Artificial Intelligence

The striking progress of statistical learning over the past decade has led to wild expectations about the ability of machines to reproduce human behavior.

Labo bcom - intelligence artificielle
© Fred Pieau
    These Artificial Intelligence techniques can apply equally to masses of data compiled in a computing center, or to data captured and processed by a smartphone. They are now entering a domestication phase, where technological, legal, and ethical questions are being asked in order to offer responsible AI approaches.

    The Artificial Intelligence lab designs solutions to address issues in future networks, computer vision, cyberdefense, and e-health. Designing embedded imaging and radio communication applications, anomaly detection or classification, system resilience, human factor management, pathology prediction, and reliable diagnostics are the main challenges being tackled. To do so, the lab relies on traditional disciplines of automated language, signal, image processing, 3D vision, and dynamic programming, whose potential has been compounded by statistical learning. It can also count on its skills in hardware/software engineering and a legal team. Its methodological approach makes its algorithms explainable and robust so as to facilitate their certification.

    Stéphane Paquelet - bcom
    © Fred Pieau

    Stéphane Paquelet

    Artificial Intelligence lab Manager

    With current learning techniques, we can automate tasks as varied as interpreting text, mapping space, generating realistic images, predicting behaviors, and planning actions.
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    Developing my competitiveness thanks to artificial intelligence

    scientific publications

    21.07.2021

    Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system

    Probabilistic record linkage is a process of combining data from different sources, when such data refer to common entities and identifying information is not available. Fellegi and Sunter proposed a probabilistic record linkage framework that takes into account multiple non-identifying information, but is limited to simple binary comparison between matching variables. In our work, we propose an…

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    29.05.2021

    Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition

    Recognizing first-person hand activity is a challenging task, especially when not enough data are available. In this paper, we tackle this challenge by proposing a new hybrid learning pipeline for skeleton-based hand activity recognition, which is composed of three blocks. First, for a given sequence of hand’s joint positions, the spatial features are extracted using a dedicated combination of…

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    13.08.2021

    Acceptability and 5G in the Medical Field: The Impact of the Level of Information

    The issues around 5G are considerable: sovereignty, smart city, industry 4.0, energy, connected healthcare. However, 5G is currently raising many questions from the general public and professionals. To better understand these questions related to acceptability, a quantitative experimental study was conducted with 81 healthcare professionals, via an online questionnaire. The objective is to…

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    01.09.2021

    Efficient channel charting via phase-insensitive distance computation

    Channel charting is an unsupervised learning task whose objective is to encode channels so that the obtained representation reflects the relative spatial locations of the corresponding users. It has many potential applications, ranging from user scheduling to proactive handover. In this paper, a channel charting method is proposed, based on a distance measure specifically designed to reduce the…

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