Résultats de recherche

Participez à notre matinée portes ouvertes Compte tenu des travaux dans la rue de Bruxelles et de la rénovation d’une partie des parkings de l’Université, nous vous invitons à privilégier autant que possible les transports en commun (train ou bus) pour rejoindre Namur. L’UNamur bénéficie d’une localisation idéale, au cœur de la ville à cinq minutes de marche des gares TEC et SNCB.Si vous venez en voiture, consultez le plan des parkings mis à votre disposition. Au plaisir de vous rencontrer le samedi 29 juin !

The workshop provides a forum for the discussion of new research directions and applications in Artificial Life, Evolutionary Computation and in related fields, where different disciplines and research areas could effectively meet. It was first held in 2007 in Sampieri (Ragusa), as the incorporation of two separate workshops (WIVA and GSICE). 

Au programme pour tous 09h30 | Cérémonie d'accueil à l'amphithéâtre Vauban.11h00 | Célébration de la rentrée à la Cathédrale Saint-Aubain suivie de l'accueil des étudiants par les Cercles. En savoir plus

JuryDr. Gilles PERROUIN, Président, Université de NamurProf. Anthony CLEVE, Promoteur, Université de NamurProf. Benoît VANDEROSE, Membre interne, Université de NamurProf. Xavier DEVROEY, Membre interne, Université de NamurProf. Serge DEMEYER, Membre externe, Université d’AnversProf. Michele LANZA, Membre externe, Université de la Suisse ItalienneVous êtes cordialement invités à un drink, qui suivra la soutenance publique.You are kindly invited to a drink, which will follow the public defense.Pour une bonne organisation, merci de vous inscrire pour le lundi 19 août au plus tard.For a good organization, please register by Monday, August 19.

JuryProf. Wim VANHOOF, Président, Université de NamurProf. Benoit FRENAY, Promoteur, Université de NamurDr. Alexandre MAYER, Co-Promoteur, Université de NamurDr. Gilles PERROUIN, Membre interne, Université de NamurDr. Paul TEMPLE, Membre externe, Université de RennesProf. John A. LEE, Membre externe, Université de LouvainVous êtes cordialement invités à un drink, qui suivra la soutenance publique.You are kindly invited to a drink, which will follow the public defense.Pour une bonne organisation, merci vous inscrire pour le mardi 3 septembre au plus tard.For a good organization, please register by Tuesday, Septembre 3. 

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, Japon

Abstract Plastic pollution has emerged as a pervasive environmental threat, with micro- and nanoplastics (MPs and NPs) accumulating across ecosystems and organisms, including humans. Their ability to adsorb and transport contaminants raises critical concerns for both environmental and public health.This thesis investigates the developmental neurotoxicity of MPs and NPs in zebrafish (Danio rerio), emphasizing the influence of particle size and mixture toxicity. NPs were shown to cross the embryonic chorion, disrupt physiological functions, and induce anxiety-like behaviour, whereas MPs mainly altered gene expression related to neurodevelopment. When co-exposed with methylmercury (MeHg), NPs enhanced MeHg accumulation in the brain and sensory organs, exacerbating its neurotoxic effects. Notably, the mixture induced severe hypoactivity, impaired lipid metabolism and neurotransmission, and increased mortality.These findings highlight the critical need to assess plastic particle toxicity not only in isolation but also in environmentally relevant mixtures. NPs, due to their small size and high reactivity, may act as vectors for toxicants like MeHg, amplifying their effects during sensitive developmental stages. Jury Prof. Frédéric SILVESTRE (UNamur), PrésidentProf. Patrick KESTEMONT (UNamur), SecrétaireDr Valérie CORNET (UNamur)Prof. Eli THORÉ (UNamur)Prof. Jérôme CACHOT (Université de Bordeaux)Dr Krishna DAS (Université de Liège)

Abstract Detecting semantic code clones in logic programs is a longstanding challenge, due to the lack of a unified definition of semantic similarity and the diversity of syntactic expressions that can represent similar behaviours. This thesis introduces a formal and flexible framework for semantic clone detection based on Constrained Horn Clauses (CHC). The approach considers two predicates as semantic clones if they can be independently transformed, via semantics-preserving program transformations, into a common third predicate. At the core of the method lies anti-unification, a process that computes the most specific generalisation of two predicates by identifying their shared structural patterns. The framework is parametric in regard with the allowed program transformations, the notion of generality, and the so-called quality estimators that steer the anti-unification process. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Katrien Beuls - University of Namur, BelgiumProf. Jean-Marie Jacquet - University of Namur, BelgiumProf. Temur Kutsia - Johannes Kepler University, AustriaProf. Frédéric Mesnard - University of the Reunion, Reunion IslandProf. Paul Van Eecke - Free University of Brussels, Belgium

Au programme pour tous et toutes 09h00 | Accueil09h30 | Cérémonie d'accueil des nouveaux étudiants11h00 | Célébration de la rentrée à la Cathédrale Saint-Aubain (Place Saint-Aubain - 5000 Namur) puis accueil des étudiants par les Cercles. En savoir plus

BNAIC/BeNeLearn 2025 will be held at the University of Namur under the auspices of the Belgian-Dutch Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS). The conference aims at presenting an overview of state-of-the-art research in artificial intelligence and machine learning in Belgium, The Netherlands, and Luxembourg. More information and registration

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