Literatura científica selecionada sobre o tema "Apprentissages profond"
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Artigos de revistas sobre o assunto "Apprentissages profond"
Chakri, Lekbir, e My Lhassan Riouch. "Apports des TIC dans l'enseignement et l’apprentissage des mathématiques : Scénarisation pédagogique et pratiques de l'enseignement à distance". ITM Web of Conferences 39 (2021): 03012. http://dx.doi.org/10.1051/itmconf/20213903012.
Texto completo da fonteFillières-Riveau, Gauthier, Jean-Marie Favreau, Vincent Barra e Guillaume Touya. "Génération de cartes tactiles photoréalistes pour personnes déficientes visuelles par apprentissage profond". Revue Internationale de Géomatique 30, n.º 1-2 (janeiro de 2020): 105–26. http://dx.doi.org/10.3166/rig.2020.00104.
Texto completo da fontePouliquen, Geoffroy, e Catherine Oppenheim. "Débruitage par apprentissage profond: impact sur les biomarqueurs quantitatifs des tumeurs cérébrales". Journal of Neuroradiology 49, n.º 2 (março de 2022): 136. http://dx.doi.org/10.1016/j.neurad.2022.01.040.
Texto completo da fonteAbadie, Pierre, Pierre Yves Herve, Benjamin Dallaudiere, Philippe Meyer, Lionel Pesquer, Nicolas Poussange e Alain Silvestre. "Apprentissage profond pour la prise en charge décisionnelle des lésions IRM du genou". Revue de Chirurgie Orthopédique et Traumatologique 105, n.º 8 (dezembro de 2019): S123. http://dx.doi.org/10.1016/j.rcot.2019.09.068.
Texto completo da fonteFouquet, Guillaume. "60 ans démunis devant 30 ans !" Gestalt 59, n.º 2 (7 de julho de 2023): 103–14. http://dx.doi.org/10.3917/gest.059.0103.
Texto completo da fonteTremblay, Karine N., Ruth Philion, André C. Moreau, Julie Ruel, Ernesto Morales, Maryse Feliziani e Laurie-Ann Garneau-Gaudreault. "Bilan des contributions et retombées perçues de l’implantation d’une communauté de pratique auprès d’une équipe-école". Revue hybride de l'éducation 7, n.º 1 (22 de junho de 2023): 184–217. http://dx.doi.org/10.1522/rhe.v7i1.1472.
Texto completo da fonteHarel, Simon. "Transfiguration et persona : Régine, Pavillon Read de l’UQÀM, vers 1977". Romanica Silesiana 24, n.º 2 (3 de outubro de 2023): 1–10. http://dx.doi.org/10.31261/rs.2023.24.01.
Texto completo da fonteCaccamo, Emmanuelle, e Fabien Richert. "Les procédés algorithmiques au prisme des approches sémiotiques". Cygne noir, n.º 7 (1 de junho de 2022): 1–16. http://dx.doi.org/10.7202/1089327ar.
Texto completo da fonteBaudouin, Maxime. "Détection d'anévrisme intracrânien par apprentissage profond sur l'irm tof à l'aide d'un u-net régularisé à deux niveaux". Journal of Neuroradiology 50, n.º 2 (março de 2023): 187–89. http://dx.doi.org/10.1016/j.neurad.2023.01.129.
Texto completo da fontePrakash, Prem, Marc Sebban, Amaury Habrard, Jean-Claude Barthelemy, Frédéric Roche e Vincent Pichot. "Détection automatique des apnées du sommeil sur l’ECG nocturne par un apprentissage profond en réseau de neurones récurrents (RNN)". Médecine du Sommeil 18, n.º 1 (março de 2021): 43–44. http://dx.doi.org/10.1016/j.msom.2020.11.077.
Texto completo da fonteTeses / dissertações sobre o assunto "Apprentissages profond"
Hassanaly, Ravi. "Pseudo-healthy image reconstruction with deep generative models for the detection of dementia-related anomalies". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS118.
Texto completo da fonteNeuroimaging has become an essential tool in the study of markers of Alzheimer's disease. However, analyzing complex multimodal brain images remains a major challenge for clinicians. To overcome this difficulty, deep learning methods have emerged as a promising solution for the automatic and robust analysis of neuroimaging data. In this thesis, we explore the use of deep generative models for the detection of anomalies associated with dementia in 18F-fluorodesoxyglucose positron emission tomography (FDG PET) data. Our method is based on the principle of pseudo-healthy reconstruction, where we train a generative model to reconstruct healthy images from pathological data. This approach has the advantage of not requiring annotated data, which are time-consuming and costly to acquire, as well as being generalizable to different types of anomalies. We chose to implement a variational autoencoder (VAE), a simple model, but that proved its worth in the field of deep learning. However, assessing the performance of our generative models without labeled data or ground truth anomaly maps leads to an incomplete evaluation. To solve this issue, we have introduced an evaluation framework based on the simulation of hypometabolism on FDG PET images. Thus, by creating pairs of healthy and diseased images, we are able to assess the model's ability to reconstruct pseudo-healthy images. In addition, this methodology has enabled us to define new metrics for assessing the quality of reconstructions obtained from generative models. The evaluation framework allowed us to carry out a comparative study on twenty VAE variants in the context of FDG PET pseudo-healthy reconstruction. The proposed benchmark enabled us to identify the best-performing models for detecting dementia-related anomalies. Finally, several significant contributions have been made to open-source software. A PET image processing pipeline has been integrated into the Clinica software. In addition, this thesis gave rise to numerous contributions to the development of the ClinicaDL software, including its improvement, the addition of new functionalities, software maintenance and participation in project management
Béthune, Louis. "Apprentissage profond avec contraintes Lipschitz". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES014.
Texto completo da fonteThis 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
Vialatte, Jean-Charles. "Convolution et apprentissage profond sur graphes". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0118/document.
Texto completo da fonteConvolutional neural networks have proven to be the deep learning model that performs best on regularly structured datasets like images or sounds. However, they cannot be applied on datasets with an irregular structure (e.g. sensor networks, citation networks, MRIs). In this thesis, we develop an algebraic theory of convolutions on irregular domains. We construct a family of convolutions that are based on group actions (or, more generally, groupoid actions) that acts on the vertex domain and that have properties that depend on the edges. With the help of these convolutions, we propose extensions of convolutional neural netowrks to graph domains. Our researches lead us to propose a generic formulation of the propagation between layers, that we call the neural contraction. From this formulation, we derive many novel neural network models that can be applied on irregular domains. Through benchmarks and experiments, we show that they attain state-of-the-art performances, and beat them in some cases
Terreau, Enzo. "Apprentissage de représentations d'auteurs et d'autrices à partir de modèles de langue pour l'analyse des dynamiques d'écriture". Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20001.
Texto completo da fonteThe recent and massive democratization of digital tools has empowered individuals to generate and share information on the web through various means such as blogs, social networks, sharing platforms, and more. The exponential growth of available information, mostly textual data, requires the development of Natural Language Processing (NLP) models to mathematically represent it and subsequently classify, sort, or recommend it. This is the essence of representation learning. It aims to construct a low-dimensional space where the distances between projected objects (words, texts) reflect real-world distances, whether semantic, stylistic, and so on.The proliferation of available data, coupled with the rise in computing power and deep learning, has led to the creation of highly effective language models for word and document embeddings. These models incorporate complex semantic and linguistic concepts while remaining accessible to everyone and easily adaptable to specific tasks or corpora. One can use them to create author embeddings. However, it is challenging to determine the aspects on which a model will focus to bring authors closer or move them apart. In a literary context, it is preferable for similarities to primarily relate to writing style, which raises several issues. The definition of literary style is vague, assessing the stylistic difference between two texts and their embeddings is complex. In computational linguistics, approaches aiming to characterize it are mainly statistical, relying on language markers. In light of this, our first contribution is a framework to evaluate the ability of language models to grasp writing style. We will have previously elaborated on text embedding models in machine learning and deep learning, at the word, document, and author levels. We will also have presented the treatment of the notion of literary style in Natural Language Processing, which forms the basis of our method. Transferring knowledge between black-box large language models and these methods derived from linguistics remains a complex task. Our second contribution aims to reconcile these approaches through a representation learning model focusing on style, VADES (Variational Author and Document Embedding with Style). We compare our model to state-of-the-art ones and analyze their limitations in this context.Finally, we delve into dynamic author and document embeddings. Temporal information is crucial, allowing for a more fine-grained representation of writing dynamics. After presenting the state of the art, we elaborate on our last contribution, B²ADE (Brownian Bridge Author and Document Embedding), which models authors as trajectories. We conclude by outlining several leads for improving our methods and highlighting potential research directions for the future
Katranji, Mehdi. "Apprentissage profond de la mobilité des personnes". Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA024.
Texto completo da fonteKnowledge of mobility is a major challenge for authorities mobility organisers and urban planning. Due to the lack of formal definition of human mobility, the term "people's mobility" will be used in this book. This topic will be introduced by a description of the ecosystem by considering these actors and applications.The creation of a learning model has prerequisites: an understanding of the typologies of the available data sets, their strengths and weaknesses. This state of the art in mobility knowledge is based on the four-step model that has existed and been used since 1970, ending with the renewal of the methodologies of recent years.Our models of people's mobility are then presented. Their common point is the emphasis on the individual, unlike traditional approaches that take the locality as a reference. The models we propose are based on the fact that the intake of individuals' decisions is based on their perception of the environment.This finished book on the study of the deep learning methods of Boltzmann machines restricted. After a state of the art of this family of models, we are looking for strategies to make these models viable in the application world. This last chapter is our contribution main theoretical, by improving robustness and performance of these models
Deschaintre, Valentin. "Acquisition légère de matériaux par apprentissage profond". Thesis, Université Côte d'Azur (ComUE), 2019. http://theses.univ-cotedazur.fr/2019AZUR4078.
Texto completo da fonteWhether it is used for entertainment or industrial design, computer graphics is ever more present in our everyday life. Yet, reproducing a real scene appearance in a virtual environment remains a challenging task, requiring long hours from trained artists. A good solution is the acquisition of geometries and materials directly from real world examples, but this often comes at the cost of complex hardware and calibration processes. In this thesis, we focus on lightweight material appearance capture to simplify and accelerate the acquisition process and solve industrial challenges such as result image resolution or calibration. Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in pictures. Designing algorithms able to leverage these cues to recover spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a few images has challenged computer graphics researchers for decades. We explore the use of deep learning to tackle lightweight appearance capture and make sense of these visual cues. Once trained, our networks are capable of recovering per-pixel normals, diffuse albedo, specular albedo and specular roughness from as little as one picture of a flat surface lit by the environment or a hand-held flash. We show how our method improves its prediction with the number of input pictures to reach high quality reconstructions with up to 10 images --- a sweet spot between existing single-image and complex multi-image approaches --- and allows to capture large scale, HD materials. We achieve this goal by introducing several innovations on training data acquisition and network design, bringing clear improvement over the state of the art for lightweight material capture
Paumard, Marie-Morgane. "Résolution automatique de puzzles par apprentissage profond". Thesis, CY Cergy Paris Université, 2020. http://www.theses.fr/2020CYUN1067.
Texto completo da fonteThe objective of this thesis is to develop semantic methods of reassembly in the complicated framework of heritage collections, where some blocks are eroded or missing.The reassembly of archaeological remains is an important task for heritage sciences: it allows to improve the understanding and conservation of ancient vestiges and artifacts. However, some sets of fragments cannot be reassembled with techniques using contour information or visual continuities. It is then necessary to extract semantic information from the fragments and to interpret them. These tasks can be performed automatically thanks to deep learning techniques coupled with a solver, i.e., a constrained decision making algorithm.This thesis proposes two semantic reassembly methods for 2D fragments with erosion and a new dataset and evaluation metrics.The first method, Deepzzle, proposes a neural network followed by a solver. The neural network is composed of two Siamese convolutional networks trained to predict the relative position of two fragments: it is a 9-class classification. The solver uses Dijkstra's algorithm to maximize the joint probability. Deepzzle can address the case of missing and supernumerary fragments, is capable of processing about 15 fragments per puzzle, and has a performance that is 25% better than the state of the art.The second method, Alphazzle, is based on AlphaZero and single-player Monte Carlo Tree Search (MCTS). It is an iterative method that uses deep reinforcement learning: at each step, a fragment is placed on the current reassembly. Two neural networks guide MCTS: an action predictor, which uses the fragment and the current reassembly to propose a strategy, and an evaluator, which is trained to predict the quality of the future result from the current reassembly. Alphazzle takes into account the relationships between all fragments and adapts to puzzles larger than those solved by Deepzzle. Moreover, Alphazzle is compatible with constraints imposed by a heritage framework: at the end of reassembly, MCTS does not access the reward, unlike AlphaZero. Indeed, the reward, which indicates if a puzzle is well solved or not, can only be estimated by the algorithm, because only a conservator can be sure of the quality of a reassembly
Haykal, Vanessa. "Modélisation des séries temporelles par apprentissage profond". Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4019.
Texto completo da fonteTime series prediction is a problem that has been addressed for many years. In this thesis, we have been interested in methods resulting from deep learning. It is well known that if the relationships between the data are temporal, it is difficult to analyze and predict accurately due to non-linear trends and the existence of noise specifically in the financial and electrical series. From this context, we propose a new hybrid noise reduction architecture that models the recursive error series to improve predictions. The learning process fusessimultaneouslyaconvolutionalneuralnetwork(CNN)andarecurrentlongshort-term memory network (LSTM). This model is distinguished by its ability to capture globally a variety of hybrid properties, where it is able to extract local signal features, to learn long-term and non-linear dependencies, and to have a high noise resistance. The second contribution concerns the limitations of the global approaches because of the dynamic switching regimes in the signal. We present a local unsupervised modification with our previous architecture in order to adjust the results by adapting the Hidden Markov Model (HMM). Finally, we were also interested in multi-resolution techniques to improve the performance of the convolutional layers, notably by using the variational mode decomposition method (VMD)
Sors, Arnaud. "Apprentissage profond pour l'analyse de l'EEG continu". Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAS006/document.
Texto completo da fonteThe objective of this research is to explore and develop machine learning methods for the analysis of continuous electroencephalogram (EEG). Continuous EEG is an interesting modality for functional evaluation of cerebral state in the intensive care unit and beyond. Today its clinical use remains more limited that it could be because interpretation is still mostly performed visually by trained experts. In this work we develop automated analysis tools based on deep neural models.The subparts of this work hinge around post-anoxic coma prognostication, chosen as pilot application. A small number of long-duration records were performed and available existing data was gathered from CHU Grenoble. Different components of a semi-supervised architecture that addresses the application are imagined, developed, and validated on surrogate tasks.First, we validate the effectiveness of deep neural networks for EEG analysis from raw samples. For this we choose the supervised task of sleep stage classification from single-channel EEG. We use a convolutional neural network adapted for EEG and we train and evaluate the system on the SHHS (Sleep Heart Health Study) dataset. This constitutes the first neural sleep scoring system at this scale (5000 patients). Classification performance reaches or surpasses the state of the art.In real use for most clinical applications, the main challenge is the lack of (and difficulty of establishing) suitable annotations on patterns or short EEG segments. Available annotations are high-level (for example, clinical outcome) and therefore they are few. We search how to learn compact EEG representations in an unsupervised/semi-supervised manner. The field of unsupervised learning using deep neural networks is still young. To compare to existing work we start with image data and investigate the use of generative adversarial networks (GANs) for unsupervised adversarial representation learning. The quality and stability of different variants are evaluated. We then apply Gradient-penalized Wasserstein GANs on EEG sequences generation. The system is trained on single channel sequences from post-anoxic coma patients and is able to generate realistic synthetic sequences. We also explore and discuss original ideas for learning representations through matching distributions in the output space of representative networks.Finally, multichannel EEG signals have specificities that should be accounted for in characterization architectures. Each EEG sample is an instantaneous mixture of the activities of a number of sources. Based on this statement we propose an analysis system made of a spatial analysis subsystem followed by a temporal analysis subsystem. The spatial analysis subsystem is an extension of source separation methods built with a neural architecture with adaptive recombination weights, i.e. weights that are not learned but depend on features of the input. We show that this architecture learns to perform Independent Component Analysis if it is trained on a measure of non-gaussianity. For temporal analysis, standard (shared) convolutional neural networks applied on separate recomposed channels can be used
Sheikh, Shakeel Ahmad. "Apprentissage profond pour la détection du bégaiement". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0005.
Texto completo da fonteStuttering is a speech disorder that is most frequently observed among speech impairments and results in the form of core behaviours. The tedious and time-consuming task of detecting and analyzing speech patterns of PWS, with the goal of rectifying them is often handled manually by speech therapists, and is biased towards their subjective beliefs. Moreover, the ASR systems also fail to recognize the stuttered speech, which makes it impractical for PWS to access virtual digital assistants such as Siri, Alexa, etc.This thesis tries to develop audio based SD systems that successfully capture different variabilities from stuttering utterances such as speaking styles, age, accents, etc., and learns robust stuttering representations with an aim to provide a fair, consistent, and unbiased assessment of stuttered speech.While most of the existing SD systems use multiple binary classifiers for each stutter type, we present a unified multi-class StutterNet capable of detecting multiple stutter types. Approaching the class-imbalance problem in stuttering domain, we investigated the impact of applying weighted loss function, and, also presented Multi-contextual (MC) Multi-branch (MB) StutterNet to improve the detection performance of minority classes.Exploiting the speaker information with an assumption that the stuttering models should be invariant to meta-data such as speaker information, we present, an adversarial MTL SD method that learns robust stutter discrimintaive speaker-invariant representations.Due to paucity of unlabeled data, the automated SD task is limited in its use of large deep models in capturing different varaibilities, we introduced the first-ever SSL framework to SD domain. The SSL framework first trains a feature extractor for a pre-text task using a large quantity of unlabeled non-stuttering audio data to capture these different varaibilities, and then applies the learned feature extractor to a downstream SD task using limited labeled stuttering audio data
Livros sobre o assunto "Apprentissages profond"
M, Senge Peter, e Society for Organizational Learning, eds. Presence: Exploring profound change in people, organizations, and society. New York: Doubleday, 2005.
Encontre o texto completo da fontePatenaude, Jean-Victor. Les maladies thrombo-emboliques veineuses: Module d'auto-apprentissage : les thrombophlébites superficielles et profondes, les embolies pulmonaires. 2a ed. Montréal: Presses de l'Université de Montréal, 1998.
Encontre o texto completo da fonteM. I. A. - Apprentissage Profond. Quebec Amerique, 2024.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Apprentissages profond"
FLEURY SOARES, Gustavo, e Induraj PUDHUPATTU RAMAMURTHY. "Comparaison de modèles d’apprentissage automatique et d’apprentissage profond". In Optimisation et apprentissage, 153–71. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch6.
Texto completo da fonteJACQUEMONT, Mikaël, Thomas VUILLAUME, Alexandre BENOIT, Gilles MAURIN e Patrick LAMBERT. "Analyse d’images Cherenkov monotélescope par apprentissage profond". In Inversion et assimilation de données de télédétection, 303–35. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9142.ch9.
Texto completo da fonteATIEH, Mirna, Omar MOHAMMAD, Ali SABRA e Nehme RMAYTI. "IdO, apprentissage profond et cybersécurité dans la maison connectée : une étude". In Cybersécurité des maisons intelligentes, 215–56. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9086.ch6.
Texto completo da fonteTKACHENKO, Iuliia, Alain TREMEAU e Thierry FOURNEL. "Protection de documents par impression d’éléments anticopies". In Sécurité multimédia 2, 41–69. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9027.ch2.
Texto completo da fonteCOGRANNE, Rémi, Marc CHAUMONT e Patrick BAS. "Stéganalyse : détection d’information cachée dans des contenus multimédias". In Sécurité multimédia 1, 261–303. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9026.ch8.
Texto completo da fonteKoishi, Atsuko. "Comment dépasser le «monolinguisme» au Japon ?" In Le Japon, acteur de la Francophonie, 49–58. Editions des archives contemporaines, 2016. http://dx.doi.org/10.17184/eac.5526.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Apprentissages profond"
Fourcade, A. "Apprentissage profond : un troisième oeil pour les praticiens". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601014.
Texto completo da fonte