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Artykuły w czasopismach na temat "Réseaux de neurones lipschitz"
-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 "Réseaux de neurones lipschitz"
Béthune, Louis. "Apprentissage profond avec contraintes Lipschitz". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES014.
Pełny tekst źródłaThis thesis explores the characteristics and applications of Lipschitz networks in machine learning tasks. First, the framework of "optimization as a layer" is presented, showcasing various applications, including the parametrization of Lipschitz-constrained layers. Then, the expressiveness of these networks in classification tasks is investigated, revealing an accuracy/robustness tradeoff controlled by entropic regularization of the loss, accompanied by generalization guarantees. Subsequently, the research delves into the utilization of signed distance functions as a solution to a regularized optimal transport problem, showcasing their efficacy in robust one-class learning and the construction of neural implicit surfaces. After, the thesis demonstrates the adaptability of the back-propagation algorithm to propagate bounds instead of vectors, enabling differentially private training of Lipschitz networks without incurring runtime and memory overhead. Finally, it goes beyond Lipschitz constraints and explores the use of convexity constraint for multivariate quantiles
Neacșu, Ana-Antonia. "Robust Deep learning methods inspired by signal processing algorithms". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST212.
Pełny tekst źródłaUnderstanding the importance of defense strategies against adversarial attacks has become paramount in ensuring the trustworthiness and resilience of neural networks. While traditional security measures focused on protecting data and software from external threats, the unique challenge posed by adversarial attacks lies in their ability to exploit the inherent vulnerabilities of the underlying machine learning algorithms themselves.The first part of the thesis proposes new constrained learning strategies that ensure robustness against adversarial perturbations by controlling the Lipschitz constant of a classifier. We focus on nonnegative neural networks for which accurate Lipschitz bounds can be derived, and we propose different spectral norm constraints offering robustness guarantees from a theoretical viewpoint. We validate our solution in the context of gesture recognition based on Surface Electromyographic (sEMG) signals.In the second part of the thesis, we propose a new class of neural networks (ACNN) which can be viewed as establishing a link between fully connected and convolutional networks, and we propose an iterative algorithm to control their robustness during training. Next, we extend our solution to the complex plane and address the problem of designing robust complex-valued neural networks by proposing a new architecture (RCFF-Net) for which we derive tight Lipschitz constant bounds. Both solutions are validated for audio denoising.In the last part, we introduce ABBA Networks, a novel class of (almost) non-negative neural networks, which we show to be universal approximators. We derive tight Lipschitz bounds for both linear and convolutional layers, and we propose an algorithm to train robust ABBA networks. We show the effectiveness of the proposed approach in the context of image classification
Gupta, Kavya. "Stability Quantification of Neural Networks". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST004.
Pełny tekst źródłaArtificial neural networks are at the core of recent advances in Artificial Intelligence. One of the main challenges faced today, especially by companies likeThales designing advanced industrial systems is to ensure the safety of newgenerations of products using these technologies. In 2013 in a key observation, neural networks were shown to be sensitive to adversarial perturbations, raising serious concerns about their applicability in critically safe environments. In the last years, publications studying the various aspects of this robustness of neural networks, and rising questions such as "Why adversarial attacks occur?", "How can we make the neural network more robust to adversarial noise?", "How to generate stronger attacks?" etc., have grown exponentially. The contributions of this thesis aim to tackle such problems. The adversarial machine learning community concentrates majorly on classification scenarios, whereas studies on regression tasks are scarce. Our contributions bridge this significant gap between adversarial machine learning and regression applications.The first contribution in Chapter 3 proposes a white-box attackers designed to attack regression models. The presented adversarial attacker is derived from the algebraic properties of the Jacobian of the network. We show that our attacker successfully fools the neural network and measure its effectiveness in reducing the estimation performance. We present our results on various open-source and real industrial tabular datasets. Our analysis relies on the quantification of the fooling error as well as different error metrics. Another noteworthy feature of our attacker is that it allows us to optimally attack a subset of inputs, which may help to analyze the sensitivity of some specific inputs. We also, show the effect of this attacker on spectrally normalised trained models which are known to be more robust in handling attacks.The second contribution of this thesis (Chapter 4) presents a multivariate Lipschitz constant analysis of neural networks. The Lipschitz constant is widely used in the literature to study the internal properties of neural networks. But most works do a single parametric analysis, which do not allow to quantify the effect of individual inputs on the output. We propose a multivariate Lipschitz constant-based stability analysis of fully connected neural networks allowing us to capture the influence of each input or group of inputs on the neural network stability. Our approach relies on a suitable re-normalization of the input space, intending to perform a more precise analysis than the one provided by a global Lipschitz constant. We display the results of this analysis by a new representation designed for machine learning practitioners and safety engineers termed as a Lipschitz star. We perform experiments on various open-access tabular datasets and an actual Thales Air Mobility industrial application subject to certification requirements.The use of spectral normalization in designing a stability control loop is discussed in Chapter 5. A critical part of the optimal model is to behave according to specified performance and stability targets while in operation. But imposing tight Lipschitz constant constraints while training the models usually leads to a reduction of their accuracy. Hence, we design an algorithm to train "stable-by-design" neural network models using our spectral normalization approach, which optimizes the model by taking into account both performance and stability targets. We focus on Small Unmanned Aerial Vehicles (UAVs). More specifically, we present a novel application of neural networks to detect in real-time elevon positioning faults to allow the remote pilot to take necessary actions to ensure safety
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 źródłaKsiążki na temat "Réseaux de neurones lipschitz"
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 źródłaAlmeida, Fernando Carvalho de. L'evaluation des risques de défaillance des entreprises à partir des réseaux de neurones insérés dans les systèmes d'aide à la décision. Grenoble: A.N.R.T, Université Pierre Mendes France (Grenoble II), 1993.
Znajdź pełny tekst źródłaUniversité de Paris X: Nanterre, red. L'avènement de la complexité dans la construction des apprentissages: Application à la pédagogie des recherches menées en informatique sur le chaos déterministe et les réseaux de neurones artificiels. Lille: A.N.R.T, Université de Lille III, 1996.
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 źródłaCzęści książek na temat "Réseaux de neurones lipschitz"
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 "Réseaux de neurones lipschitz"
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.
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