selecting candidate projects, as well as the criteria for evaluating proposals and the general provisions for funding. Projects will have a duration of between 36 and 48 months. The amount of funding requested must be a minimum of 600 k€ and a maximum of 1 M€.

Call is closed and being processed.

Link to ANR website : anr.fr/appel-a-projets-mathematiques-de-lapprentissage-profond-2024/


Autres projets

 NNawaQ
NNawaQ
NNawaQ, Neural Network Adequate Hardware Architecture for Quantization (HOLIGRAIL project)
Voir plus
 Package Python Keops
Package Python Keops
Package Python Keops for (very) high-dimensional tensor calculations (PDE-AI project)
Voir plus
 MPTorch
MPTorch
MPTorch, a PyTorch-based framework for simulating and emulating custom precision DNN training (HOLIGRAIL project)
Voir plus
 CaBRNeT
CaBRNeT
CaBRNeT, a library for developing and evaluating Case-Based Reasoning Models (SAIF project)
Voir plus
 SNN Software
SNN Software
SNN Software, Open Source Tools for SNN Design (EMERGENCES project)
Voir plus
 SDOT
SDOT
SDOT, A C++ and Python library for Semi-Discrete Optimal Transport (PDE-AI project)
Voir plus
 FloPoCo
FloPoCo
FloPoCo (Floating-Point Cores), a generator of arithmetic cores and its applications to IA accelerators (HOLIGRAIL project)
Voir plus
 Lazylinop
Lazylinop
Lazylinop (Lazy Linear Operator), a high-level linear operator based on an arbitrary underlying implementation, (SHARP project)
Voir plus
 CAISAR
CAISAR
CAISAR, a platform for characterizing artificial intelligence safety and robustness
Voir plus
 P16
P16
P16 or to develop, distribute and maintain a set of sovereign libraries for AI
Voir plus
 AIDGE
AIDGE
AIDGE, the DEEPGREEN project's open embedded development platform
Voir plus
 Jean-Zay
Jean-Zay
Jean Zay or the national infrastructure for the AI research community
Voir plus
 ADAPTING
ADAPTING
Adaptive architectures for embedded artificial intelligence
Voir plus
 Call of chairs Attractivité
Call of chairs Attractivité
PEPR AI Chairs program offers exceptionally talented AI researchers the opportunity to establish and lead a research program and team for a duration of 4 years in France.
Voir plus
 CAUSALI-T-AI
CAUSALI-T-AI
When causality and AI teams up to enhance interpretability and robustness of AI algorithms
Voir plus
 EMERGENCES
EMERGENCES
Near-physics emerging models for embedded AI
Voir plus
 FOUNDRY
FOUNDRY
The foundations of robustness and reliability in artificial intelligence
Voir plus
 HOLIGRAIL
HOLIGRAIL
Hollistic approaches to greener model architectures for inference and learning
Voir plus
 PDE-AI
PDE-AI
Numerical analysis, optimal control and optimal transport for AI / "New architectures for machine learning".
Voir plus
 REDEEM
REDEEM
Resilient, decentralized and privacy-preserving machine learning
Voir plus
 SAIF
SAIF
Safe AI through formal methods
Voir plus
 SHARP
SHARP
Sharp theoretical and algorithmic principles for frugal ML
Voir plus