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

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

France 31 Oct 2021

ABOUT THE INSTITUTION

CEA Tech is building on CEA Letis successful track record innovating for industry.
Visit institution page

OPPORTUNITY DETAILS

State University
Area
Host Country
Deadline
31 Oct 2021
Study level
Opportunity type
PhD
Specialities
Eligible Countries
This opportunity is destined for all countries
Eligible Region
All Regions

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)

LOCATION

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Artificielle Embarquée

Saclay

CONTACT PERSON

THIELE Johannes

CEA

DRT/DSCIN/DSCIN/LIAE

CEA SACLAY - NANO INNOVBAT. 86291191 GIF SUR YVETTE

Phone number: 33.1.69.08.25.10

Email: johannes.thiele@cea.fr

UNIVERSITY / GRADUATE SCHOOL

Paris-Saclay

Sciences et Technologies de l’Information et de la Communication (STIC)

START DATE

Start date on

THESIS SUPERVISOR

DELEZOIDE Bertrand

CEA

DRT/DIASI//LASTI

CEA SACLAY - NANO INNOVBAT. 861Point courier 17391191 GIF SUR YVETTE

Phone number: 33.1.69.08.01.53

Email: bertrand.delezoide@cea.fr

Choose your study destination


Choose the country you wish to travel to study for free, work or volunteer

Please find also