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Статті в журналах з теми "Réseaux neuronaux pour les graphs"
Sauteur, Tania. "Comment les cerveaux humains encodent-ils leurs propres processus d'apprentissage et de mémorisation et comment la topologie du réseau social élargi d'une personne présente-t-elle des schémas neuronaux similaires à ceux de ses ami-e-s et communautés ?" Cortica 2, no. 2 (September 19, 2023): 157–63. http://dx.doi.org/10.26034/cortica.2023.4208.
Повний текст джерелаRemzi, M., and B. Djavan. "Réseaux neuronaux artificiels pour la prise de décision en cancérologie urologique." Annales d'Urologie 41, no. 3 (June 2007): 110–15. http://dx.doi.org/10.1016/j.anuro.2007.04.003.
Повний текст джерелаBouaoune, Djahida, and Malika Dahmani-Megrerouche. "Reconstitution de données climatiques pour l’Algérie du Nord : application des réseaux neuronaux." Comptes Rendus Geoscience 342, no. 11 (November 2010): 815–22. http://dx.doi.org/10.1016/j.crte.2010.09.005.
Повний текст джерелаRaus, Rachele, Michela Tonti, Tania Cerquitelli, Luca Cagliero, Giuseppe Attanasio, Moreno La Quatra, and Salvatore Greco. "L’analyse du discours et l’intelligence artificielle pour réaliser une écriture inclusive : le projet EMIMIC." SHS Web of Conferences 138 (2022): 01007. http://dx.doi.org/10.1051/shsconf/202213801007.
Повний текст джерелаDonadille, C., R. Perisse, and P. H. Prevost. "Utilisation de réseaux neuronaux pour la prévision des courbes de transformation des aciers." Revue de Métallurgie 89, no. 10 (October 1992): 892–94. http://dx.doi.org/10.1051/metal/199289100892.
Повний текст джерелаSabil, Abdelkebir, Nathalie Raymond, and Marc Sapene. "Évaluation d’un algorithme utilisant les réseaux neuronaux pour la lecture automatique de la polygraphie." Médecine du Sommeil 17, no. 1 (March 2020): 43–44. http://dx.doi.org/10.1016/j.msom.2019.12.026.
Повний текст джерелаGirardeau, Gabrielle. "Rôle des rythmes cérébraux dans la fonction mnésique du sommeil." médecine/sciences 39, no. 11 (November 2023): 836–44. http://dx.doi.org/10.1051/medsci/2023160.
Повний текст джерелаHenchi, Kamel, Mario Fafard, Martin Talbot, and David Langis. "L’application des réseaux neuronaux artificiels pour l’identification et la détection de l’endommagement dans les ponts." Revue Européenne des Éléments Finis 7, no. 1-3 (January 1998): 257–72. http://dx.doi.org/10.1080/12506559.1998.11690478.
Повний текст джерелаMichel-Flutot, Pauline, and Stéphane Vinit. "La stimulation magnétique répétée pour le traitement des traumas spinaux." médecine/sciences 38, no. 8-9 (August 2022): 679–85. http://dx.doi.org/10.1051/medsci/2022108.
Повний текст джерелаLek, S., I. Dimopoulos, M. Derraz, and Y. El Ghachtoul. "Modélisation de la relation pluie-débit à l'aide des réseaux de neurones artificiels." Revue des sciences de l'eau 9, no. 3 (April 12, 2005): 319–31. http://dx.doi.org/10.7202/705255ar.
Повний текст джерелаДисертації з теми "Réseaux neuronaux pour les graphs"
Hafidi, Hakim. "Robust machine learning for Graphs/Networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT004.
Повний текст джерелаThis thesis addresses advancements in graph representation learning, focusing on the challengesand opportunities presented by Graph Neural Networks (GNNs). It highlights the significanceof graphs in representing complex systems and the necessity of learning node embeddings that capture both node features and graph structure. The study identifies key issues in GNNs, such as their dependence on high-quality labeled data, inconsistent performanceacross various datasets, and susceptibility to adversarial attacks.To tackle these challenges, the thesis introduces several innovative approaches. Firstly, it employs contrastive learning for node representation, enabling self-supervised learning that reduces reliance on labeled data. Secondly, a Bayesian-based classifier isproposed for node classification, which considers the graph’s structure to enhance accuracy. Lastly, the thesis addresses the vulnerability of GNNs to adversarialattacks by assessing the robustness of the proposed classifier and introducing effective defense mechanisms.These contributions aim to improve both the performance and resilience of GNNs in graph representation learning
Carboni, Lucrezia. "Graphes pour l’exploration des réseaux de neurones artificiels et de la connectivité cérébrale humaine." Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALM060.
Повний текст джерелаThe main objective of this thesis is to explore brain and artificial neural network connectivity from agraph-based perspective. While structural and functional connectivity analysis has been extensivelystudied in the context of the human brain, there is a lack of a similar analysis framework in artificialsystems.To address this gap, this research focuses on two main axes.In the first axis, the main objective is to determine a healthy signature characterization of the humanbrain resting state functional connectivity. To achieve this objective, a novel framework is proposed,integrating traditional graph statistics and network reduction tools, to determine healthy connectivitypatterns. Hence, we build a graph pair-wise comparison and a classifier to identify pathological statesand rank associated perturbed brain regions. Additionally, the generalization and robustness of theproposed framework were investigated across multiple datasets and variations in data quality.The second research axis explores the benefits of brain-inspired connectivity exploration of artificialneural networks (ANNs) in the future perspective of more robust artificial systems development. Amajor robustness issue in ANN models is represented by catastrophic forgetting when the networkdramatically forgets previously learned tasks when adapting to new ones. Our work demonstrates thatgraph modeling offers a simple and elegant framework for investigating ANNs, comparing differentlearning strategies, and detecting deleterious behaviors such as catastrophic forgetting.Moreover, we explore the potential of leveraging graph-based insights to effectively mitigatecatastrophic forgetting, laying a foundation for future research and explorations in this area
Khalife, Sammy. "Graphes, géométrie et représentations pour le langage et les réseaux d'entités." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX055.
Повний текст джерелаThe automated treatment of familiar objects, either natural or artifacts, always relies on a translation into entities manageable by computer programs. The choice of these abstract representations is always crucial for the efficiency of the treatments and receives the utmost attention from computer scientists and developers. However, another problem rises: the correspondence between the object to be treated and "its" representation is not necessarily one-to-one! Therefore, the ambiguous nature of certain discrete structures is problematic for their modeling as well as their processing and analysis with a program. Natural language, and in particular its textual representation, is an example. The subject of this thesis is to explore this question, which we approach using combinatorial and geometric methods. These methods allow us to address the problem of extracting information from large networks of entities and to construct representations useful for natural language processing.Firstly, we start by showing combinatorial properties of a family of graphs implicitly involved in sequential models. These properties essentially concern the inverse problem of finding a sequence representing a given graph. The resulting algorithms allow us to carry out an experimental comparison of different sequential models used in language modeling.Secondly, we consider an application for the problem of identifying named entities. Following a review of recent solutions, we propose a competitive method based on the comparison of knowledge graph structures which is less costly in annotating examples dedicated to the problem. We also establish an experimental analysis of the influence of entities from capital relations. This analysis suggests to broaden the framework for applying the identification of entities to knowledge bases of different natures. These solutions are used today in a software library in the banking sector.Then, we perform a geometric study of recently proposed representations of words, during which we discuss a geometric conjecture theoretically and experimentally. This study suggests that language analogies are difficult to transpose into geometric properties, and leads us to consider the paradigm of distance geometry in order to construct new representations.Finally, we propose a methodology based on the paradigm of distance geometry in order to build new representations of words or entities. We propose algorithms for solving this problem on some large scale instances, which allow us to build interpretable and competitive representations in performance for extrinsic tasks. More generally, we propose through this paradigm a new framework and research leadsfor the construction of representations in machine learning
Hubert, Nicolas. "Mesure et enrichissement sémantiques des modèles à base d'embeddings pour la prédiction de liens dans les graphes de connaissances." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0059.
Повний текст джерелаKnowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). This thesis specifically explores the advancement of KGEMs for the link prediction (LP) task, which is of utmost importance as it underpins several downstream applications such as recommender systems. In this thesis, various challenges around the use of KGEMs for LP are identified: the scarcity of semantically rich resources, the unidimensional nature of evaluation frameworks, and the lack of semantic considerations in prevailing machine learning-based approaches. Central to this thesis is the proposition of novel solutions to these challenges. Firstly, the thesis contributes to the development of semantically rich resources: mainstream datasets for link prediction are enriched using schema-based information, EducOnto and EduKG are proposed to overcome the paucity of resources in the educational domain, and PyGraft is introduced as an innovative open-source tool for generating synthetic ontologies and knowledge graphs. Secondly, the thesis proposes a new semantic-oriented evaluation metric, Sem@K, offering a multi-dimensional perspective on model performance. Importantly, popular models are reassessed using Sem@K, which reveals essential insights into their respective capabilities and highlights the need for multi-faceted evaluation frameworks. Thirdly, the thesis delves into the development of neuro-symbolic approaches, transcending traditional machine learning paradigms. These approaches do not only demonstrate improved semantic awareness but also extend their utility to diverse applications such as recommender systems. In summary, the present work not only redefines the evaluation and functionality of knowledge graph embedding models but also sets the stage for more versatile, interpretable AI systems, underpinning future explorations at the intersection of machine learning and symbolic reasoning
Bekkouch, Imad Eddine Ibrahim. "Auxiliary learning & Adversarial training pour les études des manuscrits médiévaux." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUL014.
Повний текст джерелаThis thesis is at the intersection of musicology and artificial intelligence, aiming to leverage AI to help musicologists with repetitive work, such as object searching in the museum's manuscripts. We annotated four new datasets for medieval manuscript studies: AMIMO, AnnMusiconis, AnnVihuelas, and MMSD. In the second part, we improve object detectors' performances using Transfer learning techniques and Few Shot Object Detection.In the third part, we discuss a powerful approach to Domain Adaptation, which is auxiliary learning, where we train the model on the target task and an extra task that allows for better stabilization of the model and reduces over-fitting.Finally, we discuss self-supervised learning, which does not use extra meta-data by leveraging the adversarial learning approach, forcing the model to extract domain-independent features
Mazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.
Повний текст джерелаNowadays, video contents are ubiquitous through the popular use of internet and smartphones, as well as social media. Many daily life applications such as video surveillance and video captioning, as well as scene understanding require sophisticated technologies to process video data. It becomes of crucial importance to develop automatic means to analyze and to interpret the large amount of available video data. In this thesis, we are interested in video action recognition, i.e. the problem of assigning action categories to sequences of videos. This can be seen as a key ingredient to build the next generation of vision systems. It is tackled with AI frameworks, mainly with ML and Deep ConvNets. Current ConvNets are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. ConvNets also rely on (max or average) pooling which reduces dimensionality of output layers (and hence attenuates their sensitivity to the availability of labeled data); however, this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained video representations, especially when the learned action categories are fine-grained
Aissa, Wafa. "Réseaux de modules neuronaux pour un raisonnement visuel compositionnel." Electronic Thesis or Diss., Paris, HESAM, 2023. http://www.theses.fr/2023HESAC033.
Повний текст джерелаThe context of this PhD thesis is compositional visual reasoning. When presented with an image and a question pair, our objective is to have neural networks models answer the question by following a reasoning chain defined by a program. We assess the model's reasoning ability through a Visual Question Answering (VQA) setup.Compositional VQA breaks down complex questions into modular easier sub-problems.These sub-problems include reasoning skills such as object and attribute detection, relation detection, logical operations, counting, and comparisons. Each sub-problem is assigned to a different module. This approach discourages shortcuts, demanding an explicit understanding of the problem. It also promotes transparency and explainability.Neural module networks (NMN) are used to enable compositional reasoning. The framework is based on a generator-executor framework, the generator learns the translation of the question to its function program. The executor instantiates a neural module network where each function is assigned to a specific module. We also design a neural modules catalog and define the function and the structure of each module. The training and evaluations are conducted using the pre-processed GQA dataset cite{gqa}, which includes natural language questions, functional programs representing the reasoning chain, images, and corresponding answers.The research contributions revolve around the establishment of an NMN framework for the VQA task.One primary contribution involves the integration of vision and language pre-trained (VLP) representations into modular VQA. This integration serves as a ``warm-start" mechanism for initializing the reasoning process.The experiments demonstrate that cross-modal vision and language representations outperform uni-modal ones. This utilization enables the capture of intricate relationships within each individual modality while also facilitating alignment between different modalities, consequently enhancing overall accuracy of our NMN.Moreover, we explore various training techniques to enhance the learning process and improve cost-efficiency. In addition to optimizing the modules within the reasoning chain to collaboratively produce accurate answers, we introduce a teacher-guidance approach to optimize the intermediate modules in the reasoning chain. This ensures that these modules perform their specific reasoning sub-tasks without taking shortcuts or compromising the reasoning process's integrity. We propose and implement several teacher-guidance techniques, one of which draws inspiration from the teacher-forcing method commonly used in sequential models. Comparative analyses demonstrate the advantages of our teacher-guidance approach for NMNs, as detailed in our paper [1].We also introduce a novel Curriculum Learning (CL) strategy tailored for NMNs to reorganize the training examples and define a start-small training strategy. We begin by learning simpler programs and progressively increase the complexity of the training programs. We use several difficulty criteria to define the CL approach. Our findings demonstrate that by selecting the appropriate CL method, we can significantly reduce the training cost and required training data, with only a limited impact on the final VQA accuracy. This significant contribution forms the core of our paper [2].[1] W. Aissa, M. Ferecatu, and M. Crucianu. Curriculum learning for compositional visual reasoning. In Proceedings of VISIGRAPP 2023, Volume 5: VISAPP, 2023.[2] W. Aissa, M. Ferecatu, and M. Crucianu. Multimodal representations for teacher-guidedcompositional visual reasoning. In Advanced Concepts for Intelligent Vision Systems, 21st International Conference (ACIVS 2023). Springer International Publishing, 2023.[3] D. A. Hudson and C. D. Manning. GQA: A new dataset for real-world visual reasoning and compositional question answering. 2019
Foucher, Christophe. "Analyse et amélioration d'algorithmes neuronaux et non neuronaux de quantification vectorielle pour la compression d'images." Rennes 1, 2002. http://www.theses.fr/2002REN10120.
Повний текст джерелаHenniges, Philippe. "PSO pour l'apprentissage supervisé des réseaux neuronaux de type fuzzy ARTMAP." Mémoire, École de technologie supérieure, 2006. http://espace.etsmtl.ca/508/1/HENNIGES_Pihilippe.pdf.
Повний текст джерелаQuélavoine, Régis. "Etude de l'apprentissage et des structures des réseaux de neurones multicouches pour l'analyse de données." Avignon, 1997. http://www.theses.fr/1997AVIG0002.
Повний текст джерелаКниги з теми "Réseaux neuronaux pour les graphs"
Kamp, Yves. Réseaux de neurones récursifs pour mémoires associatives. Lausanne: Presses polytechniques et universitaires romandes, 1990.
Знайти повний текст джерелаAmat, Jean-Louis. Techniques avancées pour le traitement de l'information: Réseaux de neurones, logique floue, algorithmes génétiques. 2nd ed. Toulouse: Cépaduès-Ed., 2002.
Знайти повний текст джерелаNielsen, Thomas D., and Finn V. Jensen. Bayesian Networks and Decision Graphs. Springer New York, 2010.
Знайти повний текст джерелаЧастини книг з теми "Réseaux neuronaux pour les graphs"
ZHANG, Hanwei, Teddy FURON, Laurent AMSALEG, and Yannis AVRITHIS. "Attaques et défenses de réseaux de neurones profonds : le cas de la classification d’images." In Sécurité multimédia 1, 51–85. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9026.ch2.
Повний текст джерелаBELMONTE, Romain, Pierre TIRILLY, Ioan Marius BILASCO, Nacim IHADDADENE, and Chaabane DJERABA. "Détection de points de repères faciaux par modélisation spatio-temporelle." In Analyse faciale en conditions non contrôlées, 105–49. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9111.ch3.
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