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Artykuły w czasopismach na temat "Réseaux de neurones pour graphes"
Bélanger, M., N. El-Jabi, D. Caissie, F. Ashkar i J. M. Ribi. "Estimation de la température de l'eau de rivière en utilisant les réseaux de neurones et la régression linéaire multiple". Revue des sciences de l'eau 18, nr 3 (12.04.2005): 403–21. http://dx.doi.org/10.7202/705565ar.
Pełny tekst źródłaDalud-Vincent, Monique. "Une autre manière de modéliser les réseaux sociaux. Applications à l’étude de co-publications". Nouvelles perspectives en sciences sociales 12, nr 2 (22.08.2017): 41–68. http://dx.doi.org/10.7202/1040904ar.
Pełny tekst źródłaBonnet, Nicolas. "Résilience d’un territoire face au chômage : les réseaux d’entreprises innovantes sur Montpellier". Nouvelles perspectives en sciences sociales 5, nr 1 (23.11.2009): 97–115. http://dx.doi.org/10.7202/038625ar.
Pełny tekst źródła-GLORENNEC, Pierre-Yves. "Réseaux de neurones et logique floue pour la transitique". Revue de l'Electricité et de l'Electronique -, nr 06 (1995): 26. http://dx.doi.org/10.3845/ree.1995.062.
Pełny tekst źródłaOtman, Gabriel. "Les bases de connaissances terminologiques : les banques de terminologie de seconde génération". Meta 42, nr 2 (30.09.2002): 244–56. http://dx.doi.org/10.7202/003772ar.
Pełny tekst źródłaTacnet, Jean-Marc, Elodie Forestier, Eric Mermet, Corinne Curt i Frédéric Berger. "Résilience territoriale : du concept à l'analyse d'infrastructures critiques en montagne". La Houille Blanche, nr 5-6 (październik 2018): 20–28. http://dx.doi.org/10.1051/lhb/2018047.
Pełny tekst źródłaKOSTEK, B. "Application des réseaux de neurones pour l'analyse de l'articulation musicale". Le Journal de Physique IV 04, nr C5 (maj 1994): C5–597—C5–600. http://dx.doi.org/10.1051/jp4:19945127.
Pełny tekst źródłaPigeon, Émilie. "Réseaux sociaux catholiques et construction identitaire dans les Pays d’en haut : l’exemple du fort Michilimackinac (1741-1821)". Francophonies d'Amérique, nr 40-41 (8.03.2018): 83–112. http://dx.doi.org/10.7202/1043699ar.
Pełny tekst źródłaBenbouhenni, Habib. "Commande DTC cinq niveaux à 24 secteurs basée sur les réseaux de neurones de la MAS de forte puissance". Journal of Renewable Energies 21, nr 3 (30.09.2018): 373–84. http://dx.doi.org/10.54966/jreen.v21i3.696.
Pełny tekst źródłaLOTFI, Siham, i Hicham MESK. "Prévision de Défaillance Des entreprises : Apport des Réseaux de Neurones Artificiels". International Journal of Financial Accountability, Economics, Management, and Auditing (IJFAEMA) 3, nr 3 (1.06.2021): 70–79. http://dx.doi.org/10.52502/ijfaema.v3i3.53.
Pełny tekst źródłaRozprawy doktorskie na temat "Réseaux de neurones pour graphes"
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.
Pełny tekst źródłaThe 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
Hérault, Laurent. "Réseaux de neurones récursifs pour l'optimisation combinatoire : application à la théorie des graphes et à la vision par ordinateur". Grenoble INPG, 1991. http://www.theses.fr/1991INPG0019.
Pełny tekst źródłaHafidi, Hakim. "Robust machine learning for Graphs/Networks". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT004.
Pełny tekst źródłaThis 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
Boulnois, Philippe. "Contribution à l'étude de différentes architectures de réseaux de neurones artificiels réalisant une transcription graphèmes-phonèmes pour le français". Compiègne, 1994. http://www.theses.fr/1994COMPD675.
Pełny tekst źródłaHammadi, Youssef. "Réduction d'un modèle 0D instationnaire et non-linéaire de thermique habitacle pour l’optimisation énergétique des véhicules automobiles". Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM027.
Pełny tekst źródłaThe use of automotive air conditioning leads to a fuel overconsumption. To reduce this overconsumption, we can either work upstream on the technical definitions of the cabin and the HVAC system or optimize control strategies. In both cases, it is essential to build a cabin thermal model that well balances accuracy and complexity. This is the topic of this PhD thesis driven by Renault Group. First, a model reduction methodology is used to build a 0D model starting from a 3D finite element cabin thermal model. This 0D model is based on mass and energy balances on the different cabin walls and air zones. It consists of a nonlinear differential algebraic equations system which can be reinterpreted as a Bond Graph. In addition, the 0D model is based on a weak coupling between the thermal equations and the fluid mechanics ones resulting from CFD calculations (internal airflow and external aerodynamics). Secondly, we apply a machine learning method to the data generated by the 0D model in order to build a reduced 0D model. A design of experiment is considered at this stage. Due to the nonlinearity of the heat exchanges, we have developed an approach which is inspired by the Gappy POD and EIM methods. We use a multiphysics reduced basis that takes several contributions into account (temperatures, enthalpies, heat fluxes and humidities). The resulting reduced model is a hybrid model that couples some of the original physical equations to an artificial neural network. The reduction methodology has been validated on Renault vehicles. The reduced order models have been integrated into a vehicle system-level energetic simulation platform (GREEN) which models different thermics (engine, transmission, cooling system, battery, HVAC, refrigerant circuit, underhood) in order to perform thermal management studies which are of particular importance for electric and hybrid vehicles. The reduced order models have been validated on several scenarios (temperature control for thermal comfort, driving cycles, HVAC coupling) and have achieved CPU gains of up to 99% with average errors of 0.5 °C on temperatures and 0.6% on relative humidities
Maktoobi, Sheler. "Couplage diffractif pour réseaux de neurones optiques". Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD019.
Pełny tekst źródłaPhotonic networks with high performance can be considered as substrates for future computing systems. In comparison with electronics, photonic systems have substantial privileges, for instance the possibility of a fully parallel implementation of networks. Recently, neural networks have moved into the center of attention of the photonic community. One of the most important requirements for parallel large-scale photonic networks is to realize the connectivities. Diffraction is considered as a method to process the connections between the nodes (coupling) in optical neural networks. In the current thesis, we evaluate the scalability of a diffractive coupling in more details as follow:First, we begin with a general introductions for artificial intelligence, machine learning, artificial neural network and photonic neural networks. To establish a working neural network, learning rules are an essential part to optimize a configuration for obtaining a low error from the system, hence learning rules are introduced (Chapter 1). We investigate the fundamental concepts of diffractive coupling in our spatio-temporal reservoir. In that case, theory of diffraction is explained. We use an analytical scheme to provide the limits for the size of diffractive networks which is a part of our photonic neural network (Chapter 2). The concepts of diffractive coupling are investigated experimentally by two different experiments to confirm the analytical limits and to obtain maximum number of nodes which can be coupled in the photonic network (Chapter 3). Numerical simulations for such an experimental setup is modeled in two different schemes to obtain the maximum size of network numerically, which approaches a surface of 100 mm2 (Chapter 4). Finally, the complete photonic neural network is demonstrated. We design a spatially extended reservoir for 900 nodes. Consequently, our system generalizes the prediction for the chaotic Mackey–Glass sequence (Chapter 5)
Ouali, Jamel. "Architecture intégrée flexible pour réseaux de neurones". Grenoble INPG, 1991. http://www.theses.fr/1991INPG0035.
Pełny tekst źródłaFernandez, Brillet Lucas. "Réseaux de neurones CNN pour la vision embarquée". Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM043.
Pełny tekst źródłaRecently, Convolutional Neural Networks have become the state-of-the-art soluion(SOA) to most computer vision problems. In order to achieve high accuracy rates, CNNs require a high parameter count, as well as a high number of operations. This greatly complicates the deployment of such solutions in embedded systems, which strive to reduce memory size. Indeed, while most embedded systems are typically in the range of a few KBytes of memory, CNN models from the SOA usually account for multiple MBytes, or even GBytes in model size. Throughout this thesis, multiple novel ideas allowing to ease this issue are proposed. This requires to jointly design the solution across three main axes: Application, Algorithm and Hardware.In this manuscript, the main levers allowing to tailor computational complexity of a generic CNN-based object detector are identified and studied. Since object detection requires scanning every possible location and scale across an image through a fixed-input CNN classifier, the number of operations quickly grows for high-resolution images. In order to perform object detection in an efficient way, the detection process is divided into two stages. The first stage involves a region proposal network which allows to trade-off recall for the number of operations required to perform the search, as well as the number of regions passed on to the next stage. Techniques such as bounding box regression also greatly help reduce the dimension of the search space. This in turn simplifies the second stage, since it allows to reduce the task’s complexity to the set of possible proposals. Therefore, parameter counts can greatly be reduced.Furthermore, CNNs also exhibit properties that confirm their over-dimensionment. This over-dimensionement is one of the key success factors of CNNs in practice, since it eases the optimization process by allowing a large set of equivalent solutions. However, this also greatly increases computational complexity, and therefore complicates deploying the inference stage of these algorithms on embedded systems. In order to ease this problem, we propose a CNN compression method which is based on Principal Component Analysis (PCA). PCA allows to find, for each layer of the network independently, a new representation of the set of learned filters by expressing them in a more appropriate PCA basis. This PCA basis is hierarchical, meaning that basis terms are ordered by importance, and by removing the least important basis terms, it is possible to optimally trade-off approximation error for parameter count. Through this method, it is possible to compress, for example, a ResNet-32 network by a factor of ×2 both in the number of parameters and operations with a loss of accuracy <2%. It is also shown that the proposed method is compatible with other SOA methods which exploit other CNN properties in order to reduce computational complexity, mainly pruning, winograd and quantization. Through this method, we have been able to reduce the size of a ResNet-110 from 6.88Mbytes to 370kbytes, i.e. a x19 memory gain with a 3.9 % accuracy loss.All this knowledge, is applied in order to achieve an efficient CNN-based solution for a consumer face detection scenario. The proposed solution consists of just 29.3kBytes model size. This is x65 smaller than other SOA CNN face detectors, while providing equal detection performance and lower number of operations. Our face detector is also compared to a more traditional Viola-Jones face detector, exhibiting approximately an order of magnitude faster computation, as well as the ability to scale to higher detection rates by slightly increasing computational complexity.Both networks are finally implemented in a custom embedded multiprocessor, verifying that theorical and measured gains from PCA are consistent. Furthermore, parallelizing the PCA compressed network over 8 PEs achieves a x11.68 speed-up with respect to the original network running on a single PE
Bigot, Pascal. "Utilisation des réseaux de neurones pour la télégestion des réseaux techniques urbains". Lyon 1, 1995. http://www.theses.fr/1995LYO10036.
Pełny tekst źródłaBénédic, Yohann. "Approche analytique pour l'optimisation de réseaux de neurones artificiels". Phd thesis, Université de Haute Alsace - Mulhouse, 2007. http://tel.archives-ouvertes.fr/tel-00605216.
Pełny tekst źródłaKsiążki na temat "Réseaux de neurones pour graphes"
Kamp, Yves. Réseaux de neurones récursifs pour mémoires associatives. Lausanne: Presses polytechniques et universitaires romandes, 1990.
Znajdź pełny tekst źródłaPersonnaz, L. Réseaux de neurones formels pour la modélisation, la commande et la classification. Paris: CNRS Editions, 2003.
Znajdź pełny tekst źródłaAmat, Jean-Louis. Techniques avancées pour le traitement de l'information: Réseaux de neurones, logique floue, algorithmes génétiques. Wyd. 2. Toulouse: Cépaduès-Ed., 2002.
Znajdź pełny tekst źródłaAmat, G. J. l. ;. Yahiaoui. Techniques avancées pour le traitement de l'information: Réseaux de neurones, logique floue, algorithmes génétiques. CEPADUES, 1995.
Znajdź pełny tekst źródłaCzęści książek na temat "Réseaux de neurones pour graphes"
Martaj, Dr Nadia, i Dr Mohand Mokhtari. "Réseaux de neurones". W MATLAB R2009, SIMULINK et STATEFLOW pour Ingénieurs, Chercheurs et Etudiants, 807–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11764-0_17.
Pełny tekst źródłaMOLINIER, Matthieu, Jukka MIETTINEN, Dino IENCO, Shi QIU i Zhe ZHU. "Analyse de séries chronologiques d’images satellitaires optiques pour des applications environnementales". W Détection de changements et analyse des séries temporelles d’images 2, 125–74. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch4.
Pełny tekst źródłaBYTYN, Andreas, René AHLSDORF i Gerd ASCHEID. "Systèmes multiprocesseurs basés sur un ASIP pour l’efficacité des CNN". W Systèmes multiprocesseurs sur puce 1, 93–111. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9021.ch4.
Pełny tekst źródłaATTO, Abdourrahmane M., Héla HADHRI, Flavien VERNIER i Emmanuel TROUVÉ. "Apprentissage multiclasse multi-étiquette de changements d’état à partir de séries chronologiques d’images". W Détection de changements et analyse des séries temporelles d’images 2, 247–71. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch6.
Pełny tekst źródłaATTO, Abdourrahmane M., Fatima KARBOU, Sophie GIFFARD-ROISIN i Lionel BOMBRUN. "Clustering fonctionnel de séries d’images par entropies relatives". W Détection de changements et analyse des séries temporelles d’images 1, 121–38. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch4.
Pełny tekst źródłaDE’ FAVERI TRON, Alvise. "La détection d’intrusion au moyen des réseaux de neurones : un tutoriel". W Optimisation et apprentissage, 211–47. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch8.
Pełny tekst źródłaZHANG, Hanwei, Teddy FURON, Laurent AMSALEG i Yannis AVRITHIS. "Attaques et défenses de réseaux de neurones profonds : le cas de la classification d’images". W Sécurité multimédia 1, 51–85. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9026.ch2.
Pełny tekst źródłaStreszczenia konferencji na temat "Réseaux de neurones pour graphes"
Fourcade, A. "Apprentissage profond : un troisième oeil pour les praticiens". W 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601014.
Pełny tekst źródłaGresse, Adrien, Richard Dufour, Vincent Labatut, Mickael Rouvier i Jean-François Bonastre. "Mesure de similarité fondée sur des réseaux de neurones siamois pour le doublage de voix". W XXXIIe Journées d’Études sur la Parole. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/jep.2018-2.
Pełny tekst źródłaORLIANGES, Jean-Christophe, Younes El Moustakime, Aurelian Crunteanu STANESCU, Ricardo Carrizales Juarez i Oihan Allegret. "Retour vers le perceptron - fabrication d’un neurone synthétique à base de composants électroniques analogiques simples". W Les journées de l'interdisciplinarité 2023. Limoges: Université de Limoges, 2024. http://dx.doi.org/10.25965/lji.761.
Pełny tekst źródłaKim, Lila, i Cédric Gendrot. "Classification automatique de voyelles nasales pour une caractérisation de la qualité de voix des locuteurs par des réseaux de neurones convolutifs". W XXXIVe Journées d'Études sur la Parole -- JEP 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/jep.2022-82.
Pełny tekst źródłaQuintas, Sebastião, Alberto Abad, Julie Mauclair, Virginie Woisard i Julien Pinquier. "Utilisation de réseaux de neurones profonds avec attention pour la prédiction de l’intelligibilité de la parole de patients atteints de cancers ORL". W XXXIVe Journées d'Études sur la Parole -- JEP 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/jep.2022-7.
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