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Artykuły w czasopismach na temat "Apprentisage en profondeur"
Quinqueton, Joël. "Métaconnaissance ou Apprentissage en profondeur ?" Revue Ouverte d'Intelligence Artificielle 3, nr 1-2 (23.03.2022): 51–58. http://dx.doi.org/10.5802/roia.17.
Pełny tekst źródłaDesprés, Jean-Philippe, Pamela Burnard, Francis Dubé i Sophie Stévance. "Cadre pédagogique pour l’enseignement-apprentissage de l’improvisation musicale classique fondé sur la pratique des experts du domaine". Articles 35, nr 2 (15.03.2018): 3–36. http://dx.doi.org/10.7202/1043820ar.
Pełny tekst źródłaChakri, Lekbir, i 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.
Pełny tekst źródłaLarue, Caroline. "Les stratégies d’apprentissage d’étudiantes durant le travail de groupe dans un curriculum centré sur la résolution de problèmes". Revue des sciences de l'éducation 33, nr 2 (1.05.2008): 467–88. http://dx.doi.org/10.7202/017888ar.
Pełny tekst źródłaAgnès, François, Marine Moyon i Morgane Locker. "Engagement en situation de cours ou de travaux dirigés : impacts d’un dispositif de classe inversée en licence de sciences de la vie". Didactique 5, nr 1 (24.01.2024): 1–41. http://dx.doi.org/10.37571/2024.0101.
Pełny tekst źródłaAgnès, François, Marine Moyon i Morgane Locker. "Engagement en situation de cours ou de travaux dirigés : impacts d’un dispositif de classe inversée en licence de sciences de la vie". Didactique 5, nr 2 (5.05.2024): 57–97. http://dx.doi.org/10.37571/2024.0203.
Pełny tekst źródłaLEE, Kyeong-Soo. "Comment profiter du changement du nouveau programme officiel du français ?: centré sur la liste du vocabulaire de base". Societe d'Etudes Franco-Coreennes 104 (30.04.2024): 161–79. http://dx.doi.org/10.18812/refc.2024.104.161.
Pełny tekst źródłaMinuk, Alexandra, Pamela Beach i Elena Favret. "Evaluating Online Environments for Elementary Teachers’ Literacy-Oriented Professional Learning". Alberta Journal of Educational Research 69, nr 1 (17.03.2023): 118–40. http://dx.doi.org/10.55016/ojs/ajer.v69i1.75743.
Pełny tekst źródłaTremblay, Karine N., Ruth Philion, André C. Moreau, Julie Ruel, Ernesto Morales, Maryse Feliziani i 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, nr 1 (22.06.2023): 184–217. http://dx.doi.org/10.1522/rhe.v7i1.1472.
Pełny tekst źródłaEndersby, Lisa, i Geneviève Maheux-Pelletier. "Guiding a Better Experiential Learning Journey by Making It HIP Again". Collected Essays on Learning and Teaching 13 (28.10.2020): 57–75. http://dx.doi.org/10.22329/celt.v13i0.6018.
Pełny tekst źródłaRozprawy doktorskie na temat "Apprentisage en profondeur"
Palli, Thazha Vyshakh. "Using context-cues and interaction for traffic-agent trajectory prediction". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAE001.
Pełny tekst źródłaAutonomous vehicle navigation in urban areas involves interactions with the different road-users or traffic-agents like cars, bicycles, and pedestrians, sharing the same road network. The ability of autonomous vehicle to observe, understand and predict the behaviour of these traffic-agents is very important to gain a good situation understanding prior to deciding what manoeuvre to follow. While this is achieved to various degrees of success using model-based or data-driven methods, human drivers remain much more efficient at this task, instinctively inferring different agent motions even in previously unseen and challenging situations. Moreover, context plays a very important role that enables us humans to understand what is being perceived and make finer predictions. The need to increase situational awareness of autonomous vehicles, as well as for safety related driving assistance functions, stimulates our goal to exploit contextual information to predict the future trajectories of the observed traffic-agents in different conditions.Over the past years, machine learning has proven to be efficient at solving a wide variety of problems, particularly those associated to machine perception. This thesis therefore focuses on developing machine learning models to exploit contextual information in order to observe and learn the trajectories of different interacting traffic-agents as perceived from an autonomous vehicle. While most models proposed in the past rely on a single sensor and model-based techniques, the current approaches often rely on the use of multiple sensors and process their outputs using different machine learning methods. The approach proposed in this thesis follows these trends by combining information from different sensors to predict the trajectories of the observed traffic-agents using machine learning, as well as integrating contextual information and interactions into the prediction process.The thesis gradually builds a machine learning architecture based on a theoretical formulation and experimentation. Our approach is based on an LSTM encoder-decoder model that accepts data from different inputs. Trajectory observations from 3D LiDAR point-cloud data and semantic information from map-masks are used. Map masks represent areas where the traffic-agents can operate or not, in a binary manner. The information on pedestrian attention to oncoming vehicles obtained from camera images is also exploited to enrich the sequence prediction system. The goal is to feed the model with context-cues and semantic information to enhance the prediction of the traffic-agent trajectories, by knowing whether or not the agents are aware of the presence of the subject vehicle and including knowledge on areas where they are likely to navigate. Moreover, interactions of the autonomous vehicle with traffic-agents often govern its behaviour as the vehicle navigates. A mechanism to incorporate this information to the machine learning model is also developed as an interaction-aware trajectory prediction system enhanced by context-cues.Machine learning architectures are built using datasets acquired from the perception sensors of a vehicle navigating in the expected workspace. As datasets play an important role in solving machine learning problems, available annotated datasets for autonomous navigation were reviewed according to their availability of sensor data and contextual information. Experiments were performed for our models to learn, and gradually build the resulting architecture. Their performance are demonstrated using the well-known NuScenes dataset acquired in urban settings. The performance of the proposed approach were compared with model and data-driven approaches, demonstrating that the incorporation of multiple contextual information and agent interactions provides a substantial performance increase
Goh, Hanlin. "Apprentissage de Représentations Visuelles Profondes". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2013. http://tel.archives-ouvertes.fr/tel-00948376.
Pełny tekst źródłaMoukari, Michel. "Estimation de profondeur à partir d'images monoculaires par apprentissage profond". Thesis, Normandie, 2019. http://www.theses.fr/2019NORMC211/document.
Pełny tekst źródłaComputer vision is a branch of artificial intelligence whose purpose is to enable a machine to analyze, process and understand the content of digital images. Scene understanding in particular is a major issue in computer vision. It goes through a semantic and structural characterization of the image, on one hand to describe its content and, on the other hand, to understand its geometry. However, while the real space is three-dimensional, the image representing it is two-dimensional. Part of the 3D information is thus lost during the process of image formation and it is therefore non trivial to describe the geometry of a scene from 2D images of it.There are several ways to retrieve the depth information lost in the image. In this thesis we are interested in estimating a depth map given a single image of the scene. In this case, the depth information corresponds, for each pixel, to the distance between the camera and the object represented in this pixel. The automatic estimation of a distance map of the scene from an image is indeed a critical algorithmic brick in a very large number of domains, in particular that of autonomous vehicles (obstacle detection, navigation aids).Although the problem of estimating depth from a single image is a difficult and inherently ill-posed problem, we know that humans can appreciate distances with one eye. This capacity is not innate but acquired and made possible mostly thanks to the identification of indices reflecting the prior knowledge of the surrounding objects. Moreover, we know that learning algorithms can extract these clues directly from images. We are particularly interested in statistical learning methods based on deep neural networks that have recently led to major breakthroughs in many fields and we are studying the case of the monocular depth estimation
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
Peiffer, Elsa. "Implications des structures cérébrales profondes dans les apprentissages procéduraux". Lyon 1, 2000. http://www.theses.fr/2000LYO1T267.
Pełny tekst źródłaMordan, Taylor. "Conception d'architectures profondes pour l'interprétation de données visuelles". Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS270.
Pełny tekst źródłaNowadays, images are ubiquitous through the use of smartphones and social media. It then becomes necessary to have automatic means of processing them, in order to analyze and interpret the large amount of available data. In this thesis, we are interested in object detection, i.e. the problem of identifying and localizing all objects present in an image. This can be seen as a first step toward a complete visual understanding of scenes. It is tackled with deep convolutional neural networks, under the Deep Learning paradigm. One drawback of this approach is the need for labeled data to learn from. Since precise annotations are time-consuming to produce, bigger datasets can be built with partial labels. We design global pooling functions to work with them and to recover latent information in two cases: learning spatially localized and part-based representations from image- and object-level supervisions respectively. We address the issue of efficiency in end-to-end learning of these representations by leveraging fully convolutional networks. Besides, exploiting additional annotations on available images can be an alternative to having more images, especially in the data-deficient regime. We formalize this problem as a specific kind of multi-task learning with a primary objective to focus on, and design a way to effectively learn from this auxiliary supervision under this framework
Trullo, Ramirez Roger. "Approche basées sur l'apprentissage en profondeur pour la segmentation des organes à risques dans les tomodensitométries thoraciques". Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR063.
Pełny tekst źródłaRadiotherapy is one of the options for treatment currently available for patients affected by cancer, one of the leading cause of deaths worldwide. Before radiotherapy, organs at risk (OAR) located near the target tumor, such as the heart, the lungs, the esophagus, etc. in thoracic cancer, must be outlined, in order to minimize the quantity of irradiation that they receive during treatment. Today, segmentation of the OAR is performed mainly manually by clinicians on Computed Tomography (CT) images, despite some partial software support. It is a tedious task, prone to intra and inter-observer variability. In this work, we present several frameworks using deep learning techniques to automatically segment the heart, trachea, aorta and esophagus. In particular, the esophagus is notably challenging to segment, due to the lack of surrounding contrast and shape variability across different patients. As deep networks and in particular fully convolutional networks offer now state of the art performance for semantic segmentation, we first show how a specific type of architecture based on skip connections can improve the accuracy of the results. As a second contribution, we demonstrate that context information can be of vital importance in the segmentation task, where we propose the use of two collaborative networks. Third, we propose a different, distance aware representation of the data, which is then used in junction with adversarial networks, as another way to constrain the anatomical context. All the proposed methods have been tested on 60 patients with 3D-CT scans, showing good performance compared with other methods
Chandra, Siddhartha. "Apprentissage Profond pour des Prédictions Structurées Efficaces appliqué à la Classification Dense en Vision par Ordinateur". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC033/document.
Pełny tekst źródłaIn this thesis we propose a structured prediction technique that combines the virtues of Gaussian Conditional Random Fields (G-CRFs) with Convolutional Neural Networks (CNNs). The starting point of this thesis is the observation that while being of a limited form GCRFs allow us to perform exact Maximum-APosteriori (MAP) inference efficiently. We prefer exactness and simplicity over generality and advocate G-CRF based structured prediction in deep learning pipelines. Our proposed structured prediction methods accomodate (i) exact inference, (ii) both shortand long- term pairwise interactions, (iii) rich CNN-based expressions for the pairwise terms, and (iv) end-to-end training alongside CNNs. We devise novel implementation strategies which allow us to overcome memory and computational challenges
Pinheiro, de Carvalho Marcela. "Deep Depth from Defocus : Neural Networks for Monocular Depth Estimation". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS609.
Pełny tekst źródłaDepth estimation from a single image is a key instrument for several applications from robotics to virtual reality. Successful Deep Learning approaches in computer vision tasks as object recognition and classification also benefited the domain of depth estimation. In this thesis, we develop methods for monocular depth estimation with deep neural network by exploring different cues: defocus blur and semantics. We conduct several experiments to understand the contribution of each cue in terms of generalization and model performance. At first, we propose an efficient convolutional neural network for depth estimation along with a conditional Generative Adversarial framework. Our method achieves performances among the best on standard datasets for depth estimation. Then, we propose to explore defocus blur cues, which is an optical information deeply related to depth. We show that deep models are able to implicitly learn and use this information to improve performance and overcome known limitations of classical Depth-from-Defocus. We also build a new dataset with real focused and defocused images that we use to validate our approach. Finally, we explore the use of semantic information, which brings rich contextual information while learned jointly to depth on a multi-task approach. We validate our approaches with several datasets containing indoor, outdoor and aerial images
M'Saad, Soumaya. "Détection de changement de comportement de vie chez la personne âgée par images de profondeur". Thesis, Rennes 1, 2022. http://www.theses.fr/2022REN1S039.
Pełny tekst źródłaThe number of elderly people in the world is constantly increasing, hence the challenge of helping them to continue to live at home and ageing in good health. This PhD takes part in this public health issue and proposes the detection of the person behavior change based on the recording of activities in the home by low-cost depth sensors that guarantee anonymity and that operate autonomously day and night. After an initial study combining image classification by machine learning approaches, a method based on Resnet-18 deep neural networks was proposed for fall and posture position detection. This approach gave good results with a global accuracy of 93.44% and a global sensitivity of 93.24%. The detection of postures makes possible to follow the state of the person and in particular the behavior changes which are assumed to be the routine loss. Two strategies were deployed to monitor the routine. The first one examines the succession of activities in the day by computing an edit distance or a dynamic deformation of the day, the other one consists in classifying the day into routine and non-routine by combining supervised (k-means and k-modes), unsupervised (Random Forest) or a priori knowledge about the person's routine. These strategies were evaluated both on real data recorded in EHPAD in two frail people and on simulated data created to fill the lack of real data. They have shown the possibility to detect different behavioral change scenarios (abrupt, progressive, recurrent) and prove that depth sensors can be used in EHPAD or in the home of an elderly person
Książki na temat "Apprentisage en profondeur"
Patenaude, Jean-Victor. Les maladies thrombo-emboliques veineuses: Module d'auto-apprentissage : les thrombophlébites superficielles et profondes, les embolies pulmonaires. Wyd. 2. Montréal: Presses de l'Université de Montréal, 1998.
Znajdź pełny tekst źródłaCzęści książek na temat "Apprentisage en profondeur"
Koishi, Atsuko. "Comment dépasser le «monolinguisme» au Japon ?" W Le Japon, acteur de la Francophonie, 49–58. Editions des archives contemporaines, 2016. http://dx.doi.org/10.17184/eac.5526.
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