PhD position - Training and quantization of large-scale deep neural networks for transfer learning

DETALLES DE LA OPORTUNIDAD

Recompensa total

0 $

Universidad Estatal

CEA Tech

Área

Europa Oriental

País anfitrión

Francia

Fecha límite

31 oct. 2021

Nivel de estudio

Doctorado

Tipo de oportunidad

Doctor

Especialidades

Tecnología de la información e informática.

Oportunidad de financiación

No financiación

Países elegibles

Esta oportunidad está destinada a todos los países

Región elegible

Todas las regiones

SL-DRT-21-0446

RESEARCH FIELD

Artificial intelligence & Data intelligence

ABSTRACT

Training and quantization of large-scale deep neural networks for transfer learningTransfer learning is today a common technique in Deep Learning that uses the learned parameters of a generic network (a feature extractor) to accelerate the training of another network on a more specific task. This specialized network is subsequently optimized for the hardware constraints of the specific use-case. However, given that the representations of the feature extractor are often rather generic, it might be possible to optimize the parameters before the transfer, to avoid that each end-user has to perform this optimization by herself. In this context, the thesis has the following scientific objectives:- Using several “unsupervised” learning methods (self-supervised, weakly supervised, semi-supervised) to train feature extractors on large datasets- Studying how common optimization methods (in particular quantization) can be applied on these extractors in a “task-agnostic” fashion- Quantifying the influence of these optimizations on the transfer learning capacity, by benchmarking and theoretical analysis (e.g. information compression theory)Required competences: Master degree (or equivalent), machine learning (in particular Deep Learning), programming (Python, Pytorch, Tensorflow, C++), good English (French knowledge is not required, but helpful)