Search results

Synopsis deep learning methods have become increasingly popular for building intelligent systems. Currently, many deep learning architectures constitute the state of the art in their respective domains, such as image recognition, text generation, speech recognition, etc. The availability of mature libraries and frameworks to develop such systems is also a key factor in this success.This work explores the use of these architectures to build intelligent systems for sign languages. The creation of large sign language data corpora has made it possible to train deep learning architectures from scratch. The contributions presented in this work cover all aspects of the development of an intelligent system based on deep learning. A first contribution is the creation of a database for the Langue des Signes de Belgique Francophone (LSFB). This is derived from an existing corpus and has been adapted to the needs of deep learning methods. The possibility of using crowdsourcing methods to collect more data is also explored.The second contribution is the development or adaptation of architectures for automatic sign language recognition. The use of contrastive methods to learn better representations is explored, and the transferability of these representations to other sign languages is assessed.Finally, the last contribution is the integration of models into software for the general public. This led to a reflection on the challenges of integrating an intelligent module into the software development life cycle.Jury membersProf. Wim VANHOOF, President, University of NamurProf. Benoît FRENAY, Promoter, University of NamurProf. Anthony CLEVE, Co-promoter, University of NamurProf. Laurence MEURANT, Internal Member, University of NamurProf. Lorenzo BARALDI, External Member, University of ModenaProf. Annelies BRAFFORT, External Member, University of Paris-SaclayProf. Joni DAMBRE, External Member, University of GhentYou are cordially invited to a drink, which will follow the public defense. For a good organization, please give your answer by Friday June 6. I want to register

On May 10, 2025, Vice-Rector Carine Michiels and Professor Anne-Catherine Heuskin handed over UNamur's cheque at the grand closing evening of Operation Télévie, which this year raised a record €13,351,977 for the Fonds National de la Recherche Scientifique. Télévie funds are used entirely to finance cancer research projects at universities in the Wallonia-Brussels Federation and the Grand Duchy of Luxembourg.

For Bruno Dumas, computer science fits in with the principles of applied psychology Artificial intelligence (AI) is interfering in our professional as well as our private lives. It both seduces and worries us. On a global scale, it is at the heart of major strategic, societal or economic issues, still being debated in mid-February 2025, at the AI World Summit in Paris. But how can we, as users, avoid being subjected to it? How can we gain access to the necessary transparency of its workings? By placing his research prism on the user's side, Bruno Dumas is something of a "computer psychologist". An expert in human-computer interaction, co-president of the NaDI Institute (Namur Digital Institute), he defends the idea of a reasoned and enlightened use of emerging technologies.

The program Interventions by experts and researchers in the field of data science, , AI, digital twins, digital law and participatory processes.Registrations on the Vivre la Ville... website.

Thomas Balligand, now a lecturer at UNamur, combines his passion for basic research with teaching in histology and cytology. After a diverse background in internal medicine and research, notably at Harvard, he is dedicated to training the next generation of scientists while pursuing his work on nanobodies and their potential in immunotherapy. His desire to awaken scientific curiosity in his students illuminates his new role at the university..

The program 2:00 pm | Keynote lecture on the use of AI in research - Hugues BERSINI, Professor at the Université libre de Bruxelles: "Can science be just data driven?" 3:00 pm | Presentations by UNamur researchers3:00 pm | Catherine Guirkinger: Use of AI in an economic history project3:15 pm | Nicolas Roy (PI: Alexandre Mayer): AI at the service of innovation in photonics and optics: revealing the secrets of scrolls through the classification of animal species15:25 | Nemanja Antonic (PI: Elio Tuci): An in silico representation of C. elegans collective behaviour<15h35 | Nicolas Franco : The benefits and dangers of "predicting the future" with covid-like machine learning models 15h45 | Michel Ajzen : Managerial and human implications of AI in organizations <15h55 | Robin Ghyselinck (PI : Bruno Dumas) : Deep Learning for endoscopy: towards next generation computer-aided diagnosis4:05 pm | Auguste Debroise (PI : Guilhem Cassan) : LLMs to measure the importance of stereotypes within gender representations in Hollywood films16h15 | Gabriel Dias De Carvalho : Learning practices in physics using generative AI16h25 | Sébastien Dujardin (PI : Catherine Linard) : Where Geography meets AI: A case study on mapping online flood conversations16h35 | Jeremy Dodeigne : LLMs in SHS: revolutionary tools in a Wild West Territory? Reflections on costs, transparency and open science16h45 | Antoinette Rouvroy : Governing AI in Democracy17h00 | Keynote lecture on ethics and guidelines to consider when using AI in research projects and writing research articles - Bettina BERENDT, Professor at KU Leuven18h00 | Benoît Frenay and Michaël Lobet : Creation of an IA scientific committee at UNamur18:10 | DrinkA certificate of attendance, worth 0.5 cross-disciplinary doctoral training credits, will be issued on request. Contact: secretariat.adre@unamur.beThis event is free of charge, but registration is required. I want to register

In September 1961, a few professors and fifteen students inaugurated the Faculty of Economic and Social Sciences at the University of Namur. Later renamed the Faculté des sciences économiques, sociales et de gestion, or FSESG, in over 60 years of existence, it has trained thousands of students who have become experts and decision-makers in key fields: economics, management, communication and political science. In September 2024, it changed its name to EMCP or Faculté Économie Management Communication sciencesPo. A change of name, symbol of a visionary mutation.

The workshop will feature:A keynote presentation on public value and AI implementation at VRT.Sessions on discoverability, user agency, and explainability.Discussions on regulation, including perspectives on the AI Act and transparency in media.An interactive session showingcasing AI-driven prototypes.The event will also highlight our project's latest findings. Join us for a day of thought-provoking discussions, knowledge exchange, and networking opportunities!Would you like to attend? Places are limited and will be allocated on a first-come, first-served basis, so register as soon as possible. Registration will close on April 11, 2025. More information here

Researchers at the Universities of Namur (UNamur), Brussels (ULB) and Liège (ULiège) have just taken a key step towards understanding viral mechanisms. Their study, published in the international scientific journal PLOS Pathogens, focuses on a particular type of molecule produced by viruses, circular RNAs, and presents an innovative bioinformatics tool capable of better identifying them.

Hepatocellular carcinoma is the most common primary liver cancer. Unfortunately, this tumor still has a high mortality rate due to the lack of effective treatments for its most advanced or poorly localized forms. As part of a partnership with the CHU UCL Namur - site de Godinne and with the support of Roche Belgium, researchers in the Department of Biomedical Sciences are trying to understand why liver tumor cells are so resistant to treatment, and to identify therapeutic alternatives to better target them.

Abstract In recent decades, the volume of data generated worldwide has grown exponentially, significantly accelerating advancements in machine learning. This explosion of data has led to an increased need for effective data exploration techniques, giving rise to a specialized field known as dimensionality reduction. Dimensionality reduction methods are used to transform high-dimensional data into a low-dimensional space (typically 2D or 3D), so that it can be easily visualized and understood by humans. Algorithms such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE) have become essential tools for visualizing complex datasets. These techniques play a critical role in exploratory data analysis and in interpreting complex models like Convolutional Neural Networks (CNNs). Despite their widespread adoption, dimensionality reduction techniques, particularly non-linear ones, often lack interpretability. This opacity makes it difficult for users to understand the meaning of the visualizations or the rationale behind specific low-dimensional representations. In contrast, the field of supervised machine learning has seen significant progress in explainable AI (XAI), which aims to clarify model decisions, especially in high-stakes scenarios. While many post-hoc explanation tools have been developed to interpret the outputs of supervised models, there is still a notable gap in methods for explaining the results of dimensionality reduction techniques. This research investigates how post-hoc explanation techniques can be integrated into dimensionality reduction algorithms to improve user understanding of the resulting visualizations. Specifically, it explores how interpretability methods originally developed for supervised learning can be adapted to explain the behavior of non-linear dimensionality reduction algorithms. Additionally, this work examines whether the integration of post-hoc explanations can enhance the overall effectiveness of data exploration. As these tools are intended for end-users, we also design and evaluate an interactive system that incorporates explanatory mechanisms. We argue that combining interpretability with interactivity significantly improves users' understanding of embeddings produced by non-linear dimensionality reduction techniques. In this research, we propose enhancements to an existing post-hoc explanation method that adapts LIME for t-SNE. We introduce a globally-local framework for fast and scalable explanations of t-SNE embeddings. Furthermore, we present a completely new approach that adapts saliency map-based explanations to locally interpret non-linear dimensionality reduction results. Lastly, we introduce our interactive tool, Insight-SNE, which integrates our gradient-based explanation method and enables users to explore low-dimensional embeddings through direct interaction with the explanations.. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Bruno Dumas - University of Namur, BelgiumProf. John Lee - University of Louvain, BelgiumProf. Luis Galarraga - University of Rennes, France The public defense will be followed by a reception.Registration required. I want to register

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan The public defense will be followed by a reception.Registration required. I want to register