Academic literature on the topic 'Modèle probabiliste génératifs'
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Journal articles on the topic "Modèle probabiliste génératifs"
Li, Nan. "Using the probabilistic fertility table to test the statistical significance of fertility trends." Canadian Studies in Population 43, no. 3-4 (December 20, 2016): 203. http://dx.doi.org/10.25336/p6fp4f.
Full textArnaud, P., J. Lavabre, and J. M. Masson. "Amélioration des performances d'un modèle stochastique de génération de hyétogrammes horaires: application au pourtour méditerranéen français." Revue des sciences de l'eau 12, no. 2 (April 12, 2005): 251–71. http://dx.doi.org/10.7202/705351ar.
Full textDissertations / Theses on the topic "Modèle probabiliste génératifs"
Azeraf, Elie. "Classification avec des modèles probabilistes génératifs et des réseaux de neurones. Applications au traitement des langues naturelles." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. https://theses.hal.science/tel-03880848.
Full textMany probabilistic models have been neglected for classification tasks with supervised learning for several years, as the Naive Bayes or the Hidden Markov Chain. These models, called generative, are criticized because the induced classifier must learn the observations' law. This problem is too complex when the number of observations' features is too large. It is especially the case with Natural Language Processing tasks, as the recent embedding algorithms convert words in large numerical vectors to achieve better scores.This thesis shows that every generative model can define its induced classifier without using the observations' law. This proposition questions the usual categorization of the probabilistic models and classifiers and allows many new applications. Therefore, Hidden Markov Chain can be efficiently applied to Chunking and Naive Bayes to sentiment analysis.We go further, as this proposition allows to define the classifier induced from a generative model with neural network functions. We "neuralize" the models mentioned above and many of their extensions. Models so obtained allow to achieve relevant scores for many Natural Language Processing tasks while being interpretable, able to require little training data, and easy to serve
Ferdjoukh, Adel. "Une approche déclarative pour la génération de modèles." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT325/document.
Full textOwning data is useful in many different fields. Data can be used to test and to validate approaches, algorithms and concepts. Unfortunately, data is rarely available, is cost to obtain, or is not adapted to most of cases due to a lack of quality.An automated data generator is a good way to generate quickly and easily data that are valid, in different sizes, likelihood and diverse.In this thesis, we propose a novel and complete model driven approach, based on constraint programming for automated data generation
De, Félice Sven. "Automates codéterministes et automates acycliques : analyse d'algorithmes et génération aléatoire." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1111/document.
Full textThe general context of this thesis is the quantitative analysis of objects coming from rational language theory. We adapt techniques from the field of analysis of algorithms (average-case complexity, generic complexity, random generation...) to objects and algorithms that involve particular classes of automata. In a first part we study the complexity of Brzozowski's minimisation algorithm. Although the worst-case complexity of this algorithm is bad, it is known to be efficient in practice. Using typical properties of random mappings and random permutations, we show that the generic complexityof Brzozowski's algorithm grows faster than any polynomial in n, where n is the number of states of the automaton. In a second part, we study the random generation of acyclic automata. These automata recognize the finite sets of words, and for this reason they are widely use in applications, especially in natural language processing. We present two random generators, one using a model of Markov chain, the other a ``recursive method", based on a cominatorics decomposition of structures. The first method can be applied in many situations cases but is very difficult to calibrate, the second method is more efficient. Once implemented, this second method allows to observe typical properties of acyclic automata of large size
Cordier, Nicolas. "Approches multi-atlas fondées sur l'appariement de blocs de voxels pour la segmentation et la synthèse d'images par résonance magnétique de tumeurs cérébrales." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4111/document.
Full textThis thesis focuses on the development of automatic methods for the segmentation and synthesis of brain tumor Magnetic Resonance images. The main clinical perspective of glioma segmentation is growth velocity monitoring for patient therapy management. To this end, the thesis builds on the formalization of multi-atlas patch-based segmentation with probabilistic graphical models. A probabilistic model first extends classical multi-atlas approaches used for the segmentation of healthy brains structures to the automatic segmentation of pathological cerebral regions. An approximation of the marginalization step replaces the concept of local search windows with a stratification with respect to both atlases and labels. A glioma detection model based on a spatially-varying prior and patch pre-selection criteria are introduced to obtain competitive running times despite patch matching being non local. This work is validated and compared to state-of-the-art algorithms on publicly available datasets. A second probabilistic model mirrors the segmentation model in order to synthesize realistic MRI of pathological cases, based on a single label map. A heuristic method allows to solve for the maximum a posteriori and to estimate uncertainty of the image synthesis model. Iterating patch matching reinforces the spatial coherence of synthetic images. The realism of our synthetic images is assessed against real MRI, and against outputs of the state-of-the-art method. The junction of a tumor growth model to the proposed synthesis approach allows to generate databases of annotated synthetic cases
Mihoub, Alaeddine. "Apprentissage statistique de modèles de comportement multimodal pour les agents conversationnels interactifs." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT079/document.
Full textFace to face interaction is one of the most fundamental forms of human communication. It is a complex multimodal and coupled dynamic system involving not only speech but of numerous segments of the body among which gaze, the orientation of the head, the chest and the body, the facial and brachiomanual movements, etc. The understanding and the modeling of this type of communication is a crucial stage for designing interactive agents capable of committing (hiring) credible conversations with human partners. Concretely, a model of multimodal behavior for interactive social agents faces with the complex task of generating gestural scores given an analysis of the scene and an incremental estimation of the joint objectives aimed during the conversation. The objective of this thesis is to develop models of multimodal behavior that allow artificial agents to engage into a relevant co-verbal communication with a human partner. While the immense majority of the works in the field of human-agent interaction (HAI) is scripted using ruled-based models, our approach relies on the training of statistical models from tracks collected during exemplary interactions, demonstrated by human trainers. In this context, we introduce "sensorimotor" models of behavior, which perform at the same time the recognition of joint cognitive states and the generation of the social signals in an incremental way. In particular, the proposed models of behavior have to estimate the current unit of interaction ( IU) in which the interlocutors are jointly committed and to predict the co-verbal behavior of its human trainer given the behavior of the interlocutor(s). The proposed models are all graphical models, i.e. Hidden Markov Models (HMM) and Dynamic Bayesian Networks (DBN). The models were trained and evaluated - in particular compared with classic classifiers - using datasets collected during two different interactions. Both interactions were carefully designed so as to collect, in a minimum amount of time, a sufficient number of exemplars of mutual attention and multimodal deixis of objects and places. Our contributions are completed by original methods for the interpretation and comparative evaluation of the properties of the proposed models. By comparing the output of the models with the original scores, we show that the HMM, thanks to its properties of sequential modeling, outperforms the simple classifiers in term of performances. The semi-Markovian models (HSMM) further improves the estimation of sensorimotor states thanks to duration modeling. Finally, thanks to a rich structure of dependency between variables learnt from the data, the DBN has the most convincing performances and demonstrates both the best performance and the most faithful multimodal coordination to the original multimodal events
Villéger, Emmanuel. "Constance de largeur et désocclusion dans les images digitales." Phd thesis, Université de Nice Sophia-Antipolis, 2005. http://tel.archives-ouvertes.fr/tel-00011229.
Full textnous regroupons des points lumineux et/ou des objets selon certaines
règles pour former des objets plus gros, des Gestalts.
La première partie de cette thèse est consacrée à la constance de
largeur. La Gestalt constance de largeur regroupe des points situés
entre deux bords qui restent parallèles. Nous cherchons donc dans les
images des courbes ``parallèles.'' Nous voulons faire une détection
a contrario, nous proposons donc une quantification du ``non
parallélisme'' de deux courbes par trois méthodes. La première méthode
utilise un modèle de génération de courbes régulières et nous
calculons une probabilité. La deuxième méthode est une méthode de
simulation de type Monte-Carlo pour estimer cette probabilité. Enfin
la troisième méthode correspond à un développement limité de la
première en faisant tendre un paramètre vers 0 sous certaines
contraintes. Ceci conduit à une équation aux dérivées partielles
(EDP). Parmi ces trois méthodes la méthode de type Monte-Carlo est
plus robuste et plus rapide.
L'EDP obtenue est très similaire à celles utilisées pour la
désocclusion d'images. C'est pourquoi dans la deuxième partie de cette
thèse nous nous intéressons au problème de la désocclusion. Nous
présentons les méthodes existantes puis une nouvelle méthode basée sur
un système de deux EDPs dont l'une est inspirée de celle de la
première partie. Nous introduisons la probabilité de l'orientation du
gradient de l'image. Nous prenons ainsi en compte l'incertitude sur
l'orientation calculée du gradient de l'image. Cette incertitude est
quantifiée en relation avec la norme du gradient.
Avec la quantification du non parallélisme de deux courbes, l'étape
suivante est la détection de la constance de largeur dans
les images. Il faut alors définir un seuil pour sélectionner les
bonnes réponses du détecteur et surtout parmi les réponses définir
des réponses ``maximales.'' Le système d'EDPs pour
la désocclusion dépend de beaucoup de paramètres, il faut trouver une
méthode de calibration des paramètres pour obtenir de bons résultats
adaptés à chaque image.
Almahairi, Amjad. "Advances in deep learning with limited supervision and computational resources." Thèse, 2018. http://hdl.handle.net/1866/23434.
Full textDeep neural networks are the cornerstone of state-of-the-art systems for a wide range of tasks, including object recognition, language modelling and machine translation. In the last decade, research in the field of deep learning has led to numerous key advances in designing novel architectures and training algorithms for neural networks. However, most success stories in deep learning heavily relied on two main factors: the availability of large amounts of labelled data and massive computational resources. This thesis by articles makes several contributions to advancing deep learning, specifically in problems with limited or no labelled data, or with constrained computational resources. The first article addresses sparsity of labelled data that emerges in the application field of recommender systems. We propose a multi-task learning framework that leverages natural language reviews in improving recommendation. Specifically, we apply neural-network-based methods for learning representations of products from review text, while learning from rating data. We demonstrate that the proposed method can achieve state-of-the-art performance on the Amazon Reviews dataset. The second article tackles computational challenges in training large-scale deep neural networks. We propose a conditional computation network architecture which can adaptively assign its capacity, and hence computations, across different regions of the input. We demonstrate the effectiveness of our model on visual recognition tasks where objects are spatially localized within the input, while maintaining much lower computational overhead than standard network architectures. The third article contributes to the domain of unsupervised learning with the generative adversarial networks paradigm. We introduce a flexible adversarial training framework, in which not only the generator converges to the true data distribution, but also the discriminator recovers the relative density of the data at the optimum. We validate our framework empirically by showing that the discriminator is able to accurately estimate the true energy of data while obtaining state-of-the-art quality of samples. Finally, in the fourth article, we address the problem of unsupervised domain translation. We propose a model which can learn flexible, many-to-many mappings across domains from unpaired data. We validate our approach on several image datasets, and we show that it can be effectively applied in semi-supervised learning settings.
Dinh, Laurent. "Reparametrization in deep learning." Thèse, 2018. http://hdl.handle.net/1866/21139.
Full textTan, Shawn. "Latent variable language models." Thèse, 2018. http://hdl.handle.net/1866/22131.
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