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Статті в журналах з теми "Pratique computationnelle":
Dondero, Maria Giulia. "Les forces dans l’image et les gestualités émotionnelles." SHS Web of Conferences 81 (2020): 03005. http://dx.doi.org/10.1051/shsconf/20208103005.
Gabay, Simon, Rachel Bawden, Philippe Gambette, Jonathan Poinhos, Eleni Kogkitsidou, and Benoît Sagot. "Le changement linguistique au XVIIe s. : nouvelles approches scriptométriques." SHS Web of Conferences 138 (2022): 02006. http://dx.doi.org/10.1051/shsconf/202213802006.
Meunier, Jean-Guy. "Enjeux de la modélisation formelle en sémiotique computationnelle." Cygne noir, no. 7 (June 1, 2022): 42–78. http://dx.doi.org/10.7202/1089329ar.
LAMOUREUX, Samuel, and Jean-Hugues ROY. "De la capture à l’asservissement : Comment la machine Meta remodèle les pratiques des journalistes francophones." Varia Vol. 17 | N° 1-2 | 1er semestre (2023): 195–229. http://dx.doi.org/10.4000/11r9d.
Pulizzotto, Davide. "L’analyse de texte assistée par ordinateur : introduction à l’un des champs fondamentaux de la sémiotique computationnelle." Cygne noir, no. 7 (June 1, 2022): 17–41. http://dx.doi.org/10.7202/1089328ar.
Beaude, Boris. "(re)Médiations numériques et perturbations des sciences sociales contemporaines." Sociologie et sociétés 49, no. 2 (December 4, 2018): 83–111. http://dx.doi.org/10.7202/1054275ar.
Gardin, Jean-Claude. "Archéologie, formalisation et sciences sociales." Sociologie et sociétés 31, no. 1 (October 2, 2002): 119–27. http://dx.doi.org/10.7202/001282ar.
Gauld, Christophe. "Une brève histoire des sciences computationnelles." Médecine et Philosophie, January 31, 2021. http://dx.doi.org/10.51328/mep.100.
Gauld, Christophe. "Une brève histoire des sciences computationnelles." Médecine et Philosophie, January 31, 2021. http://dx.doi.org/10.51328/100.
Дисертації з теми "Pratique computationnelle":
Kaninda, Tshitwala Lynda. "Analyse des pratiques computationnelles anormes des enseignants du primaire en République Démocratique du Congo : réflexions pour une théorie des pratiques retournées." Electronic Thesis or Diss., Bordeaux 3, 2023. http://www.theses.fr/2023BOR30039.
This research work examines the computational practices of Congolese primary school teachers who have been introduced to computer tools and services as part of the Francophone initiative for distance teacher training (IFADEM, in French). It proposes a discourse that distances itself from any a priori conformity or non-conformity to the norms of use prescribed by the body of trainers in information and communication technologies for education (ICTE). The aim of this doctoral project is to understand the "reversed" mechanisms - as singular as they are individual, as novel as they are unstable - by which each practitioner reinvents the use of ICTE (abnormal computational usage practices). What are the representational, contextual, pedagogical and transliterative factors by which some of these users act as practitioner-refusers? To answer this question, we carried out a microsociological analysis. Our methodology is based on a research-action-training approach, using both qualitative (interviews and screen tracking) and quantitative (statistical processing of questionnaires) techniques. Contrary to our initial hypotheses, the conclusions we reached are rather surprising
Delalleau, Olivier. "Apprentissage machine efficace : théorie et pratique." Thèse, 2012. http://hdl.handle.net/1866/8669.
Despite constant progress in terms of available computational power, memory and amount of data, machine learning algorithms need to be efficient in how they use them. Although minimizing cost is an obvious major concern, another motivation is to attempt to design algorithms that can learn as efficiently as intelligent species. This thesis tackles the problem of efficient learning through various papers dealing with a wide range of machine learning algorithms: this topic is seen both from the point of view of computational efficiency (processing power and memory required by the algorithms) and of statistical efficiency (n umber of samples necessary to solve a given learning task).The first contribution of this thesis is in shedding light on various statistical inefficiencies in existing algorithms. Indeed, we show that decision trees do not generalize well on tasks with some particular properties (chapter 3), and that a similar flaw affects typical graph-based semi-supervised learning algorithms (chapter 5). This flaw is a form of curse of dimensionality that is specific to each of these algorithms. For a subclass of neural networks, called sum-product networks, we prove that using networks with a single hidden layer can be exponentially less efficient than when using deep networks (chapter 4). Our analyses help better understand some inherent flaws found in these algorithms, and steer research towards approaches that may potentially overcome them. We also exhibit computational inefficiencies in popular graph-based semi-supervised learning algorithms (chapter 5) as well as in the learning of mixtures of Gaussians with missing data (chapter 6). In both cases we propose new algorithms that make it possible to scale to much larger datasets. The last two chapters also deal with computational efficiency, but in different ways. Chapter 7 presents a new view on the contrastive divergence algorithm (which has been used for efficient training of restricted Boltzmann machines). It provides additional insight on the reasons why this algorithm has been so successful. Finally, in chapter 8 we describe an application of machine learning to video games, where computational efficiency is tied to software and hardware engineering constraints which, although often ignored in research papers, are ubiquitous in practice.