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Centro de investigación ofrece experiencia en aprendizaje automático, aprendizaje profundo y sistemas de recomendación para comercio electrónico, publicidad, juegos y otras aplicaciones informáticas

Resumen

Tipo:
Oferta Tecnológica
Referencia:
TOFR20161028002
Publicado:
15/11/2016
Caducidad:
15/11/2017
Resumen:
Un equipo de investigación de un centro de I+D francés especializado en ciencias informáticas ofrece experiencia técnica en aprendizaje secuencial, toma de decisiones bajo incertidumbre, problemas ante ataques, dilemas de exploración/explotación y aprendizaje por refuerzo, predicción y aprendizaje profundo. El equipo de investigación transfiere experiencia y resultados de investigación a compañías (start-ups, pymes y multinacionales) en campos tan diversos como sanidad, transporte, energía, comunicaciones, seguridad y protección de privacidad, ciudades inteligentes y fábricas del futuro. Se buscan socios industriales y académicos con el fin de establecer acuerdos de cooperación técnica o investigación y participar en proyectos del programa H2020.

Details

Tittle:
Expertise offered in machine learning, deep learning and recommendation systems for ecommerce, advertising, gaming and other ICT applications
Summary:
A research team of a French R-Y-D center dedicated to computational sciences offers its technical expertise in sequential learning, decision making under uncertainty, bandit problems, exploration/exploitation dilemma and reinforcement learning, prediction and deep learning. It offers to industry and academia a collaboration under a research or a technical cooperation agreement. It is also interested in collaborations under H2020 projects.
Description:
The French research team is part of a research center dedicated to computational sciences The Institute is organized in project teams bringing together researchers with complementary skills to focus on specific scientific projects. With this open, agile model, it is able to explore original approaches with its partners in industry and academia. It transfers expertise and research results to companies (startups, SMEs and major multinationals) in fields as diverse as healthcare, transport, energy, communications, security and privacy protection, smart cities and the factory of the future.

In this line, the research team focuses on the task of learning in artificial systems (either hardware robots, or software agents) that gather information over a long period of time.

The term sequential refers to two aspects:
- the sequential acquisition of data, from which a model is learned (supervised and non-supervised learning),
- the sequential decision making task, based on the learned model (reinforcement learning).

Examples of sequential learning problems include:
- Supervised learning: tasks deal with the prediction of some response given a certain set of observations of input variables and responses. New sample points keep on being observed.
- Unsupervised learning: tasks deal with clustering objects, these latter making a flow of objects. The (unknown) number of clusters typically evolves during time, as new objects are observed.
- Reinforcement learning: tasks deal with the control (a policy) of some system which has to be optimized. It is not assumed that the availability of a model of the system is controlled.

The team works on investigating how to create a software that, in some way, adapts to its users or its environment more generally, by learning. It is therefore specialized in studying the sequential learning algorithms with focus on reinforcement and bandit learning.

Application domains include:
- Recommendation systems in a broad sense: systems that aim at providing personalized responses/items to users, based on their characteristics, and the environment in which the interaction happens. Furthermore, the group conducts new work aiming to introduce deep learning in recommender systems.
- Spoken dialog systems: working particularly on machine learning techniques such as reinforcement and imitation learning to optimize this specific sequential decision making under uncertainty, as dialogue management module must take sequences of decisions in unknown, noisy and hard to model environments.
- Adaptive/learning systems more generally: systems that adapt their behavior to their environment such as educative tutoring systems; adaptive heating systems in buildings; players that adapt their strength to that of their human opponent; bioreactors, for example.
- Prediction in general: prediction for web-server load in a non-stationary environment or prediction of a bug in a software code, for example.

The group´s work spans from theory of learnability, to the design of efficient algorithms, to concrete applications.

It offers to collaborate under research or technical cooperation agreements with industry and academia. It is also interested in joining existing consortia in EU projects including H2020 projects.
Advantages and Innovations:
- Joint project team of distinguished scientists from several specialized science institutions
- Part of a collaborative research programme with innovative SMEs enhancing technology transfer
- Experience working with the industry - from startups to multinationals
- Experience in funding projects (FP7, H2020)
- Award-winning applications and solution developed for clients.
Stage of Development:
Under development/lab tested
IPs:
Other

Partner sought

Type and Role of Partner Sought:
Partners can be from the industry, ranging from startups to multinationals, as well as from academia.

They can have projects requiring research on learning in artificial systems including in the e-commerce, the IT, the gaming or the advertising industries, among other sectors.

The partner should already have a set of data (text, images or sound) in order for the research team to be able to work.

Challenges partners face may be related to recommendation systems providing some content based on a request, which is either explicit (such as a set of keywords given to a search engine), or implicit (such as the mere fact that the target visits a certain webpage), human-machine interactions, the design of systems that adapt their behavior to their environment or prediction in general.

The French research group can work on the development of applications.

It is also interested in collaborating under H2020 projects where it can have added value.

The research group offers to collaborate under technical or research cooperation agreements.

Client

Type and Size of Client:
R&D Institution
Already Engaged in Trans-National Cooperation:
Si
Languages Spoken:
English
French

Keywords

Technology Keywords:
01003003 Artificial Intelligence (AI)
01001001 Automation, Robotics Control Systems
01003014 Internet Technologies/Communication (Wireless, Bluetooth)
01003 Procesado de información, sistemas de información, gestión de la carga de trabajo