Gotowa bibliografia na temat „Elagage de réseaux de neurones”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Elagage de réseaux de neurones”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Elagage de réseaux de neurones"
-BORNE, Pierre. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, nr 08 (2006): 31. http://dx.doi.org/10.3845/ree.2006.074.
Pełny tekst źródła-BORNE, Pierre. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, nr 08 (2006): 37. http://dx.doi.org/10.3845/ree.2006.075.
Pełny tekst źródła-Y. HAGGEGE, Joseph. "Les réseaux de neurones". Revue de l'Electricité et de l'Electronique -, nr 08 (2006): 43. http://dx.doi.org/10.3845/ree.2006.076.
Pełny tekst źródła-BENREJEB, Mohamed. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, nr 08 (2006): 47. http://dx.doi.org/10.3845/ree.2006.077.
Pełny tekst źródła-Y. HAGGEGE, Joseph. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, nr 08 (2006): 50. http://dx.doi.org/10.3845/ree.2006.078.
Pełny tekst źródła-BENREJEB, Mohamed. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, nr 08 (2006): 55. http://dx.doi.org/10.3845/ree.2006.079.
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 źródłaMézard, Marc, i Jean-Pierre Nadal. "Réseaux de neurones et physique statistique". Intellectica. Revue de l'Association pour la Recherche Cognitive 9, nr 1 (1990): 213–45. http://dx.doi.org/10.3406/intel.1990.884.
Pełny tekst źródłaLaks, Bernard. "Réseaux de neurones et syllabation du français". Linx 34, nr 1 (1996): 327–46. http://dx.doi.org/10.3406/linx.1996.1440.
Pełny tekst źródłaJelassi, Khaled, Najiba Bellaaj-Merabet i Bruno Dagues. "Estimation du flux par réseaux de neurones". Revue internationale de génie électrique 7, nr 1-2 (30.04.2004): 105–31. http://dx.doi.org/10.3166/rige.7.105-131.
Pełny tekst źródłaRozprawy doktorskie na temat "Elagage de réseaux de neurones"
Hubens, Nathan. "Towards lighter and faster deep neural networks with parameter pruning". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS025.
Pełny tekst źródłaSince their resurgence in 2012, Deep Neural Networks have become ubiquitous in most disciplines of Artificial Intelligence, such as image recognition, speech processing, and Natural Language Processing. However, over the last few years, neural networks have grown exponentially deeper, involving more and more parameters. Nowadays, it is not unusual to encounter architectures involving several billions of parameters, while they mostly contained thousands less than ten years ago.This generalized increase in the number of parameters makes such large models compute-intensive and essentially energy inefficient. This makes deployed models costly to maintain but also their use in resource-constrained environments very challenging.For these reasons, much research has been conducted to provide techniques reducing the amount of storage and computing required by neural networks. Among those techniques, neural network pruning, consisting in creating sparsely connected models, has been recently at the forefront of research. However, although pruning is a prevalent compression technique, there is currently no standard way of implementing or evaluating novel pruning techniques, making the comparison with previous research challenging.Our first contribution thus concerns a novel description of pruning techniques, developed according to four axes, and allowing us to unequivocally and completely define currently existing pruning techniques. Those components are: the granularity, the context, the criteria, and the schedule. Defining the pruning problem according to those components allows us to subdivide the problem into four mostly independent subproblems and also to better determine potential research lines.Moreover, pruning methods are still in an early development stage, and primarily designed for the research community. Indeed, most pruning works are usually implemented in a self-contained and sophisticated way, making it troublesome for non-researchers to apply such techniques without having to learn all the intricacies of the field. To fill this gap, we proposed FasterAI toolbox, intended to be helpful to researchers, eager to create and experiment with different compression techniques, but also to newcomers, that desire to compress their neural network for concrete applications. In particular, the sparsification capabilities of FasterAI have been built according to the previously defined pruning components, allowing for a seamless mapping between research ideas and their implementation.We then propose four theoretical contributions, each one aiming at providing new insights and improving on state-of-the-art methods in each of the four identified description axes. Also, those contributions have been realized by using the previously developed toolbox, thus validating its scientific utility.Finally, to validate the applicative character of the pruning technique, we have selected a use case: the detection of facial manipulation, also called DeepFakes Detection. The goal is to demonstrate that the developed tool, as well as the different proposed scientific contributions, can be applicable to a complex and actual problem. This last contribution is accompanied by a proof-of-concept application, providing DeepFake detection capabilities in a web-based environment, thus allowing anyone to perform detection on an image or video of their choice.This Deep Learning era has emerged thanks to the considerable improvements in high-performance hardware and access to a large amount of data. However, since the decline of Moore's Law, experts are suggesting that we might observe a shift in how we conceptualize the hardware, by going from task-agnostic to domain-specialized computations, thus leading to a new era of collaboration between software, hardware, and machine learning communities. This new quest for more efficiency will thus undeniably go through neural network compression techniques, and particularly sparse computations
Wenzek, Didier. "Construction de réseaux de neurones". Phd thesis, Grenoble INPG, 1993. http://tel.archives-ouvertes.fr/tel-00343569.
Pełny tekst źródłaTsopze, Norbert. "Treillis de Galois et réseaux de neurones : une approche constructive d'architecture des réseaux de neurones". Thesis, Artois, 2010. http://www.theses.fr/2010ARTO0407/document.
Pełny tekst źródłaThe artificial neural networks are successfully applied in many applications. But theusers are confronted with two problems : defining the architecture of the neural network able tosolve their problems and interpreting the network result. Many research works propose some solutionsabout these problems : to find out the architecture of the network, some authors proposeto use the problem domain theory and deduct the network architecture and some others proposeto dynamically add neurons in the existing networks until satisfaction. For the interpretabilityproblem, solutions consist to extract rules which describe the network behaviour after training.The contributions of this thesis concern these problems. The thesis are limited to the use of theartificial neural networks in solving the classification problem.In this thesis, we present a state of art of the existing methods of finding the neural networkarchitecture : we present a theoritical and experimental study of these methods. From this study,we observe some limits : difficulty to use some method when the knowledges are not available ;and the network is seem as ’black box’ when using other methods. We a new method calledCLANN (Concept Lattice-based Artificial Neural Network) which builds from the training dataa semi concepts lattice and translates this semi lattice into the network architecture. As CLANNis limited to the two classes problems, we propose MCLANN which extends CLANN to manyclasses problems.A new method of rules extraction called ’MaxSubsets Approach’ is also presented in thisthesis. Its particularity is the possibility of extracting the two kind of rules (If then and M-of-N)from an internal structure.We describe how to explain the MCLANN built network result aboutsome inputs
Voegtlin, Thomas. "Réseaux de neurones et auto-référence". Lyon 2, 2002. http://theses.univ-lyon2.fr/documents/lyon2/2002/voegtlin_t.
Pełny tekst źródłaThe purpose of this thesis is to present a class of unsupervised learning algorithms for recurrent networks. In the first part (chapters 1 to 4), I propose a new approach to this question, based on a simple principle: self-reference. A self-referent algorithm is not based on the minimization of an objective criterion, such as an error function, but on a subjective function, that depends on what the network has previously learned. An example of a supervised recurrent network where learning is self-referent is the Simple Recurrent Network (SRN) by Elman (1990). In the SRN, self-reference is applied to the supervised error back-propagation algorithm. In this aspect, the SRN differs from other generalizations of back-propagation to recurrent networks, that use an objective criterion, such as Back-Propagation Through Time, or Real-Time Recurrent Learning. In this thesis, I show that self-reference can be combined with several well-known unsupervised learning methods: the Self-Organizing Map (SOM), Principal Components Analysis (PCA), and Independent Components Analysis (ICA). These techniques are classically used to represent static data. Self-reference allows one to generalize these techniques to time series, and to define unsupervised learning algorithms for recurrent networks
Teytaud, Olivier. "Apprentissage, réseaux de neurones et applications". Lyon 2, 2001. http://theses.univ-lyon2.fr/documents/lyon2/2001/teytaud_o.
Pełny tekst źródłaCôté, Marc-Alexandre. "Réseaux de neurones génératifs avec structure". Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10489.
Pełny tekst źródłaJodouin, Jean-François. "Réseaux de neurones et traitement du langage naturel : étude des réseaux de neurones récurrents et de leurs représentations". Paris 11, 1993. http://www.theses.fr/1993PA112079.
Pełny tekst źródłaBrette, Romain. "Modèles Impulsionnels de Réseaux de Neurones Biologiques". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2003. http://tel.archives-ouvertes.fr/tel-00005340.
Pełny tekst źródłaTardif, Patrice. "Autostructuration des réseaux de neurones avec retards". Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24240/24240.pdf.
Pełny tekst źródłaMaktoobi, 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)
Książki na temat "Elagage de réseaux de neurones"
Michel, Verleysen, red. Les réseaux de neurones artificiels. Paris: Presses universitaires de France, 1996.
Znajdź pełny tekst źródłaKamp, Yves. Réseaux de neurones récursifs pour mémoires associatives. Lausanne: Presses polytechniques et universitaires romandes, 1990.
Znajdź pełny tekst źródłaRollet, Guy. Les RÉSEAUX DE NEURONES DE LA CONSCIENCE - Approche multidisciplinaire du phénomène. Paris: Editions L'Harmattan, 2013.
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 źródłaJournées d'électronique (1989 Lausanne, Switzerland). Réseaux de neurones artificiels: Comptes rendus des Journées d'électronique 1989, Lausanne, 10-12 october 1983. Lausanne: Presses polytechniques romande, 1989.
Znajdź pełny tekst źródłaSeidou, 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 źródłaSuzanne, Tyc-Dumont, red. Le neurone computationnel: Histoire d'un siècle de recherches. Paris: CNRS, 2005.
Znajdź pełny tekst źródłaBiophysics of computation: Information processing in single neurons. New York: Oxford University Press, 1999.
Znajdź pełny tekst źródłaK, Kaczmarek Leonard, red. The neuron: Cell and molecular biology. Wyd. 3. Oxford: Oxford University Press, 2002.
Znajdź pełny tekst źródłaCzęści książek na temat "Elagage de réseaux de neurones"
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 źródłaKipnis, C., i E. Saada. "Un lien entre réseaux de neurones et systèmes de particules: Un modele de rétinotopie". W Lecture Notes in Mathematics, 55–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0094641.
Pełny tekst źródła"4. Les réseaux de neurones artificiels". W L'intelligence artificielle, 91–112. EDP Sciences, 2021. http://dx.doi.org/10.1051/978-2-7598-2580-6.c006.
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 źródłaBENMAMMAR, Badr, i Asma AMRAOUI. "Application de l’intelligence artificielle dans les réseaux de radio cognitive". W Gestion et contrôle intelligents des réseaux, 233–60. ISTE Group, 2020. http://dx.doi.org/10.51926/iste.9008.ch9.
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 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 źródłaStreszczenia konferencji na temat "Elagage de réseaux de neurones"
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 źródłaWalid, Tazarki, Fareh Riadh i Chichti Jameleddine. "La Prevision Des Crises Bancaires: Un essai de modélisation par la méthode des réseaux de neurones [Not available in English]". W International Conference on Information and Communication Technologies from Theory to Applications - ICTTA'08. IEEE, 2008. http://dx.doi.org/10.1109/ictta.2008.4529985.
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.
Pełny tekst źródła