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PhD position - Knowledge-driven universal multimodal representation
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JOB DETAILS
Published: 8 days ago
Application deadline: Oct 31
Location: Grenoble, France
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PhD position - Knowledge-driven universal multimodal representation

SL-DRT-18-0962

RESEARCH FIELD

Computer science and software

ABSTRACT

It is now common knowledge that the domain of artificial intelligence has seen a significant renewal these last years. It is mainly due to the impressive results obtained, for many tasks, by deep neural networks that raise many opportunities. In particular, these approaches have shown very impressive results for several classical visual recognition tasks, natural language and speech processing and recognition tasks but they also enable considerable advances in higher level tasks such as playing at the Go Game or answering to visual questions. However, despite the superior performance of these deep neural networks approaches, it remains challenging to understand their inner workings and explain their output predictions. As a consequence they are often considered as block-boxes which is a strong limitation for their applications in various domains in which transparency and explainability are of strong importance. As a consequence, despite the recent success, one of the major challenge of AI is to be able to designed AI-enabled systems, able to predict but also to explain and to justify the results of their predictions. Although, various initiatives have been launched recently both in the artificial intelligence community (XAI : explainable artificial intelligence) and in the machine learning one (Interpretable machine learning models). In particular it is common to classify the approaches on this field into three families: (1) Approaches that try to build interpretable and explainable models by design, either by using models that are by essence interpretable such that compact decision trees or rule-based systems or by capturing the interpretability into the objective function; (2) Approaches that try to interpret and understand the internal representation of black-boxes models and (3) Approaches that try to interpret the predictions resulting from black-boxes models.In this context and taking part to these initiatives, this thesis aims at combining and unifying various approaches coming from different sub-fields of Artificial Intelligence (knowledge representation and reasoning, machine and deep-neural learning, computer vision and multimedia processing) to propose new approaches to learn more universal and more interpretable multimodal representations. A possible task to test and evaluate the contributions is the Visual Question Answering (VQA) problem, defined as the problem of automatically answering questions about images. Indeed, it is known as a excellent way to test the capabilities of a system to reason about entities and their relationships and it is often taken as the AI-complete task in both the context of explainable AI and the related context of universal representations.

LOCATION

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Vision & Ingénierie des Contenus (SAC)

Saclay

CONTACT PERSON

LE BORGNE Hervé

CEA

DRT/DIASI//LVIC

CEA Saclay - Nano-INNOVBat 861 - PC 173 - F91191 Gif Sur Yvette CedexFrance

Phone number: +33 (0)1 69 08 0152

Email: herve.le-borgne@cea.fr

UNIVERSITY / GRADUATE SCHOOL

Ecole Centrale Paris

Interfaces: Approches interdisciplinaires / fondements; applications et innovation

FIND OUT MORE

https://sites.google.com/site/hleborgne/

http://www.kalisteo.com/en/index.htm

START DATE

Start date on 01-09-2018

THESIS SUPERVISOR

HUDELOT Céline

CentraleSupelec

Laboratoire MICS

Centrale-SupélecCampus de Châtenay-Malabry

Phone number:

Email:

« The age limit is 26 years for PhD offers and 30 years old for post-doc offers. »

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