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Artykuły w czasopismach na temat "Réseaux de neurones récurrents convolutifs"
Postadjian, Tristan, Arnaud Le Bris, Hichem Sahbi i Clément Mallet. "Classification à très large échelle d'images satellites à très haute résolution spatiale par réseaux de neurones convolutifs". Revue Française de Photogrammétrie et de Télédétection, nr 217-218 (21.09.2018): 73–86. http://dx.doi.org/10.52638/rfpt.2018.418.
Pełny tekst źródłaJovanović, S., i S. Weber. "Modélisation et accélération de réseaux de neurones profonds (CNN) en Python/VHDL/C++ et leur vérification et test à l’aide de l’environnement Pynq sur les FPGA Xilinx". J3eA 21 (2022): 1028. http://dx.doi.org/10.1051/j3ea/20220028.
Pełny tekst źródłaPalluat, Nicolas, Daniel Racoceanu i Noureddine Zerhouni. "Utilisation des réseaux de neurones temporels pour le pronostic et la surveillance dynamique. Etude comparative de trois réseaux de neurones récurrents". Revue d'intelligence artificielle 19, nr 6 (1.12.2005): 913–50. http://dx.doi.org/10.3166/ria.19.913-950.
Pełny tekst źródłaZemouri, Ryad, Daniel Racoceanu i Nourredine Zerhouni. "Réseaux de neurones récurrents à fonctions de base radiales : RRFR Application au pronostic". Revue d'intelligence artificielle 16, nr 3 (1.06.2002): 307–38. http://dx.doi.org/10.3166/ria.16.307-338.
Pełny tekst źródłaZemouri, Ryad, Daniel Racoceanu i Nourredine Zerhouni. "Réseaux de neurones récurrents à fonctions de base radiales. Application à la surveillance dynamique". Journal Européen des Systèmes Automatisés 37, nr 1 (30.01.2003): 49–81. http://dx.doi.org/10.3166/jesa.37.49-81.
Pełny tekst źródłaAit Si Selmi, T., F. Müller Fouarge, T. Estienne, S. Bekadar, Y. Carrillon, C. Pouchy i M. Bonnin. "Analyse automatique de la sévérité de l’arthrose sur des radiographies du genou à l’aide de réseaux de neurones convolutifs". Revue du Rhumatisme 89 (grudzień 2022): A128. http://dx.doi.org/10.1016/j.rhum.2022.10.186.
Pełny tekst źródłaLe Bris, Arnaud, Cyril Wendl, Nesrine Chehata, Anne Puissant i Tristan Postadjian. "Fusion tardive d'images SPOT-6/7 et de données multi-temporelles Sentinel-2 pour la détection de la tâche urbaine". Revue Française de Photogrammétrie et de Télédétection, nr 217-218 (21.09.2018): 87–97. http://dx.doi.org/10.52638/rfpt.2018.415.
Pełny tekst źródłaHARINAIVO, A., H. HAUDUC i I. TAKACS. "Anticiper l’impact de la météo sur l’influent des stations d’épuration grâce à l’intelligence artificielle". Techniques Sciences Méthodes 3 (20.03.2023): 33–42. http://dx.doi.org/10.36904/202303033.
Pełny tekst źródłaNguyen, K. L., A. Almhdie-Imjabbar, H. Toumi, R. Jennane i E. Lespessailles. "Combinaison de la texture trabéculaire osseuse et des réseaux de neurones convolutifs pour la prédiction de la progression de la gonarthrose : données des cohortes de l’OsteoArthritis Initiative (OAI) et de la Multicenter Osteoarthritis Study (MOST)". Revue du Rhumatisme 87 (grudzień 2020): A90. http://dx.doi.org/10.1016/j.rhum.2020.10.153.
Pełny tekst źródłaMaulin, Maëva, Nicolas Estre, David Tisseur, Grégoire Kessedjian, Alix Sardet, Emmanuel Payan i Daniel Eck. "Défloutage de projections tomographiques industrielles hautes énergies à l’aide d’un réseau de neurones convolutifs". e-journal of nondestructive testing 28, nr 9 (wrzesień 2023). http://dx.doi.org/10.58286/28481.
Pełny tekst źródłaRozprawy doktorskie na temat "Réseaux de neurones récurrents convolutifs"
Shahkarami, Abtin. "Complexity reduction over bi-RNN-based Kerr nonlinearity equalization in dual-polarization fiber-optic communications via a CRNN-based approach". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT034.
Pełny tekst źródłaThe impairments arising from the Kerr nonlinearity in optical fibers limit the achievable information rates in fiber-optic communication. Unlike linear effects, such as chromatic dispersion and polarization-mode dispersion, which can be compensated via relatively simple linear equalization at the receiver, the computational complexity of the conventional nonlinearity mitigation techniques, such as the digital backpropagation, can be substantial. Neural networks have recently attracted attention, in this context, for low-complexity nonlinearity mitigation in fiber-optic communications. This Ph.D. dissertation deals with investigating the recurrent neural networks to efficiently compensate for the nonlinear channel impairments in dual-polarization long-haul fiber-optic transmission. We present a hybrid convolutional recurrent neural network (CRNN) architecture, comprising a convolutional neural network (CNN) -based encoder followed by a recurrent layer working in tandem. The CNN-based encoder represents the shortterm channel memory arising from the chromatic dispersion efficiently, while transitioning the signal to a latent space with fewer relevant features. The subsequent recurrent layer is implemented in the form of a unidirectional vanilla RNN, responsible for capturing the long-range interactions neglected by the CNN encoder. We demonstrate that the proposed CRNN achieves the performance of the state-of-theart equalizers in optical fiber communication, with significantly lower computational complexity depending on the system model. Finally, the performance complexity trade-off is established for a number of models, including multi-layer fully-connected neural networks, CNNs, bidirectional recurrent neural networks, bidirectional long short-term memory (bi-LSTM), bidirectional gated recurrent units, convolutional bi-LSTM models, and the suggested hybrid model
Barhoumi, Amira. "Une approche neuronale pour l’analyse d’opinions en arabe". Thesis, Le Mans, 2020. http://www.theses.fr/2020LEMA1022.
Pełny tekst źródłaMy thesis is part of Arabic sentiment analysis. Its aim is to determine the global polarity of a given textual statement written in MSA or dialectal arabic. This research area has been subject of numerous studies dealing with Indo-European languages, in particular English. One of difficulties confronting this thesis is the processing of Arabic. In fact, Arabic is a morphologically rich language which implies a greater sparsity : we want to overcome this problem by producing, in a completely automatic way, new arabic specific embeddings. Our study focuses on the use of a neural approach to improve polarity detection, using embeddings. These embeddings have revealed fundamental in various natural languages processing tasks (NLP). Our contribution in this thesis concerns several axis. First, we begin with a preliminary study of the various existing pre-trained word embeddings resources in arabic. These embeddings consider words as space separated units in order to capture semantic and syntactic similarities in the embedding space. Second, we focus on the specifity of Arabic language. We propose arabic specific embeddings that take into account agglutination and morphological richness of Arabic. These specific embeddings have been used, alone and in combined way, as input to neural networks providing an improvement in terms of classification performance. Finally, we evaluate embeddings with intrinsic and extrinsic methods specific to sentiment analysis task. For intrinsic embeddings evaluation, we propose a new protocol introducing the notion of sentiment stability in the embeddings space. We propose also a qualitaive extrinsic analysis of our embeddings by using visualisation methods
Boutin, Victor. "Etude d’un algorithme hiérarchique de codage épars et prédictif : vers un modèle bio-inspiré de la perception visuelle". Thesis, Aix-Marseille, 2020. http://www.theses.fr/2020AIXM0028.
Pełny tekst źródłaBuilding models to efficiently represent images is a central and difficult problem in the machine learning community. The neuroscientific study of the early visual cortical areas is a great source of inspiration to find economical and robust solutions. For instance, Sparse Coding (SC) is one of the most successful frameworks to model neural computation at the local scale in the visual cortex. At the structural scale of the ventral visual pathways, the Predictive Coding (PC) theory has been proposed to model top-down and bottom-up interaction between cortical regions. The presented thesis introduces a model called the Sparse Deep Predictive Coding (SDPC) that combines Sparse Coding and Predictive Coding in a hierarchical and convolutional architecture. We analyze the SPDC from a computational and a biological perspective. In terms of computation, the recurrent connectivity introduced by the PC framework allows the SDPC to converge to lower prediction errors with a higher convergence rate. In addition, we combine neuroscientific evidence with machine learning methods to analyze the impact of recurrent processing at both the neural organization and representational level. At the neural organization level, the feedback signal of the model accounted for a reorganization of the V1 association fields that promotes contour integration. At the representational level, the SDPC exhibited significant denoising ability which is highly correlated with the strength of the feedback from V2 to V1. These results from the SDPC model demonstrate that neuro-inspiration might be the right methodology to design more powerful and more robust computer vision algorithms
Pothier, Dominique. "Réseaux convolutifs à politiques". Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69184.
Pełny tekst źródłaDespite their excellent performances, artificial neural networks high demand of both data and computational power limit their adoption in many domains. Developing less demanding architecture thus remain an important endeavor. This thesis seeks to produce a more flexible and less resource-intensive architecture by using reinforcement learning theory. When considering a network as an agent instead of a function approximator, one realize that the implicit policy followed by popular feed forward networks is extremely simple. We hypothesize that an architecture able to learn a more flexible policy could reach similar performances while reducing its resource footprint. The architecture we propose is inspired by research done in weight prediction, particularly by the hypernetwork architecture, which we use as a baseline model.Our results show that learning a dynamic policy achieving similar results to the static policies of conventional networks is not a trivial task. Our proposed architecture succeeds in limiting its parameter space by 20%, but does so at the cost of a 24% computation increase and loss of5% accuracy. Despite those results, we believe that this architecture provides a baseline that can be improved in multiple ways that we describe in the conclusion.
Al, Hajj Hassan. "Video analysis for augmented cataract surgery". Thesis, Brest, 2018. http://www.theses.fr/2018BRES0041/document.
Pełny tekst źródłaThe digital era is increasingly changing the world due to the sheer volume of data produced every day. The medical domain is highly affected by this revolution, because analysing this data can be a source of education/support for the clinicians. In this thesis, we propose to reuse the surgery videos recorded in the operating rooms for computer-assisted surgery system. We are chiefly interested in recognizing the surgical gesture being performed at each instant in order to provide relevant information. To achieve this goal, this thesis addresses the surgical tool recognition problem, with applications in cataract surgery. The main objective of this thesis is to address the surgical tool recognition problem in cataract surgery videos.In the surgical field, those tools are partially visible in videos and highly similar to one another. To address the visual challenges in the cataract surgical field, we propose to add an additional camera filming the surgical tray. Our goal is to detect the tool presence in the two complementary types of videos: tool-tissue interaction and surgical tray videos. The former records the patient's eye and the latter records the surgical tray activities.Two tasks are proposed to perform the task on the surgical tray videos: tools change detection and tool presence detection.First, we establish a similar pipeline for both tasks. It is based on standard classification methods on top of visual learning features. It yields satisfactory results for the tools change task, howev-lateer, it badly performs the surgical tool presence task on the tray. Second, we design deep learning architectures for the surgical tool detection on both video types in order to address the difficulties in manually designing the visual features.To alleviate the inherent challenges on the surgical tray videos, we propose to generate simulated surgical tray scenes along with a patch-based convolutional neural network (CNN).Ultimately, we study the temporal information using RNN processing the CNN results. Contrary to our primary hypothesis, the experimental results show deficient results for surgical tool presence on the tray but very good results on the tool-tissue interaction videos. We achieve even better results in the surgical field after fusing the tool change information coming from the tray and tool presence signals on the tool-tissue interaction videos
Boné, Romuald. "Réseaux de neurones récurrents pour la prévision de séries temporelles". Tours, 2000. http://www.theses.fr/2000TOUR4003.
Pełny tekst źródłaStrock, Anthony. "Mémoire de travail dans les réseaux de neurones récurrents aléatoires". Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0195.
Pełny tekst źródłaWorking memory can be defined as the ability to temporarily store and manipulate information of any kind.For example, imagine that you are asked to mentally add a series of numbers.In order to accomplish this task, you need to keep track of the partial sum that needs to be updated every time a new number is given.The working memory is precisely what would make it possible to maintain (i.e. temporarily store) the partial sum and to update it (i.e. manipulate).In this thesis, we propose to explore the neuronal implementations of this working memory using a limited number of hypotheses.To do this, we place ourselves in the general context of recurrent neural networks and we propose to use in particular the reservoir computing paradigm.This type of very simple model nevertheless makes it possible to produce dynamics that learning can take advantage of to solve a given task.In this job, the task to be performed is a gated working memory task.The model receives as input a signal which controls the update of the memory.When the door is closed, the model should maintain its current memory state, while when open, it should update it based on an input.In our approach, this additional input is present at all times, even when there is no update to do.In other words, we require our model to be an open system, i.e. a system which is always disturbed by its inputs but which must nevertheless learn to keep a stable memory.In the first part of this work, we present the architecture of the model and its properties, then we show its robustness through a parameter sensitivity study.This shows that the model is extremely robust for a wide range of parameters.More or less, any random population of neurons can be used to perform gating.Furthermore, after learning, we highlight an interesting property of the model, namely that information can be maintained in a fully distributed manner, i.e. without being correlated to any of the neurons but only to the dynamics of the group.More precisely, working memory is not correlated with the sustained activity of neurons, which has nevertheless been observed for a long time in the literature and recently questioned experimentally.This model confirms these results at the theoretical level.In the second part of this work, we show how these models obtained by learning can be extended in order to manipulate the information which is in the latent space.We therefore propose to consider conceptors which can be conceptualized as a set of synaptic weights which constrain the dynamics of the reservoir and direct it towards particular subspaces; for example subspaces corresponding to the maintenance of a particular value.More generally, we show that these conceptors can not only maintain information, they can also maintain functions.In the case of mental arithmetic mentioned previously, these conceptors then make it possible to remember and apply the operation to be carried out on the various inputs given to the system.These conceptors therefore make it possible to instantiate a procedural working memory in addition to the declarative working memory.We conclude this work by putting this theoretical model into perspective with respect to biology and neurosciences
Etienne, Caroline. "Apprentissage profond appliqué à la reconnaissance des émotions dans la voix". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS517.
Pełny tekst źródłaThis thesis deals with the application of artificial intelligence to the automatic classification of audio sequences according to the emotional state of the customer during a commercial phone call. The goal is to improve on existing data preprocessing and machine learning models, and to suggest a model that is as efficient as possible on the reference IEMOCAP audio dataset. We draw from previous work on deep neural networks for automatic speech recognition, and extend it to the speech emotion recognition task. We are therefore interested in End-to-End neural architectures to perform the classification task including an autonomous extraction of acoustic features from the audio signal. Traditionally, the audio signal is preprocessed using paralinguistic features, as part of an expert approach. We choose a naive approach for data preprocessing that does not rely on specialized paralinguistic knowledge, and compare it with the expert approach. In this approach, the raw audio signal is transformed into a time-frequency spectrogram by using a short-term Fourier transform. In order to apply a neural network to a prediction task, a number of aspects need to be considered. On the one hand, the best possible hyperparameters must be identified. On the other hand, biases present in the database should be minimized (non-discrimination), for example by adding data and taking into account the characteristics of the chosen dataset. We study these aspects in order to develop an End-to-End neural architecture that combines convolutional layers specialized in the modeling of visual information with recurrent layers specialized in the modeling of temporal information. We propose a deep supervised learning model, competitive with the current state-of-the-art when trained on the IEMOCAP dataset, justifying its use for the rest of the experiments. This classification model consists of a four-layer convolutional neural networks and a bidirectional long short-term memory recurrent neural network (BLSTM). Our model is evaluated on two English audio databases proposed by the scientific community: IEMOCAP and MSP-IMPROV. A first contribution is to show that, with a deep neural network, we obtain high performances on IEMOCAP, and that the results are promising on MSP-IMPROV. Another contribution of this thesis is a comparative study of the output values of the layers of the convolutional module and the recurrent module according to the data preprocessing method used: spectrograms (naive approach) or paralinguistic indices (expert approach). We analyze the data according to their emotion class using the Euclidean distance, a deterministic proximity measure. We try to understand the characteristics of the emotional information extracted autonomously by the network. The idea is to contribute to research focused on the understanding of deep neural networks used in speech emotion recognition and to bring more transparency and explainability to these systems, whose decision-making mechanism is still largely misunderstood
Jouffroy, Guillaume. "Contrôle oscillatoire par réseau de neurones récurrents". Paris 8, 2008. http://www.theses.fr/2008PA082918.
Pełny tekst źródłaIn the control field, most of the applications need a non-oscillatory continuous control. This work focuses instead on controllers with recurrent neural networks (RNN) which generate a periodic oscillatory control. The purpose of the present work is to study stochastic optimisation methods which can be used to discover the parameters of a network so that it generates a cyclic input. First we take a look at the knowledge about biological oscillators. Tthen we describe the mathematical tools to be able to guarantee the stability oscillators. The potential of RNN, especially applied to dynamical systems being still poorly used, we propose for each method, a general detailed matrix formalization and we precise the computational complexity of the methods. We validate each method using a simple example of oscillator, and we demonstrate analytically the stability of the resulting oscillator, but also how it is robust to parameters perturbations. We then compare these different methods with these criteria and the speed of convergence. We finish this thesis with an illustration, where we take all the steps of the construction of an oscillatory neural controller, to control the axis of direction of a particular vehicle. This will let us discuss how realistic is the use of recurrent neural networks in the field of control, and propose interesting questions
Jodouin, 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 źródłaCzęści książek na temat "Réseaux de neurones récurrents convolutifs"
MOLINIER, 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 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 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 récurrents convolutifs"
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 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.
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