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Статті в журналах з теми "Réseau neuronal en graphes"
Lemieux, Vincent. "L'articulation des réseaux sociaux." Recherches sociographiques 17, no. 2 (April 12, 2005): 247–60. http://dx.doi.org/10.7202/055716ar.
Повний текст джерелаDíaz Villalba, Alejandro. "Comment outiller l’étude des autorités avec l’analyse de réseaux dans les grammaires françaises des XVIe et XVIIe siècles." SHS Web of Conferences 138 (2022): 03003. http://dx.doi.org/10.1051/shsconf/202213803003.
Повний текст джерелаPry, René. "TND et intelligence : une approche en réseau." Enfance N° 2, no. 2 (June 20, 2024): 83–100. http://dx.doi.org/10.3917/enf2.242.0083.
Повний текст джерелаVazquez, J., M. François, and 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, no. 4 (April 12, 2005): 425–42. http://dx.doi.org/10.7202/705516ar.
Повний текст джерелаMésangeau, Julien. "Articuler graphes et représentations d’utilisateurs d’un réseau socionumérique : retours sur une méthodologie d’entretien." Sciences de la société, no. 92 (December 1, 2014): 143–59. http://dx.doi.org/10.4000/sds.1172.
Повний текст джерелаSeifi, Massoud. "Visualisation interactive multi-échelle des grands graphes. Application à un réseau de blogs." Revue d'intelligence artificielle 26, no. 4 (August 30, 2012): 351–68. http://dx.doi.org/10.3166/ria.26.351-368.
Повний текст джерелаPigeon, Émilie. "Réseaux sociaux catholiques et construction identitaire dans les Pays d’en haut : l’exemple du fort Michilimackinac (1741-1821)." Francophonies d'Amérique, no. 40-41 (March 8, 2018): 83–112. http://dx.doi.org/10.7202/1043699ar.
Повний текст джерелаFiamma, M. N., Z. Samara, B. Quenet, G. Horcholle-Bossavit, I. Rivals, L. Personnaz, T. Similowski, and C. Straus. "142 Modélisation de la ventilation pulmonaire épisodique du têtard par réseau neuronal." Revue des Maladies Respiratoires 23, no. 5 (November 2006): 588. http://dx.doi.org/10.1016/s0761-8425(06)71970-2.
Повний текст джерелаRosé, Isabelle. "Autour de la reine Emma (vers 890-934): Réseaux, itinéraire biographique féminin et questions documentaires au début du Moyen Âge central." Annales. Histoire, Sciences Sociales 73, no. 4 (December 2018): 817–47. http://dx.doi.org/10.1017/ahss.2019.92.
Повний текст джерелаBenaïssa, Ibtissem. "Analogie du transport neuronal au transport électronique en nanotechnologie." Journal of Renewable Energies 12, no. 1 (October 26, 2023): 9–28. http://dx.doi.org/10.54966/jreen.v12i1.115.
Повний текст джерелаДисертації з теми "Réseau neuronal en graphes"
Albano, Alice. "Dynamique des graphes de terrain : analyse en temps intrinsèque." Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066260.
Повний текст джерелаWe are surrounded by a multitude of interaction networks from different contexts. These networks can be modeled as graphs, called complex networks. They have a community structure, i.e. groups of nodes closely related to each other and less connected with the rest of the graph. An other phenomenon studied in complex networks in many contexts is diffusion. The spread of a disease is an example of diffusion. These phenomena are dynamic and depend on an important parameter, which is often little studied: the time scale in which they are observed. According to the chosen scale, the graph dynamics can vary significantly. In this thesis, we propose to study dynamic processes using a suitable time scale. We consider a notion of relative time which we call intrinsic time, opposed to "traditional" time, which we call extrinsic time. We first study diffusion phenomena using intrinsic time, and we compare our results with an extrinsic time scale. This allows us to highlight the fact that the same phenomenon observed at two different time scales can have a very different behavior. We then analyze the relevance of the use of intrinsic time scale for detecting dynamic communities. Comparing communities obtained according extrinsic and intrinsic scales shows that the intrinsic time scale allows a more significant detection than extrinsic time scale
Albano, Alice. "Dynamique des graphes de terrain : analyse en temps intrinsèque." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066260/document.
Повний текст джерелаWe are surrounded by a multitude of interaction networks from different contexts. These networks can be modeled as graphs, called complex networks. They have a community structure, i.e. groups of nodes closely related to each other and less connected with the rest of the graph. An other phenomenon studied in complex networks in many contexts is diffusion. The spread of a disease is an example of diffusion. These phenomena are dynamic and depend on an important parameter, which is often little studied: the time scale in which they are observed. According to the chosen scale, the graph dynamics can vary significantly. In this thesis, we propose to study dynamic processes using a suitable time scale. We consider a notion of relative time which we call intrinsic time, opposed to "traditional" time, which we call extrinsic time. We first study diffusion phenomena using intrinsic time, and we compare our results with an extrinsic time scale. This allows us to highlight the fact that the same phenomenon observed at two different time scales can have a very different behavior. We then analyze the relevance of the use of intrinsic time scale for detecting dynamic communities. Comparing communities obtained according extrinsic and intrinsic scales shows that the intrinsic time scale allows a more significant detection than extrinsic time scale
Wang, Lianfa. "Improving the confidence of CFD results by deep learning." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLM008.
Повний текст джерелаComputational Fluid Dynamics (CFD) has become an indispensable tool for studying complex flow phenomena in both research and industry over the years. The accuracy of CFD simulations depends on various parameters – geometry, mesh, schemes, solvers, etc. – as well as phenomenological knowledge that only an expert CFD engineer can configure and optimize. The objective of this thesis is to propose an AI assistant to help users, whether they are experts or not, to better choose simulation options and ensure the reliability of results for a target flow phenomenon. In this context, deep learning algorithms are explored to identify the characteristics of flows computed on structured and unstructured meshes of complex geometries. Initially, convolutional neural networks (CNNs), known for their ability to extract patterns from im-ages, are used to identify flow phenomena such as vortices and thermal stratification on structured 2D meshes. Although the results obtained on structured meshes are satisfactory, CNNs can only be applied to structured meshes. To overcome this limitation, a graph-based neural network (GNN) framework is proposed. This framework uses the U-Net architecture and a hierarchy of successively refined graphs through the implementation of a multigrid method (AMG) inspired by the one used in the Code_Saturne CFD code. Subsequently, an in-depth study of kernel functions was conducted according to identification accuracy and training efficiency criteria to better filter the different phenomena on unstructured meshes. After comparing available kernel functions in the literature, a new kernel function based on the Gaussian mixture model was proposed. This function is better suited to identifying flow phenomena on unstructured meshes. The superiority of the proposed architecture and kernel function is demonstrated by several numerical experiments identifying 2D vortices and its adaptability to identifying the characteristics of a 3D flow
Aracena, Julio. "Modèles mathématiques discrets associées à des systèmes biologiques : applications aux réseaux de régulation génétique." Université Joseph Fourier (Grenoble ; 1971-2015), 2001. http://www.theses.fr/2001GRE10215.
Повний текст джерелаAracena, Julio. "Modèles mathématiques discrets associées à des systèmes biologiques : applications aux réseaux de régulation génétique." Université Joseph Fourier (Grenoble), 2001. http://www.theses.fr/2001GRE1A004.
Повний текст джерелаTiano, Donato. "Learning models on healthcare data with quality indicators." Electronic Thesis or Diss., Lyon 1, 2022. http://www.theses.fr/2022LYO10182.
Повний текст джерелаTime series are collections of data obtained through measurements over time. The purpose of this data is to provide food for thought for event extraction and to represent them in an understandable pattern for later use. The whole process of discovering and extracting patterns from the dataset is carried out with several extraction techniques, including machine learning, statistics, and clustering. This domain is then divided by the number of sources adopted to monitor a phenomenon. Univariate time series when the data source is single and multivariate time series when the data source is multiple. The time series is not a simple structure. Each observation in the series has a strong relationship with the other observations. This interrelationship is the main characteristic of time series, and any time series extraction operation has to deal with it. The solution adopted to manage the interrelationship is related to the extraction operations. The main problem with these techniques is that they do not adopt any pre-processing operation on the time series. Raw time series have many undesirable effects, such as noisy points or the huge memory space required for long series. We propose new data mining techniques based on the adoption of the most representative features of time series to obtain new models from the data. The adoption of features has a profound impact on the scalability of systems. Indeed, the extraction of a feature from the time series allows for the reduction of an entire series to a single value. Therefore, it allows for improving the management of time series, reducing the complexity of solutions in terms of time and space. FeatTS proposes a clustering method for univariate time series that extracts the most representative features of the series. FeatTS aims to adopt the features by converting them into graph networks to extract interrelationships between signals. A co-occurrence matrix merges all detected communities. The intuition is that if two time series are similar, they often belong to the same community, and the co-occurrence matrix reveals this. In Time2Feat, we create a new multivariate time series clustering. Time2Feat offers two different extractions to improve the quality of the features. The first type of extraction is called Intra-Signal Features Extraction and allows to obtain of features from each signal of the multivariate time series. Inter-Signal Features Extraction is used to obtain features by considering pairs of signals belonging to the same multivariate time series. Both methods provide interpretable features, which makes further analysis possible. The whole time series clustering process is lighter, which reduces the time needed to obtain the final cluster. Both solutions represent the state of the art in their field. In AnomalyFeat, we propose an algorithm to reveal anomalies from univariate time series. The characteristic of this algorithm is the ability to work among online time series, i.e. each value of the series is obtained in streaming. In the continuity of previous solutions, we adopt the functionality of revealing anomalies in the series. With AnomalyFeat, we unify the two most popular algorithms for anomaly detection: clustering and recurrent neural network. We seek to discover the density area of the new point obtained with clustering
Faucheux, Cyrille. "Segmentation supervisée d'images texturées par régularisation de graphes." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4050/document.
Повний текст джерелаIn this thesis, we improve a recent image segmentation algorithm based on a graph regularization process. The goal of this method is to compute an indicator function that satisfies a regularity and a fidelity criteria. Its particularity is to represent images with similarity graphs. This data structure allows relations to be established between similar pixels, leading to non-local processing of the data. In order to improve this approach, combine it with another non-local one: the texture features. Two solutions are developped, both based on Haralick features. In the first one, we propose a new fidelity term which is based on the work of Chan and Vese and is able to evaluate the homogeneity of texture features. In the second method, we propose to replace the fidelity criteria by the output of a supervised classifier. Trained to recognize several textures, the classifier is able to produce a better modelization of the problem by identifying the most relevant texture features. This method is also extended to multiclass segmentation problems. Both are applied to 2D and 3D textured images
Limnios, Stratis. "Graph Degeneracy Studies for Advanced Learning Methods on Graphs and Theoretical Results Edge degeneracy: Algorithmic and structural results Degeneracy Hierarchy Generator and Efficient Connectivity Degeneracy Algorithm A Degeneracy Framework for Graph Similarity Hcore-Init: Neural Network Initialization based on Graph Degeneracy." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX038.
Повний текст джерелаExtracting Meaningful substructures from graphs has always been a key part in graph studies. In machine learning frameworks, supervised or unsupervised, as well as in theoretical graph analysis, finding dense subgraphs and specific decompositions is primordial in many social and biological applications among many others.In this thesis we aim at studying graph degeneracy, starting from a theoretical point of view, and building upon our results to find the most suited decompositions for the tasks at hand.Hence the first part of the thesis we work on structural results in graphs with bounded edge admissibility, proving that such graphs can be reconstructed by aggregating graphs with almost-bounded-edge-degree. We also provide computational complexity guarantees for the different degeneracy decompositions, i.e. if they are NP-complete or polynomial, depending on the length of the paths on which the given degeneracy is defined.In the second part we unify the degeneracy and admissibility frameworks based on degree and connectivity. Within those frameworks we pick the most expressive, on the one hand, and computationally efficient on the other hand, namely the 1-edge-connectivity degeneracy, to experiment on standard degeneracy tasks, such as finding influential spreaders.Following the previous results that proved to perform poorly we go back to using the k-core but plugging it in a supervised framework, i.e. graph kernels. Thus providing a general framework named core-kernel, we use the k-core decomposition as a preprocessing step for the kernel and apply the latter on every subgraph obtained by the decomposition for comparison. We are able to achieve state-of-the-art performance on graph classification for a small computational cost trade-off.Finally we design a novel degree degeneracy framework for hypergraphs and simultaneously on bipartite graphs as they are hypergraphs incidence graph. This decomposition is then applied directly to pretrained neural network architectures as they induce bipartite graphs and use the coreness of the neurons to re-initialize the neural network weights. This framework not only outperforms state-of-the-art initialization techniques but is also applicable to any pair of layers convolutional and linear thus being applicable however needed to any type of architecture
Lachaud, Guillaume. "Extensions and Applications of Graph Neural Networks." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS434.
Повний текст джерелаGraphs are used everywhere to represent interactions between entities, whether physical such as atoms, molecules or people, or more abstract such as cities, friendships, ideas, etc. Amongst all the methods of machine learning that can be used, the recent advances in deep learning have made graph neural networks the de facto standard for graph representation learning. This thesis can be divided in two parts. First, we review the theoretical underpinnings of the most powerful graph neural networks. Second, we explore the challenges faced by the existing models when training on real world graph data. The powerfulness of a graph neural network is defined in terms of its expressiveness, i.e., its ability to distinguish non isomorphic graphs; or, in an equivalent manner, its ability to approximate permutation invariant and equivariant functions. We distinguish two broad families of the most powerful models. We summarise the mathematical properties as well as the advantages and disadvantages of these models in practical situations. Apart from the choice of the architecture, the quality of the graph data plays a crucial role in the ability to learn useful representations. Several challenges are faced by graph neural networks given the intrinsic nature of graph data. In contrast to typical machine learning methods that deal with tabular data, graph neural networks need to consider not only the features of the nodes but also the interconnectedness between them. Due to the connections between nodes, training neural networks on graphs can be done in two settings: in transductive learning, the model can have access to the test features in the training phase; in the inductive setting, the test data remains unseen. We study the differences in terms of performance between inductive and transductive learning for the node classification task. Additionally, the features that are fed to a model can be noisy or even missing. In this thesis we evaluate these challenges on real world datasets, and we propose a novel architecture to perform missing data imputation on graphs. Finally, while graphs can be the natural way to describe interactions, other types of data can benefit from being converted into graphs. In this thesis, we perform preliminary work on how to extract the most important parts of skin lesion images that could be used to create graphs and learn hidden relations in the data
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
Книги з теми "Réseau neuronal en graphes"
Ilg, Uwe J. Dynamics of Visual Motion Processing: Neuronal, Behavioral, and Computational Approaches. Boston, MA: Springer Science+Business Media, LLC, 2010.
Знайти повний текст джерелаS, Weigend Andreas, and Gershenfeld Neil A, eds. Time series prediction: Forecasting the future and understanding the past : proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis, held in Santa Fe, New Mexico, May 14-17, 1992. Reading, MA: Addison-Wesley Pub. Co., 1994.
Знайти повний текст джерелаHawkins, Jeff. Intelligence. Paris: CampusPress, 2005.
Знайти повний текст джерелаRojas, Raúl. Neural networks: A systematic introduction. Berlin: Springer-Verlag, 1996.
Знайти повний текст джерелаWatson, Mark. Programming in Scheme: Learn Scheme through artificial intelligence programs. New York: Springer, 1996.
Знайти повний текст джерела1961-, Fiesler Emile, and Beale R, eds. Handbook of neural computation. Bristol: Institute of Physics Pub., 1997.
Знайти повний текст джерелаAntony, Browne, ed. Neural network perspectives on cognition and adaptive robotics. Bristol: Institute of Physics Pub., 1997.
Знайти повний текст джерелаLynn, Nadel, ed. Neural connections, mental computation. Cambridge, Mass: MIT Press, 1990.
Знайти повний текст джерелаDayhoff, Judith E. Neural network architectures: An introduction. New York, N.Y: Van Nostrand Reinhold, 1990.
Знайти повний текст джерелаWaldrop, M. Mitchell. Complexity: The emerging science at the edge of order and chaos. London: Viking, 1993.
Знайти повний текст джерелаЧастини книг з теми "Réseau neuronal en graphes"
Chan, M., and A. Herrera. "Détection de la baisse de vigilance par réseau neuronal." In Vigilance et transports, 375–79. Presses universitaires de Lyon, 1995. http://dx.doi.org/10.4000/books.pul.40172.
Повний текст джерелаZumbühl, Heinz J., and Samuel U. Nussbaumer. "24. Réseau neuronal et fluctuations des glaciers dans les Alpes occidentales." In Des climats et des hommes, 391–403. La Découverte, 2012. http://dx.doi.org/10.3917/dec.berge.2012.01.0391.
Повний текст джерелаBRÜCKERHOFF-PLÜCKELMANN, Frank, Johannes FELDMANN, and Wolfram PERNICE. "Les puces photoniques." In Au-delà du CMOS, 395–422. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9127.ch9.
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