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Artykuły w czasopismach na temat "Profondeur statistique"
Singh, Bhawan, i Richard Taillefer. "Le bilan énergétique et le gel au sol : une analyse statistique". Géographie physique et Quaternaire 38, nr 2 (29.11.2007): 135–47. http://dx.doi.org/10.7202/032548ar.
Pełny tekst źródłaCosta, Carla Barroso da, i Anderson Araújo-Oliveira. "MÉTACOGNITION, ÉTATS AFFECTIFS ET ENGAGEMENT COGNITIF CHEZ DES ÉTUDIANTS UNIVERSITAIRES: TRIADE PERCUTANTE POUR L’APPRENTISSAGE ET L’INCLUSION". Revista Educativa - Revista de Educação 23, nr 1 (20.01.2021): 8602. http://dx.doi.org/10.18224/educ.v23i1.8602.
Pełny tekst źródłaBrunet, Etienne. "La lexicométrie française : naissance, évolution et perspectives". Articles 45, nr 1-2 (8.02.2017): 13–33. http://dx.doi.org/10.7202/1038899ar.
Pełny tekst źródłaAssemian, Emile Assie, Fernand Koffi Kouamé, Mahaman Bachir Saley, Kouadio Affian, Marc Youan Ta, Jean Patrice Roger Jourda i Jean Biemi. "Étude de la productivité d’un aquifère de socle et approche statistique pour la détermination des tranches de profondeurs potentiellement productives : cas de la région de Bongouanou, est de la Côte d’Ivoire". Revue des sciences de l’eau 27, nr 1 (29.01.2014): 81–97. http://dx.doi.org/10.7202/1021984ar.
Pełny tekst źródłaCaissie, D., N. El-Jabi i G. Bourgeois. "Évaluation du débit réservé par méthodes hydrologiques et hydrobiologiques". Revue des sciences de l'eau 11, nr 3 (12.04.2005): 347–64. http://dx.doi.org/10.7202/705311ar.
Pełny tekst źródłaDemazeux, S. "DSM-5 : pourquoi avoir supprimé le système multiaxial ?" European Psychiatry 28, S2 (listopad 2013): 34–35. http://dx.doi.org/10.1016/j.eurpsy.2013.09.084.
Pełny tekst źródłaBeaud, Jean-Pierre, i Jean-Guy Prévost. "Un regard nouveau sur la prospérité. L’émergence des statistiques économiques au Canada (1891-1911)". Hors thème, nr 18-19 (19.04.2011): 277–95. http://dx.doi.org/10.7202/1002312ar.
Pełny tekst źródłaBrabet, Julienne. "Faut-il encore parler d'approche qualitative et d'approche quantitative?" Recherche et Applications en Marketing (French Edition) 3, nr 1 (marzec 1988): 75–89. http://dx.doi.org/10.1177/076737018800300105.
Pełny tekst źródłaLudwig, Antoinette, Michel Begras-Poulin, Stephane Lair i Denise Belanger. "Morphological Description of American Crow, Corvus brachyrhynchos, Populations in Southern Quebec". Canadian Field-Naturalist 123, nr 2 (1.04.2009): 133. http://dx.doi.org/10.22621/cfn.v123i2.691.
Pełny tekst źródłaAguilera, Mathieu. "« Así se administra a ciegas en España » : statistique, territoire et nation à l’ Estamento de Procuradores (1835)". Parlement[s], Revue d'histoire politique N° HS 12, nr 3 (8.11.2017): 41–62. http://dx.doi.org/10.3917/parl2.hs12.0041.
Pełny tekst źródłaRozprawy doktorskie na temat "Profondeur statistique"
Cisse, Mouhamadou Moustapha. "La fonction de profondeur de Tukey". Master's thesis, Université Laval, 2019. http://hdl.handle.net/20.500.11794/34506.
Pełny tekst źródłaIn this memoir we define the Tukey depth function of a positive finite measure on Rd. Then we study the properties of this function, in particular the properties of continuity and convexity. We seek to establish a characterization of a measure by its depth function. That is, given μ, v finite positive measures on Rd, do we have μ = v if μ and v have the same Tukey depth function? We use the properties of the depth function to establish such a characterization when the measure satisfies certain geometric properties. Then we exhibit some approaches for computing the Tukey depth function. Finally we prove the theorem of characterisation of a discrete measure by its Tukey depth function.
Genest, Maxime. "Mesures de localisation et de dispersion et profondeur de Tukey en statistique directionnelle". Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27669/27669.pdf.
Pełny tekst źródłaResmerita, 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
Moukari, 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
Narasimha, Ramya. "Méthodes dʼestimation de la profondeur par mise en correspondance stéréoscopique à lʼaide de champs aléatoires couplés". Grenoble, 2010. http://www.theses.fr/2010GRENM053.
Pełny tekst źródłaThe depth of objects in 3-D scene can be recovered from a stereo image-pair by finding correspondences between the two views. This stereo matching task involves identifying the corresponding points in the left and the right images, which are the projections of the same scene point. The difference between the locations of the two corresponding points is the disparity, which is inversely related to the 3-D depth. In this thesis, we focus on Bayesian techniques that constrain the disparity estimates. In particular, these constraints involve explicit smoothness assumptions. However, there are further constraints that should be included, for example, the disparities should not be smoothed across object boundaries, the disparities should be consistent with geometric properties of the surface, and regions with similar colour should have similar disparities. The goal of this thesis is to incorporate such constraints using monocular cues and differential geometric information about the surface. To this end, this thesis considers two important problems associated with stereo matching; the first is localizing disparity discontinuities and second aims at recovering binocular disparities in accordance with the surface properties of the scene under consideration. We present a possible solution for each these problems. In order to deal with disparity discontinuities, we propose to cooperatively estimating disparities and object boundaries. This is motivated by the fact that the disparity discontinuities occur near object boundaries. The second one deals with recovering surface consistent disparities and surface normals by estimating the two simultaneously
Narasimha, Ramya. "Méthodes dʼestimation de la profondeur par mise en correspondance stéréoscopique à lʼaide de champs aléatoires couplés". Phd thesis, Grenoble, 2010. http://www.theses.fr/2010GRENM056.
Pełny tekst źródłaThe depth of objects in 3-D scene can be recovered from a stereo image-pair by finding correspondences between the two views. This stereo matching task involves identifying the corresponding points in the left and the right images, which are the projections of the same scene point. The difference between the locations of the two corresponding points is the disparity, which is inversely related to the 3-D depth. In this thesis, we focus on Bayesian techniques that constrain the disparity estimates. In particular, these constraints involve explicit smoothness assumptions. However, there are further constraints that should be included, for example, the disparities should not be smoothed across object boundaries, the disparities should be consistent with geometric properties of the surface, and regions with similar colour should have similar disparities. The goal of this thesis is to incorporate such constraints using monocular cues and differential geometric information about the surface. To this end, this thesis considers two important problems associated with stereo matching; the first is localizing disparity discontinuities and second aims at recovering binocular disparities in accordance with the surface properties of the scene under consideration. We present a possible solution for each these problems. In order to deal with disparity discontinuities, we propose to cooperatively estimating disparities and object boundaries. This is motivated by the fact that the disparity discontinuities occur near object boundaries. The second one deals with recovering surface consistent disparities and surface normals by estimating the two simultaneously
Cénac, Peggy. "Étude statistique de séquences biologiques et convergence de martingales". Toulouse 3, 2006. http://www.theses.fr/2006TOU30065.
Pełny tekst źródłaThe Chaos Game Representation is a dynamical system which maps a sequence of letters taken from a finite alphabet onto an empirical measure on a set. We show how the CGR can be used to characterize the order of an homogeneous Markov chain and to define a new family of tests. Then we propose a construction of Digital Search Trees, inspired from the CGR, by successively inserting all the returned prefixes of a Markov chain. We give the asymptotic behavior of the critical lengths of paths, which turns out to be, at first order, the same one as in the case of DST built from independent Markov chains. A last part deals with properties of almost sure convergence of vectorial martingales. Under suitable regularity conditions on the growing process, we establish the convergence of normalized moments of all orders in the almost sure central limit theorem. The results are applied to the cumulated errors of estimation and prediction in linear regression models and branching processes
Staerman, Guillaume. "Functional anomaly detection and robust estimation". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT021.
Pełny tekst źródłaEnthusiasm for Machine Learning is spreading to nearly all fields such as transportation, energy, medicine, banking or insurance as the ubiquity of sensors through IoT makes more and more data at disposal with an ever finer granularity. The abundance of new applications for monitoring of complex infrastructures (e.g. aircrafts, energy networks) together with the availability of massive data samples has put pressure on the scientific community to develop new reliable Machine-Learning methods and algorithms. The work presented in this thesis focuses around two axes: unsupervised functional anomaly detection and robust learning, both from practical and theoretical perspectives.The first part of this dissertation is dedicated to the development of efficient functional anomaly detection approaches. More precisely, we introduce Functional Isolation Forest (FIF), an algorithm based on randomly splitting the functional space in a flexible manner in order to progressively isolate specific function types. Also, we propose the novel notion of functional depth based on the area of the convex hull of sampled curves, capturing gradual departures from centrality, even beyond the envelope of the data, in a natural fashion. Estimation and computational issues are addressed and various numerical experiments provide empirical evidence of the relevance of the approaches proposed. In order to provide recommendation guidance for practitioners, the performance of recent functional anomaly detection techniques is evaluated using two real-world data sets related to the monitoring of helicopters in flight and to the spectrometry of construction materials.The second part describes the design and analysis of several robust statistical approaches relying on robust mean estimation and statistical data depth. The Wasserstein distance is a popular metric between probability distributions based on optimal transport. Although the latter has shown promising results in many Machine Learning applications, it suffers from a high sensitivity to outliers. To that end, we investigate how to leverage Medians-of-Means (MoM) estimators to robustify the estimation of Wasserstein distance with provable guarantees. Thereafter, a new statistical depth function, the Affine-Invariant Integrated Rank-Weighted (AI-IRW) depth is introduced. Beyond the theoretical analysis carried out, numerical results are presented, providing strong empirical confirmation of the relevance of the depth function proposed. The upper-level sets of statistical depths—the depth-trimmed regions—give rise to a definition of multivariate quantiles. We propose a new discrepancy measure between probability distributions that relies on the average of the Hausdorff distance between the depth-based quantile regions w.r.t. each distribution and demonstrate that it benefits from attractive properties of data depths such as robustness or interpretability. All algorithms developed in this thesis are open-sourced and available online
Jarrah, Adil. "Développement de méthodes statistiques et probabilistes en corrosion par piqûres pour l’estimation de la profondeur maximale : application à l’aluminium A5". Paris, ENSAM, 2009. http://www.theses.fr/2009ENAM0024.
Pełny tekst źródłaPitting corrosion is one of the most prevalent forms of corrosion. It affects all materials and takes place in a very important economic context. Pits can manifest locally over the structure and leads to its deterioration particularly in the presence of mechanical solicitations. The stochastic aspect of the phenomenon led to the development of statistical methods in order to characterize it. This characterization is often done through the estimation of maximum pit depth in the aim to assess the risk of perforation. For this aim, the method of Gumbel is often used. The objective of this work is to check the conditions of application of this method notably the independence and compare it with the approaches based on law of generalized extreme values and the peak over threshold. The condition of independence is verified using spatial process. An adaptation of the spectral analysis in the context of pitting corrosion is also proposed. The comparison between the approaches is based on numerical simulations which the parameters come from the experimentation
Van, Der Baan Mirko. "Deux méthodes d'interférence statistique appliquées aux données de sismique réflexion profonde : détection de signaux et localisation d'onde". Grenoble 1, 1999. https://tel.archives-ouvertes.fr/tel-00745500.
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