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Artykuły w czasopismach na temat "Réseaux de neurones sur graphes"
Lemieux, Vincent. "L'articulation des réseaux sociaux". Recherches sociographiques 17, nr 2 (12.04.2005): 247–60. http://dx.doi.org/10.7202/055716ar.
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 źródłaVazquez, J., M. François i D. Gilbert. "Gestion en temps réel d'un réseau d'assainissement : vérification de l'optimalité et de l'applicabilité de la théorie des graphes par rapport à la programmation linéaire mixte". Revue des sciences de l'eau 16, nr 4 (12.04.2005): 425–42. http://dx.doi.org/10.7202/705516ar.
Pełny tekst źródłaBélanger, 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 źródłaDechemi, N., T. Benkaci i A. Issolah. "Modélisation des débits mensuels par les modèles conceptuels et les systèmes neuro-flous". Revue des sciences de l'eau 16, nr 4 (12.04.2005): 407–24. http://dx.doi.org/10.7202/705515ar.
Pełny tekst źródłaVanderhaegen, Frédéric. "Pédagogie active et inclusive pour l’analyse de dangers de systèmes d’aide à la conduite basée sur la recherche de dissonances". J3eA 21 (2022): 2053. http://dx.doi.org/10.1051/j3ea/20222053.
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 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 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 źródłaBESNIER, Jean-Baptiste, Frédéric CHERQUI, Gilles CHUZEVILLE i Aurélie LAPLANCHE. "Amélioration de la connaissance patrimoniale des réseaux d’assainissement de la métropole de Lyon". TSM 12 2023, TSM 12 2023 (20.12.2023): 169–77. http://dx.doi.org/10.36904/tsm/202312169.
Pełny tekst źródłaRozprawy doktorskie na temat "Réseaux de neurones sur graphes"
Pasdeloup, Bastien. "Extending convolutional neural networks to irregular domains through graph inference". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0048/document.
Pełny tekst źródłaThis manuscript sums up our work on extending convolutional neuralnetworks to irregular domains through graph inference. It consists of three main chapters, each giving the details of a part of a methodology allowing the definition of such networks to process signals evolving on graphs with unknown structures.First, graph inference from data is explored, in order to provide a graph modeling the support of the signals to classify. Second, translation operators that preserve neighborhood properties of the vertices are identified on the inferred graph. Third, these translations are used to shift a convolutional kernel on the graph in order to define a convolutional neural network that is adapted to the input data.We have illustrated our methodology on a dataset of images. While not using any particular knowledge on the signals, we have been able to infer a graph that is close to a grid. Translations on this graph resemble Euclidean translations. Therefore, this has allowed us to define an adapted convolutional neural network that is very close what one would obtain when using the information that signals are images. This network, trained on the initial data, has out performed state of the art methods by more than 13 points, while using a very simple and easily improvable architecture.The method we have introduced is a generalization of convolutional neural networks. As a matter of fact, they can be seen as aparticularization of our approach in the case where the graph is a grid. Our work thus opens the way to numerous perspectives, as it provides an efficient way to build networks that are adapted to the data
Rosar, Kós Lassance Carlos Eduardo. "Graphs for deep learning representations". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0204.
Pełny tekst źródłaIn recent years, Deep Learning methods have achieved state of the art performance in a vast range of machine learning tasks, including image classification and multilingual automatic text translation. These architectures are trained to solve machine learning tasks in an end-to-end fashion. In order to reach top-tier performance, these architectures often require a very large number of trainable parameters. There are multiple undesirable consequences, and in order to tackle these issues, it is desired to be able to open the black boxes of deep learning architectures. Problematically, doing so is difficult due to the high dimensionality of representations and the stochasticity of the training process. In this thesis, we investigate these architectures by introducing a graph formalism based on the recent advances in Graph Signal Processing (GSP). Namely, we use graphs to represent the latent spaces of deep neural networks. We showcase that this graph formalism allows us to answer various questions including: ensuring generalization abilities, reducing the amount of arbitrary choices in the design of the learning process, improving robustness to small perturbations added to the inputs, and reducing computational complexity
Cattai, Tiziana. "Leveraging brain connectivity networks to detect mental states during motor imagery". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS081.
Pełny tekst źródłaThe brain is a complex network and we know that inter-areal synchronization and de-synchronization mechanisms are crucial to perform motor and cognitive tasks. Nowadays, brain functional interactions are studied in brain-computer interface BCI) applications with more and more interest. This might have strong impact on BCI systems, typically based on univariate features which separately characterize brain regional activities. Indeed, brain connectivity features can be used to develop alternative BCIs in an effort to improve performance and to extend their real-life applicability. The ambition of this thesis is the investigation of brain functional connectivity networks during motor imagery (MI)-based BCI tasks. It aims to identify complex brain functioning, re-organization processes and time-varying dynamics, at both group and individual level. This thesis presents different developments that sequentially enrich an initially simple model in order to obtain a robust method for the study of functional connectivity networks. Experimental results on simulated and real EEG data recorded during BCI tasks prove that our proposed method well explains the variegate behaviour of brain EEG data. Specifically, it provides a characterization of brain functional mechanisms at group level, together with a measure of the separability of mental conditions at individual level. We also present a graph denoising procedure to filter data which simultaneously preserve the graph connectivity structure and enhance the signal-to-noise ratio. Since the use of a BCI system requires a dynamic interaction between user and machine, we finally propose a method to capture the evolution of time-varying data. In essence, this thesis presents a novel framework to grasp the complexity of graph functional connectivity during cognitive tasks
Corne, Christophe. "Parallélisation de réseaux de neurones sur architecture distribuée". Mulhouse, 1999. http://www.theses.fr/1999MULH0583.
Pełny tekst źródłaCarboni, 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
Alvado, Ludovic. "Neurones artificiels sur silicium : une évolution vers les réseaux". Bordeaux 1, 2003. http://www.theses.fr/2003BOR12674.
Pełny tekst źródłaThis thesis describes a new approach for modelling biological neuron networks. This approach uses analogue specific integrated circuit (ASIC) in which Hodgkin-Huxley formalism as been implemented to integrate medium density artificial neural network, modelled at a biological realistic level. This thesis also deals with the component mismatches problem and the pertinent choice of optimized structure dedicated to network applications
Bissery, Christophe. "La détection centralisée des fuites sur les réseaux d'eau potable par réseaux de neurones". Lyon, INSA, 1994. http://www.theses.fr/1994ISAL0112.
Pełny tekst źródłaFor few years, under the influence of the urban environment, the perception of dysfunction risk in technical systems and in particular in water supply networks has changed. The lack of risk doesn't exist and it's necessary to learn how to manage it. It's in this way that appears the need of centralized leakage detection on water supply networks, leaks that represent an important part of the dysfunction risk of water supply. This study proposes a centralized leakage detection system using a computerized neural network approach. The building method of learning bases and the sensors localization method are pointed out and developed. This study has showed that on a realistic network model results obtained with the centralized leakage detection system using a computerized neural network approach allowed experimentations on real networks. The study ends on the presentation of the working priorities for these real experimentations (and in particular the need of hourly water consumption previsions)
Faÿ, Armelle. "Sur la propagation de l'information dans les réseaux probabilistes". Paris 6, 1997. http://www.theses.fr/1997PA066770.
Pełny tekst źródłaWang, Shengrui. "Réseaux multicouches de neurones artificiels : algorithmes d'apprentissage, implantations sur hypercube : applications". Phd thesis, Grenoble INPG, 1989. http://tel.archives-ouvertes.fr/tel-00335818.
Pełny tekst źródłaLaflaquière, Arnaud. "Neurones artificiels sur silicium : conception analogique et construction de réseaux hybrides". Bordeaux 1, 1998. http://www.theses.fr/1998BOR10617.
Pełny tekst źródłaKsiążki na temat "Réseaux de neurones sur graphes"
Seidou, Ousmane. Modélisation de la croissance de glace de lac par réseaux de neurones artificiels et estimation du volume de la glace abandonnée sur les berges des réservoirs hydroélectriques pendant les opérations d'hiver. Québec, QC: INRS--ETE, 2005.
Znajdź pełny tekst źródłaCzęści książek na temat "Réseaux de neurones sur graphes"
BYTYN, 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 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 źródłaCOGRANNE, Rémi, Marc CHAUMONT i Patrick BAS. "Stéganalyse : détection d’information cachée dans des contenus multimédias". W Sécurité multimédia 1, 261–303. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9026.ch8.
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 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 źródłaATIEH, Mirna, Omar MOHAMMAD, Ali SABRA i Nehme RMAYTI. "IdO, apprentissage profond et cybersécurité dans la maison connectée : une étude". W Cybersécurité des maisons intelligentes, 215–56. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9086.ch6.
Pełny tekst źródłaStreszczenia konferencji na temat "Réseaux de neurones sur graphes"
Gresse, 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 źródłaGendrot, Cedric, Emmanuel Ferragne i Anaïs Chanclu. "Analyse phonétique de la variation inter-locuteurs au moyen de réseaux de neurones convolutifs : voyelles seules et séquences courtes de parole". W XXXIVe Journées d'Études sur la Parole -- JEP 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/jep.2022-94.
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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