Informations générales
Entité de rattachement
Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.
Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.
Implanté au cœur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.
Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :
• La conscience des responsabilités
• La coopération
• La curiosité
Référence
2026-41976
Description de l'unité
Based in Saclay (Essonne), the LIST is one of the two institutes of CEA Tech, the Technological Research Division of the CEA. Dedicated to intelligent digital systems, its mission is to carry out technological developments of excellence on behalf of industrial partners, in order to create value.
Within the LIST, the Laboratory of Vision for Modeling and Localization (LVML) conducts its research in the field of computer vision and artificial intelligence for the perception of intelligent and autonomous systems. The laboratory's research themes include 3D localization, segmentation, characterization and vision for robotics.
Description du poste
Domaine
Mathématiques, information scientifique, logiciel
Contrat
Stage
Intitulé de l'offre
Stage - Training networks to be compressible: low rank, quantization, and how they combine-Saclay-H/F
Sujet de stage
To run on smaller hardware or serve more cheaply, large neural networks are often compressed before deployment: most commonly by quantization (storing numbers with few bits), less often by low-rank factorization (replacing a weight matrix by a product of two thin factors). Models are increasingly trained or fine-tuned so that they compress well, for instance by pushing their weights toward low rank. These methods are mostly judged on factorization alone, yet a factorized model is also quantized before it is deployed. Does preparing a model for low rank help or hurt once it is also quantized, and can the combination compete with quantization alone? Our first measurements suggest the answer is less simple than it looks. Our group develops compression methods that measure errors by their effect on the network's outputs, and that predict in closed form what rounding low-rank factors costs.
Durée du contrat (en mois)
6 mois
Description de l'offre
As an intern at the CEA, you will have the opportunity to work in a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an environment conducive to learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work performed may potentially lead to a scientific publication.
Context:
This internship aims to understand how training a network toward low rank changes what it costs to quantize its factors, using the team's closed-form price of rounding both to explain the effect and to act on it. It combines matrix analysis with controlled experiments on language models, to:
What do we expect from you?
- Measure what low-rank preparation does to a model's compressibility, by factorization and by quantization, against an unprepared model and against quantization alone, at equal memory.
- Explain it: what the preparation changes in the factors, read through the closed-form price, and whether the price predicts the measured cost.
- Use the price to propose and test an improvement.
The internship may lead to a PhD starting in October 2027.
#Cea List
Moyens / Méthodes / Logiciels
Pyyhon - PyTorch
Profil du candidat
Profile:
- Master's (M2) or engineering-school student in machine learning, applied mathematics or computer science
- Strong linear algebra; probability and optimization are a plus
- Solid Python and PyTorch
- Care in designing experiments and reading their results
- Experience with Hugging Face language models is a plus
Conformément aux engagements pris par le CEA en faveur de l'intégration des personnes handicapées, cet emploi est ouvert à toutes et à tous. Le CEA propose des aménagements et/ou des possibilités d'organisation pour l'inclusion des travailleurs handicapés.
Localisation du poste
Site
Saclay
Localisation du poste
France, Ile-de-France, Essonne (91)
Ville
Saclay
Critères candidat
Langues
Anglais (Intermédiaire)
Diplôme préparé
Bac+5 - Diplôme École d'ingénieurs
Formation recommandée
Master 2 ou équivalent
Possibilité de poursuite en thèse
Oui
Demandeur
Disponibilité du poste
01/02/2027
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