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Artykuły w czasopismach na temat "Compression 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 "Compression de réseaux de neurones"
Fernandez, Brillet Lucas. "Réseaux de neurones CNN pour la vision embarquée". Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM043.
Pełny tekst źródłaRecently, Convolutional Neural Networks have become the state-of-the-art soluion(SOA) to most computer vision problems. In order to achieve high accuracy rates, CNNs require a high parameter count, as well as a high number of operations. This greatly complicates the deployment of such solutions in embedded systems, which strive to reduce memory size. Indeed, while most embedded systems are typically in the range of a few KBytes of memory, CNN models from the SOA usually account for multiple MBytes, or even GBytes in model size. Throughout this thesis, multiple novel ideas allowing to ease this issue are proposed. This requires to jointly design the solution across three main axes: Application, Algorithm and Hardware.In this manuscript, the main levers allowing to tailor computational complexity of a generic CNN-based object detector are identified and studied. Since object detection requires scanning every possible location and scale across an image through a fixed-input CNN classifier, the number of operations quickly grows for high-resolution images. In order to perform object detection in an efficient way, the detection process is divided into two stages. The first stage involves a region proposal network which allows to trade-off recall for the number of operations required to perform the search, as well as the number of regions passed on to the next stage. Techniques such as bounding box regression also greatly help reduce the dimension of the search space. This in turn simplifies the second stage, since it allows to reduce the task’s complexity to the set of possible proposals. Therefore, parameter counts can greatly be reduced.Furthermore, CNNs also exhibit properties that confirm their over-dimensionment. This over-dimensionement is one of the key success factors of CNNs in practice, since it eases the optimization process by allowing a large set of equivalent solutions. However, this also greatly increases computational complexity, and therefore complicates deploying the inference stage of these algorithms on embedded systems. In order to ease this problem, we propose a CNN compression method which is based on Principal Component Analysis (PCA). PCA allows to find, for each layer of the network independently, a new representation of the set of learned filters by expressing them in a more appropriate PCA basis. This PCA basis is hierarchical, meaning that basis terms are ordered by importance, and by removing the least important basis terms, it is possible to optimally trade-off approximation error for parameter count. Through this method, it is possible to compress, for example, a ResNet-32 network by a factor of ×2 both in the number of parameters and operations with a loss of accuracy <2%. It is also shown that the proposed method is compatible with other SOA methods which exploit other CNN properties in order to reduce computational complexity, mainly pruning, winograd and quantization. Through this method, we have been able to reduce the size of a ResNet-110 from 6.88Mbytes to 370kbytes, i.e. a x19 memory gain with a 3.9 % accuracy loss.All this knowledge, is applied in order to achieve an efficient CNN-based solution for a consumer face detection scenario. The proposed solution consists of just 29.3kBytes model size. This is x65 smaller than other SOA CNN face detectors, while providing equal detection performance and lower number of operations. Our face detector is also compared to a more traditional Viola-Jones face detector, exhibiting approximately an order of magnitude faster computation, as well as the ability to scale to higher detection rates by slightly increasing computational complexity.Both networks are finally implemented in a custom embedded multiprocessor, verifying that theorical and measured gains from PCA are consistent. Furthermore, parallelizing the PCA compressed network over 8 PEs achieves a x11.68 speed-up with respect to the original network running on a single PE
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
Dupont, Robin. "Deep Neural Network Compression for Visual Recognition". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS565.
Pełny tekst źródłaThanks to the miniaturisation of electronics, embedded devices have become ubiquitous since the 2010s, performing various tasks around us. As their usage expands, there's an increasing demand for efficient data processing and decision-making. Deep neural networks are apt tools for this, but they are often too large and intricate for embedded systems. Therefore, methods to compress these networks without affecting their performance are crucial. This PhD thesis introduces two methods focused on pruning to compress networks, maintaining accuracy. The thesis first details a budget-aware method for compressing large neural networks using weight reparametrisation and a budget loss, eliminating the need for fine-tuning. Traditional pruning methods often use post-training indicators to cut weights, ignoring desired pruning rates. Our method incorporates a budget loss, directing pruning during training, enabling simultaneous topology and weight optimisation. By soft-pruning smaller weights via reparametrisation, we reduce accuracy loss compared to standard pruning. We validate our method on several datasets and architectures. Later, the thesis examines extracting efficient subnetworks without weight training. We aim to discern the optimal subnetwork topology within a large network, bypassing weight optimisation yet ensuring strong performance. This is realized with our Arbitrarily Shifted Log Parametrisation, a differentiable method for discrete topology sampling, facilitating masks' training to denote weight selection probability. Additionally, a weight recalibration technique, Smart Rescale, is presented. It boosts extracted subnetworks' performance and hastens their training. Our method identifies the best pruning rate in a single training cycle, averting exhaustive hyperparameter searches and various rate training. Through extensive tests, our technique consistently surpasses similar state-of-the-art methods, creating streamlined networks that achieve high sparsity without notable accuracy drops
Yvinec, Edouard. "Efficient Neural Networks : Post Training Pruning and Quantization". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS581.
Pełny tekst źródłaDeep neural networks have grown to be the most widely adopted models to solve most computer vision and natural language processing tasks. Since the renewed interest, sparked in 2012, for these architectures, in machine learning, their size in terms of memory footprint and computational costs have increased tremendously, which has hindered their deployment. In particular, with the rising interest for generative ai such as large language models and diffusion models, this phenomenon has recently reached new heights, as these models can weight several billions of parameters and require multiple high-end gpus in order to infer in real-time. In response, the deep learning community has researched for methods to compress and accelerate these models. These methods are: efficient architecture design, tensor decomposition, pruning and quantization. In this manuscript, I paint a landscape of the current state-of-the art in deep neural networks compression and acceleration as well as my contributions to the field. First, I propose a general introduction to the aforementioned techniques and highlight their shortcomings and current challenges. Second, I provide a detailed discussion regarding my contributions to the field of deep neural networks pruning. These contributions led to the publication of three articles: RED, RED++ and SInGE. In RED and RED++, I introduced a novel way to perform data-free pruning and tensor decomposition based on redundancy reduction. On the flip side, in SInGE, I proposed a new importance-based criterion for data-driven pruning. This criterion was inspired by attribution techniques which consist in ranking inputs by their relative importance with respect to the final prediction. In SInGE, I adapted one of the most effective attribution technique to weight importance ranking for pruning. In the third chapter, I layout my contributions to the field of deep quantization: SPIQ, PowerQuant, REx, NUPES, and a best practice paper. Each of these methods address one of the previous limitations of post-training quantization. In SPIQ, PowerQuant and REx, I provide a solution to the granularity limitations of quantization, a novel non-uniform format which is particularly effective on transformer architectures and a technique for quantization decomposition which eliminates the need for unsupported bit-widths, respectively. In the two remaining articles, I provide significant improvements over existing gradient-based post-training quantization techniques, bridging the gap between such techniques and non-uniform quantization. In the last chapter, I propose a set of leads for future work which I believe to be the, current, most important unanswered questions in the field
Resmerita, Diana. "Compression pour l'apprentissage en profondeur". Thesis, Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ4043.
Pełny tekst źródłaAutonomous cars are complex applications that need powerful hardware machines to be able to function properly. Tasks such as staying between the white lines, reading signs, or avoiding obstacles are solved by using convolutional neural networks (CNNs) to classify or detect objects. It is highly important that all the networks work in parallel in order to transmit all the necessary information and take a common decision. Nowadays, as the networks improve, they also have become bigger and more computational expensive. Deploying even one network becomes challenging. Compressing the networks can solve this issue. Therefore, the first objective of this thesis is to find deep compression methods in order to cope with the memory and computational power limitations present on embedded systems. The compression methods need to be adapted to a specific processor, Kalray's MPPA, for short term implementations. Our contributions mainly focus on compressing the network post-training for storage purposes, which means compressing the parameters of the network without retraining or changing the original architecture and the type of the computations. In the context of our work, we decided to focus on quantization. Our first contribution consists in comparing the performances of uniform quantization and non-uniform quantization, in order to identify which of the two has a better rate-distortion trade-off and could be quickly supported in the company. The company's interest is also directed towards finding new innovative methods for future MPPA generations. Therefore, our second contribution focuses on comparing standard floating-point representations (FP32, FP16) to recently proposed alternative arithmetical representations such as BFloat16, msfp8, Posit8. The results of this analysis were in favor for Posit8. This motivated the company Kalray to conceive a decompressor from FP16 to Posit8. Finally, since many compression methods already exist, we decided to move to an adjacent topic which aims to quantify theoretically the effects of quantization error on the network's accuracy. This is the second objective of the thesis. We notice that well-known distortion measures are not adapted to predict accuracy degradation in the case of inference for compressed neural networks. We define a new distortion measure with a closed form which looks like a signal-to-noise ratio. A set of experiments were done using simulated data and small networks, which show the potential of this distortion measure
Cherkashyn, Valeriy. "Représentation adaptative d'images de télédétection à très haute résolution spatiale une nouvelle approche hybride (la décomposition pyramidale avec des réseaux de neurones)". Thèse, Université de Sherbrooke, 2011. http://hdl.handle.net/11143/5831.
Pełny tekst źródłaMitrica, Iulia. "Video compression of airplane cockpit screens content". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT042.
Pełny tekst źródłaThis thesis addresses the problem of encoding the video of airplane cockpits.The cockpit of modern airliners consists in one or more screens displaying the status of the plane instruments (e.g., the plane location as reported by the GPS, the fuel level as read by the sensors in the tanks, etc.,) often superimposed over natural images (e.g., navigation maps, outdoor cameras, etc.).Plane sensors are usually inaccessible due to security reasons, so recording the cockpit is often the only way to log vital plane data in the event of, e.g., an accident.Constraints on the recording storage available on-board require the cockpit video to be coded at low to very low bitrates, whereas safety reasons require the textual information to remain intelligible after decoding. In addition, constraints on the power envelope of avionic devices limit the cockpit recording subsystem complexity.Over the years, a number of schemes for coding images or videos with mixed computer-generated and natural contents have been proposed. Text and other computer generated graphics yield high-frequency components in the transformed domain. Therefore, the loss due to compression may hinder the readability of the video and thus its usefulness. For example, the recently standardized Screen Content Coding (SCC) extension of the H.265/HEVC standard includes tools designed explicitly for screen contents compression. Our experiments show however that artifacts persist at the low bitrates targeted by our application, prompting for schemes where the video is not encoded in the pixel domain.This thesis proposes methods for low complexity screen coding where text and graphical primitives are encoded in terms of their semantics rather than as blocks of pixels.At the encoder side, characters are detected and read using a convolutional neural network.Detected characters are then removed from screen via pixel inpainting, yielding a smoother residual video with fewer high frequencies. The residual video is encoded with a standard video codec and is transmitted to the receiver side together with text and graphics semantics as side information.At the decoder side, text and graphics are synthesized using the decoded semantics and superimposed over the residual video, eventually recovering the original frame. Our experiments show that an AVC/H.264 encoder retrofitted with our method has better rate-distortion performance than H.265/HEVC and approaches that of its SCC extension.If the complexity constraints allow inter-frame prediction, we also exploit the fact that co-located characters in neighbor frames are strongly correlated.Namely, the misclassified symbols are recovered using a proposed method based on low-complexity model of transitional probabilities for characters and graphics. Concerning character recognition, the error rate drops up to 18 times in the easiest cases and at least 1.5 times in the most difficult sequences despite complex occlusions.By exploiting temporal redundancy, our scheme further improves in rate-distortion terms and enables quasi-errorless character decoding. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to video compression standards
Verma, Sagar. "Deep Neural Network Modeling of Electric Motors". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST088.
Pełny tekst źródłaThis thesis deals with the application of neural networks in solving electrical motor problems. Chapter 2 contributes to identifying a neural network that can learn the multivariate relationship between different electrical motor signals.The identified network is then used for speed-torque estimation from currents and voltages. Chapter 3 focuses on detecting and recovering from faulty measurements. Our method encompasses electrical sensor faults, mechanical faults, and temperature estimation.Chapter 4 then discusses the reliability of the speed-torque estimator in case of noisy currents and voltages. We presenta denoising method which allows our speed- torque estimator to be applicable in a realistic context. This is followed by an analysis of the adversarial robustness of the neural networks used in electrical motor tasks. The generalization capability of the speed-torque estimator is also briefly considered. In Chapter 5, we focus on the final roadblock in achieving real-world application of neural networks: computational requirements. We present the Subdifferential Inclusion for Sparsity (SIS) method to find the best sparse network from pretrained weights while maintaining original accuracy
Burel, Gilles. "RESEAUX DE NEURONES EN TRAITEMENT D'IMAGES - Des Modèles théoriques aux Applications Industrielles -". Phd thesis, Université de Bretagne occidentale - Brest, 1991. http://tel.archives-ouvertes.fr/tel-00101699.
Pełny tekst źródłatraitement du signal et de l'image. On se place d'emblée du point de vue de
l'industriel impliqué dans la recherche, c'est à dire que l'on s'intéresse à
des problèmes réalistes, sans pour autant négliger la recherche
théorique.
Dans une première partie, nous montrons
l'intérêt des réseaux de neurones comme source d'inspiration pour la
conception de nouveaux algorithmes. Nous proposons en particulier une
structure originale pour la prédiction, ainsi que de nouveaux algorithmes de
Quantification Vectorielle. Les propriétés des algorithmes existants sont
également éclaircies du point de vue théorique, et des méthodes de réglage
automatique de leurs paramètres sont proposées.
On montre ensuite les capacités des réseaux de neurones à traiter un vaste champ
d'applications d'intérêt industriel. Pour divers problèmes de traitement de
l'image et du signal (de la segmentation à la séparation de sources, en
passant par la reconnaissance de formes et la compression de données), on
montre qu'il est possible de développer à moindre coût une solution neuronale
efficace.
Boukli, Hacene Ghouthi. "Processing and learning deep neural networks on chip". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0153/document.
Pełny tekst źródłaIn the field of machine learning, deep neural networks have become the inescapablereference for a very large number of problems. These systems are made of an assembly of layers,performing elementary operations, and using a large number of tunable variables. Using dataavailable during a learning phase, these variables are adjusted such that the neural networkaddresses the given task. It is then possible to process new data.To achieve state-of-the-art performance, in many cases these methods rely on a very largenumber of parameters, and thus large memory and computational costs. Therefore, they are oftennot very adapted to a hardware implementation on constrained resources systems. Moreover, thelearning process requires to reuse the training data several times, making it difficult to adapt toscenarios where new information appears on the fly.In this thesis, we are first interested in methods allowing to reduce the impact of computations andmemory required by deep neural networks. Secondly, we propose techniques for learning on thefly, in an embedded context
Książki na temat "Compression 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 źródłaA, Chou Philip, i Schaar Mihaela van der, red. Multimedia over IP and wireless networks: Compression, networking, and systems. Amsterdam: Elsevier/Academic Press, 2007.
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 źródłaCzęści książek na temat "Compression 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 "Compression 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.
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