Literatura académica sobre el tema "Réseaux génératifs"
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Artículos de revistas sobre el tema "Réseaux génératifs"
Ruan, S., P. Vera, P. Decazes y R. Modzelewski. "RADIOGAN : réseaux de neurones profonds génératifs conditionnels pour la synthétisation d’images TEP au FDG". Médecine Nucléaire 44, n.º 2 (marzo de 2020): 105–6. http://dx.doi.org/10.1016/j.mednuc.2020.01.128.
Texto completoIvinza Lepapa, Alphonse-Christian. "Génération du millénaire et l'influence des réseaux sociaux sur l'exercice de la démocratie : L’exemple de l’Afrique et du Congo". Acta Europeana Systemica 6 (12 de julio de 2020): 31–40. http://dx.doi.org/10.14428/aes.v6i1.56793.
Texto completoDíaz 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.
Texto completoElbaz, Mikhaël. "Ethnicité et générations en Amérique du Nord. Le cas de la seconde génération de Juifs sépharades à Montréal". I. Vivre ailleurs, n.º 31 (22 de octubre de 2015): 63–77. http://dx.doi.org/10.7202/1033779ar.
Texto completoSerpantié, Jean-Pol y Jean-Marie Hamy. "Framatome, porteur d’innovations pour la GEN IV et les réacteurs avancés". Revue Générale Nucléaire, n.º 5 (septiembre de 2019): 38–41. http://dx.doi.org/10.1051/rgn/20195038.
Texto completoSoudani, Azeddine, Saadi Bougoul y Jean-Luc Harion. "Réduction des étalonnages multiples en mesures simultanées dans une couche limite turbulente d'un mélange air - hélium". Journal of Renewable Energies 6, n.º 2 (31 de diciembre de 2003): 77–94. http://dx.doi.org/10.54966/jreen.v6i2.963.
Texto completoConnidis, Ingrid. "Hal. L. Kendig, Editor, Ageing and Families: A Support Networks Perspective, Boston: Allen and Unwin. 1986. 227 pages. Index. ($29.95 CDN)." Canadian Journal on Aging / La Revue canadienne du vieillissement 8, n.º 2 (1989): 187–91. http://dx.doi.org/10.1017/s0714980800010898.
Texto completoBuhnila, Ioana, Georgeta Cislaru y Amalia Todirascu. "Analyse qualitative et quantitative des « hallucinations » générées automatiquement dans un corpus de reformulations médicales". SHS Web of Conferences 191 (2024): 11001. http://dx.doi.org/10.1051/shsconf/202419111001.
Texto completoMur, Jean-Michel. "Fibre optique et 5G… unies pour le meilleur". Photoniques, n.º 92 (julio de 2018): 38–41. http://dx.doi.org/10.1051/photon/20189238.
Texto completoSilva, Roberto Luiz y Thiago Ferreira Almeida. "L’influence de l’ecole des annales sur l’approche du tiers-monde du droit international (TWAIL)". Latin American Journal of Development 4, n.º 1 (9 de febrero de 2022): 108–28. http://dx.doi.org/10.46814/lajdv4n1-009.
Texto completoTesis sobre el tema "Réseaux génératifs"
Côté, Marc-Alexandre. "Réseaux de neurones génératifs avec structure". Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10489.
Texto completoAzeraf, Elie. "Classification avec des modèles probabilistes génératifs et des réseaux de neurones. Applications au traitement des langues naturelles". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. https://theses.hal.science/tel-03880848.
Texto completoMany probabilistic models have been neglected for classification tasks with supervised learning for several years, as the Naive Bayes or the Hidden Markov Chain. These models, called generative, are criticized because the induced classifier must learn the observations' law. This problem is too complex when the number of observations' features is too large. It is especially the case with Natural Language Processing tasks, as the recent embedding algorithms convert words in large numerical vectors to achieve better scores.This thesis shows that every generative model can define its induced classifier without using the observations' law. This proposition questions the usual categorization of the probabilistic models and classifiers and allows many new applications. Therefore, Hidden Markov Chain can be efficiently applied to Chunking and Naive Bayes to sentiment analysis.We go further, as this proposition allows to define the classifier induced from a generative model with neural network functions. We "neuralize" the models mentioned above and many of their extensions. Models so obtained allow to achieve relevant scores for many Natural Language Processing tasks while being interpretable, able to require little training data, and easy to serve
Franceschi, Jean-Yves. "Apprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS014.
Texto completoThe recent rise of deep learning has been motivated by numerous scientific breakthroughs, particularly regarding representation learning and generative modeling. However, most of these achievements have been obtained on image or text data, whose evolution through time remains challenging for existing methods. Given their importance for autonomous systems to adapt in a constantly evolving environment, these challenges have been actively investigated in a growing body of work. In this thesis, we follow this line of work and study several aspects of temporality and dynamical systems in deep unsupervised representation learning and generative modeling. Firstly, we present a general-purpose deep unsupervised representation learning method for time series tackling scalability and adaptivity issues arising in practical applications. We then further study in a second part representation learning for sequences by focusing on structured and stochastic spatiotemporal data: videos and physical phenomena. We show in this context that performant temporal generative prediction models help to uncover meaningful and disentangled representations, and conversely. We highlight to this end the crucial role of differential equations in the modeling and embedding of these natural sequences within sequential generative models. Finally, we more broadly analyze in a third part a popular class of generative models, generative adversarial networks, under the scope of dynamical systems. We study the evolution of the involved neural networks with respect to their training time by describing it with a differential equation, allowing us to gain a novel understanding of this generative model
Lavault, Antoine. "Generative Adversarial Networks for Synthesis and Control of Drum Sounds". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS614.
Texto completoAudio synthesizers are electronic systems capable of generating artificial sounds under parameters depending on their architecture. Even though multiple evolutions have transformed synthesizers from simple sonic curiosities in the 1960s and earlier to the main instruments in modern musical productions, two major challenges remain; the development of a system of sound synthesis with a parameter set coherent with its perception by a human and the design of a universal synthesis method, able to model any source and provide new original sounds. This thesis studies using and enhancing Generative Adversarial Networks (GAN) to build a system answering the previously-mentioned problems. The main objective is to propose a neural synthesizer capable of generating realistic drum sounds controllable by predefined timbre parameters and hit velocity. The first step in the project was to propose an approach based on the latest technological advances at the time of its conception to generate realistic drum sounds. We added timbre control capabilities to this method by exploring a different way from existing solutions, i.e., differentiable descriptors. To give experimental guarantees to our work, we performed evaluation experiments via objective metrics based on statistics and subjective and psychopĥysical evaluations on perceived quality and perception of control errors. These experiments continued to add velocity control to the timbral control. Still, with the idea of pursuing the realization of a versatile synthesizer with universal control, we have created a dataset ex-nihilo composed of drum sounds to create an exhaustive database of sounds accessible in the vast majority of conditions encountered in the context of music production. From this dataset, we present experimental results related to the control of dynamics, one of the critical aspects of musical performance but left aside by the literature. To justify the capabilities offered by the GANs synthesis method, we show that it is possible to marry classical synthesis methods with neural synthesis by exploiting the limits and particularities of GANs to obtain new and musically interesting hybrid sounds
Pagliarini, Silvia. "Modeling the neural network responsible for song learning". Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0107.
Texto completoDuring the first period of their life, babies and juvenile birds show comparable phases of vocal development: first, they listen to their parents/tutors in order to build a neural representation of the experienced auditory stimulus, then they start to produce sound and progressively get closer to reproducing their tutor song. This phase of learning is called the sensorimotor phase and is characterized by the presence of babbling, in babies, and subsong, in birds. It ends when the song crystallizes and becomes similar to the one produced by the adults.It is possible to find analogies between brain pathways responsible for sensorimotor learning in humans and birds: a vocal production pathway involves direct projections from auditory areas to motor neurons, and a vocal learning pathway is responsible for imitation and plasticity. The behavioral studies and the neuroanatomical structure of the vocal control circuit in humans and birds provide the basis for bio-inspired models of vocal learning.In particular, birds have brain circuits exclusively dedicated to song learning, making them an ideal model for exploring the representation of vocal learning by imitation of tutors.This thesis aims to build a vocal learning model underlying song learning in birds. An extensive review of the existing literature is discussed in the thesis: many previous studies have attempted to implement imitative learning in computational models and share a common structure. These learning architectures include the learning mechanisms and, eventually, exploration and evaluation strategies. A motor control function enables sound production and sensory response models either how sound is perceived or how it shapes the reward. The inputs and outputs of these functions lie (1)~in the motor space (motor parameters’ space), (2)~in the sensory space (real sounds) and (3)~either in the perceptual space (a low dimensional representation of the sound) or in the internal representation of goals (a non-perceptual representation of the target sound).The first model proposed in this thesis is a theoretical inverse model based on a simplified vocal learning model where the sensory space coincides with the motor space (i.e., there is no sound production). Such a simplification allows us to investigate how to introduce biological assumptions (e.g. non-linearity response) into a vocal learning model and which parameters influence the computational power of the model the most. The influence of the sharpness of auditory selectivity and the motor dimension are discussed.To have a complete model (which is able to perceive and produce sound), we needed a motor control function capable of reproducing sounds similar to real data (e.g. recordings of adult canaries). We analyzed the capability of WaveGAN (a Generative Adversarial Network) to provide a generator model able to produce realistic canary songs. In this generator model, the input space becomes the latent space after training and allows the representation of a high-dimensional dataset in a lower-dimensional manifold. We obtained realistic canary sounds using only three dimensions for the latent space. Among other results, quantitative and qualitative analyses demonstrate the interpolation abilities of the model, which suggests that the generator model we studied can be used as a motor function in a vocal learning model.The second version of the sensorimotor model is a complete vocal learning model with a full action-perception loop (i.e., it includes motor space, sensory space, and perceptual space). The sound production is performed by the GAN generator previously obtained. A recurrent neural network classifying syllables serves as the perceptual sensory response. Similar to the first model, the mapping between the perceptual space and the motor space is learned via an inverse model. Preliminary results show the influence of the learning rate when different sensory response functions are implemented
Yedroudj, Mehdi. "Steganalysis and steganography by deep learning". Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS095.
Texto completoImage steganography is the art of secret communication in order to exchange a secret message. In the other hand, image steganalysis attempts to detect the presence of a hidden message by searching artefacts within an image. For about ten years, the classic approach for steganalysis was to use an Ensemble Classifier fed by hand-crafted features. In recent years, studies have shown that well-designed convolutional neural networks (CNNs) can achieve superior performance compared to conventional machine-learning approaches.The subject of this thesis deals with the use of deep learning techniques for image steganography and steganalysis in the spatialdomain.The first contribution is a fast and very effective convolutional neural network for steganalysis, named Yedroudj-Net. Compared tomodern deep learning based steganalysis methods, Yedroudj-Net can achieve state-of-the-art detection results, but also takes less time to converge, allowing the use of a large training set. Moreover,Yedroudj-Net can easily be improved by using well known add-ons. Among these add-ons, we have evaluated the data augmentation, and the the use of an ensemble of CNN; Both increase our CNN performances.The second contribution is the application of deep learning techniques for steganography i.e the embedding. Among the existing techniques, we focus on the 3-player game approach.We propose an embedding algorithm that automatically learns how to hide a message secretly. Our proposed steganography system is based on the use of generative adversarial networks. The training of this steganographic system is conducted using three neural networks that compete against each other: the embedder, the extractor, and the steganalyzer. For the steganalyzer we use Yedroudj-Net, this for its affordable size, and for the fact that its training does not require the use of any tricks that could increase the computational time.This second contribution defines a research direction, by giving first reflection elements while giving promising first results
Grechka, Asya. "Image editing with deep neural networks". Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS683.pdf.
Texto completoImage editing has a rich history which dates back two centuries. That said, "classic" image editing requires strong artistic skills as well as considerable time, often in the scale of hours, to modify an image. In recent years, considerable progress has been made in generative modeling which has allowed realistic and high-quality image synthesis. However, real image editing is still a challenge which requires a balance between novel generation all while faithfully preserving parts of the original image. In this thesis, we will explore different approaches to edit images, leveraging three families of generative networks: GANs, VAEs and diffusion models. First, we study how to use a GAN to edit a real image. While methods exist to modify generated images, they do not generalize easily to real images. We analyze the reasons for this and propose a solution to better project a real image into the GAN's latent space so as to make it editable. Then, we use variational autoencoders with vector quantification to directly obtain a compact image representation (which we could not obtain with GANs) and optimize the latent vector so as to match a desired text input. We aim to constrain this problem, which on the face could be vulnerable to adversarial attacks. We propose a method to chose the hyperparameters while optimizing simultaneously the image quality and the fidelity to the original image. We present a robust evaluation protocol and show the interest of our method. Finally, we abord the problem of image editing from the view of inpainting. Our goal is to synthesize a part of an image while preserving the rest unmodified. For this, we leverage pre-trained diffusion models and build off on their classic inpainting method while replacing, at each denoising step, the part which we do not wish to modify with the noisy real image. However, this method leads to a disharmonization between the real and generated parts. We propose an approach based on calculating a gradient of a loss which evaluates the harmonization of the two parts. We guide the denoising process with this gradient
Hamis, Sébastien. "Compression de contenus visuels pour transmission mobile sur réseaux de très bas débit". Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS020.
Texto completoThe field of visual content compression (image, video, 2D/3D graphics elements) has known spectacular achievements for more than twenty years, with the emergence numerous international standards such as JPEG, JPEG2000 for still image compression, or MPEG-1/2/4 for video and 3D graphics content coding.The apparition of smartphones and of their related applications have also benefited from these advances, the image being today ubiquitous in a context of mobility. Nevertheless, image transmission requires reliable and available networks, since such visual data that are inherently bandwidth-intensive. While developed countries benefit today from high-performance mobile networks (3G, 4G...), this is not the case in a certain number of regions of the world, particularly in emerging countries, where communications still rely on 2G SMS networks. Transmitting visual content in such a context becomes a highly ambitious challenge, requiring the elaboration of new, for very low bitrate compression algorithm. The challenge is to ensure images transmission over a narrow bandwidth corresponding to a relatively small set (10 to 20) of SMS (140 bytes per SMS).To meet such constraints, multiple axes of development have been considered. After a state-of-the-art of traditional image compression techniques, we have oriented our research towards deep learning methods, aiming achieve post-treatments over strongly compressed data in order to improve the quality of the decoded content.Our contributions are structures around the creation of a new compression scheme, including existing codecs and a panel of post-processing bricks aiming at enhancing highly compressed content. Such bricks represent dedicated deep neural networks, which perform super-resolution and/or compression artifact reduction operations, specifically trained to meet the targeted objectives. These operations are carried out on the decoder side and can be interpreted as image reconstruction algorithms from heavily compressed versions. This approach offers the advantage of being able to rely on existing codecs, which are particularly light and resource-efficient. In our work, we have retained the BPG format, which represents the state of art in the field, but other compression schemes can also be considered.Regarding the type of neural networks, we have adopted Generative Adversarials Nets-GAN, which are particularly well-suited for objectives of reconstruction from incomplete data. Specifically, the two architectures retained and adapted to our objectives are the SRGAN and ESRGAN networks. The impact of the various elements and parameters involved, such as the super-resolution factors and the loss functions, are analyzed in detail.A final contribution concerns experimental evaluation performed. After showing the limitations of objective metrics, which fail to take into account the visual quality of the image, we have put in place a subjective evaluation protocol. The results obtained in terms of MOS (Mean Opinion Score) fully demonstrate the relevance of the proposed reconstruction approaches.Finally, we open our work to different use cases, of a more general nature. This is particularly the case for high-resolution image processing and for video compression
Kalainathan, Diviyan. "Generative Neural Networks to infer Causal Mechanisms : algorithms and applications". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS516.
Texto completoCausal discovery is of utmost importance for agents who must plan, reason and decide based on observations; where mistaking correlation with causation might lead to unwanted consequences. The gold standard to discover causal relations is to perform experiments.However, experiments are in many cases expensive, unethical, or impossible to realize. In these situations, there is a need for observational causal discovery, that is, the estimation of causal relations from observations alone.Causal discovery in the observational data setting traditionally involves making significant assumptions on the data and on the underlying causal model.This thesis aims to alleviate some of the assumptions made on the causal models by exploiting the modularity and expressiveness of neural networks for causal discovery, leveraging both conditional independences and simplicity of the causal mechanisms through two algorithms.Extensive experiments on both simulated and real-world data and a throughout theoretical anaylsis prove the good performance and the soundness of the proposed approaches
Villain, Benjamin. "Nouvelle génération de contrôleur d'accès réseau : une approche par réseaux logiciels". Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066663/document.
Texto completoThis thesis presents the importance of cross-layer network information for network applications in the context of network access control. The dissertation exposes a novel architecture in which a network access controller is mutualized in the Cloud. This architecture allows to address a key market segment for clients unwilling to buy expensive hardware to control their network. Multiple challenges come into play when hosting the controller remotely. Indeed cross-layer information are no longer available which prevents the controller from correctly controlling users activity. A first implementation to share cross-layer information is presented in chapter 2. It leverages specialized session border controllers to send these data in the application protocol, here HTTP. Then chapter 3 presents an innovative solution for the cross-layering problem which allows to intrumentalize network flows with SDN protocols. The solution focuses on a web portal redirection but is extendable to any kind of protocols. The implementation permits to intercept and modify flows in order to input cross-layer data within another network protocol. This solution was implemented in the OpenDaylight OpenFlow controller and shows great results. The mutualized approach coupled with the SDN cross-layer framework allow to build flexible networks with almost no configuration of on-site equipments. The central network controller reduces the overal cost of the solution by being mutualized among multiple clients. Moreover, having the ability to intrumentalize network traffic in software allows to implement any kind of custom behavior on the runtime
Libros sobre el tema "Réseaux génératifs"
Génération Y: Les jeunes et les réseaux sociaux, de la dérision à la subversion. 2a ed. Paris: Presses de la Fondation nationale des sciences politiques, 2013.
Buscar texto completoDagnaud, Monique. Génération Y: Les jeunes et les réseaux sociaux, de la dérision à la subversion. Paris: Presses de la Fondation nationale des sciences politiques, 2011.
Buscar texto completoMernissi, Fatima y Nadia Lamlili. Journalistes marocaines, Génération Dialogue: Atelier d'écriture animé par Fatema Mernissi. Rabat: Marsam, 2012.
Buscar texto completoStone, Leroy O. Les échanges de soutien entre les parents et les enfants et l'équité intergénérationnelle. Ottawa, Ont: Statistique Canada, Division des systèmes de soutien familiaux et sociaux, 1998.
Buscar texto completoBragagnini, Federico. L' intégration des immigrés italiens dans la Commune de Bassecourt (Canton du Jura, Suisse): Analyse du degré d'insertion des migrants des première et deuxième générations dans la société d'accueil à travers la participation aux réseaux formels et informels. Neuchâtel: Université de Neuchâtel, Institut de géographie, 1996.
Buscar texto completoYaozong, Luo, Huang, Beiling (guan li ke xue), Cai, Hongming (guan li ke xue) y Tapscott Don 1947-, eds. N shi dai chong zhuang: Wang lu xin ren lei zheng zai gai bian ni de shi jie. Tai bei shi: Mai ge luo xi er, 2009.
Buscar texto completoHolma, Harri y Antti Toskala. UMTS : Les réseaux mobiles de troisième génération. Osman Eyrolles Multimédia - OEM, 2001.
Buscar texto completoDuchenne, Geneviève y Michel Dumoulin. Générations de Fédéralistes Européens Depuis le XIXe Siècle: Individus, Groupes, Espaces et Réseaux. Lang AG International Academic Publishers, Peter, 2012.
Buscar texto completoGénérations de Fédéralistes Européens Depuis le XIXe Siècle: Individus, Groupes, Espaces et Réseaux. Lang AG International Academic Publishers, Peter, 2012.
Buscar texto completoSegregated Britain: Everyday Life in Muslim Enclaves. Lang AG International Academic Publishers, Peter, 2020.
Buscar texto completoCapítulos de libros sobre el tema "Réseaux génératifs"
HADDADOU, Kamel y Guy PUJOLLE. "Cloud et Edge Networking dans l’IoT". En Cloud et Edge Networking, 165–89. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9128.ch10.
Texto completoAL AGHA, Khaldoun, Pauline LOYGUE y Guy PUJOLLE. "Les réseaux ad hoc et mesh". En Edge Networking, 97–116. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9068.ch5.
Texto completoAL AGHA, Khaldoun, Pauline LOYGUE y Guy PUJOLLE. "L’exemple de Green Communications". En Edge Networking, 207–20. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9068.ch10.
Texto completoESLAMI, Yasamin, Mario LEZOCHE y Philippe THOMAS. "Big Data Analytics et machine learning pour les systèmes industriels cyber-physiques". En Digitalisation et contrôle des systèmes industriels cyber-physiques, 175–95. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9085.ch9.
Texto completoBELMONTE, Romain, Pierre TIRILLY, Ioan Marius BILASCO, Nacim IHADDADENE y Chaabane DJERABA. "Approches de détection des points de repère faciaux". En Analyse faciale en conditions non contrôlées, 15–74. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9111.ch1.
Texto completoKrzmarzick, Andrew. "GovLoop, réseau social des fonctionnaires américains". En Génération Y et gestion publique : quels enjeux ?, 147–59. Institut de la gestion publique et du développement économique, 2012. http://dx.doi.org/10.4000/books.igpde.963.
Texto completoCasilli, Antonio A. "L’administration en réseau, entre réconciliation participative et sursauts régaliens". En Génération Y et gestion publique : quels enjeux ?, 55–66. Institut de la gestion publique et du développement économique, 2012. http://dx.doi.org/10.4000/books.igpde.946.
Texto completoBiris, Ioan. "La fonctionnalité de l’idée de “champ” dans les sciences". En The Paideia Archive: Twentieth World Congress of Philosophy, 35–41. Philosophy Documentation Center, 1998. http://dx.doi.org/10.5840/wcp20-paideia199842772.
Texto completoActas de conferencias sobre el tema "Réseaux génératifs"
Chevalier, Marcel, Laurent Buchsbaum, Yoann Robin y Gaël Le Godais. "Génération automatique de réseaux de Petri à partir de réseaux électriques". En Congrès Lambda Mu 20 de Maîtrise des Risques et de Sûreté de Fonctionnement, 11-13 Octobre 2016, Saint Malo, France. IMdR, 2016. http://dx.doi.org/10.4267/2042/61815.
Texto completoInformes sobre el tema "Réseaux génératifs"
Fontecave, Marc y Candel Sébastien. Quelles perspectives énergétiques pour la biomasse ? Académie des sciences, enero de 2024. http://dx.doi.org/10.62686/1.
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