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Статті в журналах з теми "Apprentissage de représentations (intelligence artificielle)":
EZANNO, Pauline, Sébastien PICAULT, Nathalie WINTER, Gaël BEAUNÉE, Hervé MONOD, and Jean-François GUÉGAN. "Intelligence artificielle et santé animale." INRAE Productions Animales 33, no. 2 (September 15, 2020): 95–108. http://dx.doi.org/10.20870/productions-animales.2020.33.2.3572.
Caccamo, Emmanuelle, and Fabien Richert. "Les procédés algorithmiques au prisme des approches sémiotiques." Cygne noir, no. 7 (June 1, 2022): 1–16. http://dx.doi.org/10.7202/1089327ar.
Recanati, Catherine. "La démarche originale de Jean Nicod et son intérêt en sémantique et en sciences cognitives." Intellectica. Revue de l'Association pour la Recherche Cognitive 75, no. 2 (2021): 257–84. http://dx.doi.org/10.3406/intel.2021.2009.
Boyadjian, Julien, and Stéphanie Wojcik. "Militer dans et aux lisières des organisations partisanes." Politiques de communication N° 19, no. 2 (September 21, 2023): 153–86. http://dx.doi.org/10.3917/pdc.019.0153.
FOUQUERE, Christophe. "Représentations graphiques et intelligence artificielle." Didaskalia, no. 5 (1994). http://dx.doi.org/10.4267/2042/23237.
Lebrun, Tom. "Pour une typologie des œuvres littéraires générées par intelligence artificielle." Balisages, no. 1 (February 24, 2020). http://dx.doi.org/10.35562/balisages.304.
Gobert, Thierry. "Qualifier les interactions observables en situation d’interactivité." De l’interactivité aux interaction(s) médiatrice(s) 1, no. 1 (December 12, 2017). http://dx.doi.org/10.25965/interfaces-numeriques.130.
Дисертації з теми "Apprentissage de représentations (intelligence artificielle)":
Risser-Maroix, Olivier. "Similarité visuelle et apprentissage de représentations." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7327.
The objective of this CIFRE thesis is to develop an image search engine, based on computer vision, to assist customs officers. Indeed, we observe, paradoxically, an increase in security threats (terrorism, trafficking, etc.) coupled with a decrease in the number of customs officers. The images of cargoes acquired by X-ray scanners already allow the inspection of a load without requiring the opening and complete search of a controlled load. By automatically proposing similar images, such a search engine would help the customs officer in his decision making when faced with infrequent or suspicious visual signatures of products. Thanks to the development of modern artificial intelligence (AI) techniques, our era is undergoing great changes: AI is transforming all sectors of the economy. Some see this advent of "robotization" as the dehumanization of the workforce, or even its replacement. However, reducing the use of AI to the simple search for productivity gains would be reductive. In reality, AI could allow to increase the work capacity of humans and not to compete with them in order to replace them. It is in this context, the birth of Augmented Intelligence, that this thesis takes place. This manuscript devoted to the question of visual similarity is divided into two parts. Two practical cases where the collaboration between Man and AI is beneficial are proposed. In the first part, the problem of learning representations for the retrieval of similar images is still under investigation. After implementing a first system similar to those proposed by the state of the art, one of the main limitations is pointed out: the semantic bias. Indeed, the main contemporary methods use image datasets coupled with semantic labels only. The literature considers that two images are similar if they share the same label. This vision of the notion of similarity, however fundamental in AI, is reductive. It will therefore be questioned in the light of work in cognitive psychology in order to propose an improvement: the taking into account of visual similarity. This new definition allows a better synergy between the customs officer and the machine. This work is the subject of scientific publications and a patent. In the second part, after having identified the key components allowing to improve the performances of thepreviously proposed system, an approach mixing empirical and theoretical research is proposed. This secondcase, augmented intelligence, is inspired by recent developments in mathematics and physics. First applied tothe understanding of an important hyperparameter (temperature), then to a larger task (classification), theproposed method provides an intuition on the importance and role of factors correlated to the studied variable(e.g. hyperparameter, score, etc.). The processing chain thus set up has demonstrated its efficiency byproviding a highly explainable solution in line with decades of research in machine learning. These findings willallow the improvement of previously developed solutions
Tamaazousti, Youssef. "Vers l’universalité des représentations visuelle et multimodales." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC038/document.
Because of its key societal, economic and cultural stakes, Artificial Intelligence (AI) is a hot topic. One of its main goal, is to develop systems that facilitates the daily life of humans, with applications such as household robots, industrial robots, autonomous vehicle and much more. The rise of AI is highly due to the emergence of tools based on deep neural-networks which make it possible to simultaneously learn, the representation of the data (which were traditionally hand-crafted), and the task to solve (traditionally learned with statistical models). This resulted from the conjunction of theoretical advances, the growing computational capacity as well as the availability of many annotated data. A long standing goal of AI is to design machines inspired humans, capable of perceiving the world, interacting with humans, in an evolutionary way. We categorize, in this Thesis, the works around AI, in the two following learning-approaches: (i) Specialization: learn representations from few specific tasks with the goal to be able to carry out very specific tasks (specialized in a certain field) with a very good level of performance; (ii) Universality: learn representations from several general tasks with the goal to perform as many tasks as possible in different contexts. While specialization was extensively explored by the deep-learning community, only a few implicit attempts were made towards universality. Thus, the goal of this Thesis is to explicitly address the problem of improving universality with deep-learning methods, for image and text data. We have addressed this topic of universality in two different forms: through the implementation of methods to improve universality (“universalizing methods”); and through the establishment of a protocol to quantify its universality. Concerning universalizing methods, we proposed three technical contributions: (i) in a context of large semantic representations, we proposed a method to reduce redundancy between the detectors through, an adaptive thresholding and the relations between concepts; (ii) in the context of neural-network representations, we proposed an approach that increases the number of detectors without increasing the amount of annotated data; (iii) in a context of multimodal representations, we proposed a method to preserve the semantics of unimodal representations in multimodal ones. Regarding the quantification of universality, we proposed to evaluate universalizing methods in a Transferlearning scheme. Indeed, this technical scheme is relevant to assess the universal ability of representations. This also led us to propose a new framework as well as new quantitative evaluation criteria for universalizing methods
Franceschi, Jean-Yves. "Apprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS014.
The recent rise of deep learning has been motivated by numerous scientific breakthroughs, particularly regarding representation learning and generative modeling. However, most of these achievements have been obtained on image or text data, whose evolution through time remains challenging for existing methods. Given their importance for autonomous systems to adapt in a constantly evolving environment, these challenges have been actively investigated in a growing body of work. In this thesis, we follow this line of work and study several aspects of temporality and dynamical systems in deep unsupervised representation learning and generative modeling. Firstly, we present a general-purpose deep unsupervised representation learning method for time series tackling scalability and adaptivity issues arising in practical applications. We then further study in a second part representation learning for sequences by focusing on structured and stochastic spatiotemporal data: videos and physical phenomena. We show in this context that performant temporal generative prediction models help to uncover meaningful and disentangled representations, and conversely. We highlight to this end the crucial role of differential equations in the modeling and embedding of these natural sequences within sequential generative models. Finally, we more broadly analyze in a third part a popular class of generative models, generative adversarial networks, under the scope of dynamical systems. We study the evolution of the involved neural networks with respect to their training time by describing it with a differential equation, allowing us to gain a novel understanding of this generative model
Bourigault, Simon. "Apprentissage de représentations pour la prédiction de propagation d'information dans les réseaux sociaux." Electronic Thesis or Diss., Paris 6, 2016. http://www.theses.fr/2016PA066368.
In this thesis, we study information diffusion in online social networks. Websites like Facebook or Twitter have indeed become information medias, on which users create and share a lot of data. Most existing models of the information diffusion phenomenon relies on strong hypothesis about the structure and dynamics of diffusion. In this document, we study the problem of diffusion prediction in the context where the social graph is unknown and only user actions are observed. - We propose a learning algorithm for the independant cascades model that does not take time into account. Experimental results show that this approach obtains better results than time-based learning schemes. - We then propose several representations learning methods for this task of diffusion prediction. This let us define more compact and faster models. - Finally, we apply our representation learning approach to the source detection task, where it obtains much better results than graph-based approaches
Bourigault, Simon. "Apprentissage de représentations pour la prédiction de propagation d'information dans les réseaux sociaux." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066368/document.
In this thesis, we study information diffusion in online social networks. Websites like Facebook or Twitter have indeed become information medias, on which users create and share a lot of data. Most existing models of the information diffusion phenomenon relies on strong hypothesis about the structure and dynamics of diffusion. In this document, we study the problem of diffusion prediction in the context where the social graph is unknown and only user actions are observed. - We propose a learning algorithm for the independant cascades model that does not take time into account. Experimental results show that this approach obtains better results than time-based learning schemes. - We then propose several representations learning methods for this task of diffusion prediction. This let us define more compact and faster models. - Finally, we apply our representation learning approach to the source detection task, where it obtains much better results than graph-based approaches
Ferré, Arnaud. "Représentations vectorielles et apprentissage automatique pour l’alignement d’entités textuelles et de concepts d’ontologie : application à la biologie." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS117/document.
The impressive increase in the quantity of textual data makes it difficult today to analyze them without the assistance of tools. However, a text written in natural language is unstructured data, i.e. it cannot be interpreted by a specialized computer program, without which the information in the texts remains largely under-exploited. Among the tools for automatic extraction of information from text, we are interested in automatic text interpretation methods for the entity normalization task that consists in automatically matching text entitiy mentions to concepts in a reference terminology. To accomplish this task, we propose a new approach by aligning two types of vector representations of entities that capture part of their meanings: word embeddings for text mentions and concept embeddings for concepts, designed specifically for this work. The alignment between the two is done through supervised learning. The developed methods have been evaluated on a reference dataset from the biological domain and they now represent the state of the art for this dataset. These methods are integrated into a natural language processing software suite and the codes are freely shared
Poussevin, Mickael. "Apprentissage de représentation pour des données générées par des utilisateurs." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066040/document.
In this thesis, we study how representation learning methods can be applied to user-generated data. Our contributions cover three different applications but share a common denominator: the extraction of relevant user representations. Our first application is the item recommendation task, where recommender systems build user and item profiles out of past ratings reflecting user preferences and item characteristics. Nowadays, textual information is often together with ratings available and we propose to use it to enrich the profiles extracted from the ratings. Our hope is to extract from the textual content shared opinions and preferences. The models we propose provide another opportunity: predicting the text a user would write on an item. Our second application is sentiment analysis and, in particular, polarity classification. Our idea is that recommender systems can be used for such a task. Recommender systems and traditional polarity classifiers operate on different time scales. We propose two hybridizations of these models: the former has better classification performance, the latter highlights a vocabulary of surprise in the texts of the reviews. The third and final application we consider is urban mobility. It takes place beyond the frontiers of the Internet, in the physical world. Using authentication logs of the subway users, logging the time and station at which users take the subway, we show that it is possible to extract robust temporal profiles
Francis, Danny. "Représentations sémantiques d'images et de vidéos." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS605.
Recent research in Deep Learning has sent the quality of results in multimedia tasks rocketing: thanks to new big datasets of annotated images and videos, Deep Neural Networks (DNN) have outperformed other models in most cases. In this thesis, we aim at developing DNN models for automatically deriving semantic representations of images and videos. In particular we focus on two main tasks : vision-text matching and image/video automatic captioning. Addressing the matching task can be done by comparing visual objects and texts in a visual space, a textual space or a multimodal space. Based on recent works on capsule networks, we define two novel models to address the vision-text matching problem: Recurrent Capsule Networks and Gated Recurrent Capsules. In image and video captioning, we have to tackle a challenging task where a visual object has to be analyzed, and translated into a textual description in natural language. For that purpose, we propose two novel curriculum learning methods. Moreover regarding video captioning, analyzing videos requires not only to parse still images, but also to draw correspondences through time. We propose a novel Learned Spatio-Temporal Adaptive Pooling method for video captioning that combines spatial and temporal analysis. Extensive experiments on standard datasets assess the interest of our models and methods with respect to existing works
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.
The 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
Poussevin, Mickael. "Apprentissage de représentation pour des données générées par des utilisateurs." Electronic Thesis or Diss., Paris 6, 2015. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2015PA066040.pdf.
In this thesis, we study how representation learning methods can be applied to user-generated data. Our contributions cover three different applications but share a common denominator: the extraction of relevant user representations. Our first application is the item recommendation task, where recommender systems build user and item profiles out of past ratings reflecting user preferences and item characteristics. Nowadays, textual information is often together with ratings available and we propose to use it to enrich the profiles extracted from the ratings. Our hope is to extract from the textual content shared opinions and preferences. The models we propose provide another opportunity: predicting the text a user would write on an item. Our second application is sentiment analysis and, in particular, polarity classification. Our idea is that recommender systems can be used for such a task. Recommender systems and traditional polarity classifiers operate on different time scales. We propose two hybridizations of these models: the former has better classification performance, the latter highlights a vocabulary of surprise in the texts of the reviews. The third and final application we consider is urban mobility. It takes place beyond the frontiers of the Internet, in the physical world. Using authentication logs of the subway users, logging the time and station at which users take the subway, we show that it is possible to extract robust temporal profiles
Книги з теми "Apprentissage de représentations (intelligence artificielle)":
Programme de recherche coordonnée-groupe de recherche coordonnée. Journées nationales. Intelligence artificielle: Actes des. [Lieu de publication non identifié]: Teknea, 1988.
Kodratoff, Yves. Apprentissage symbolique: Une approche de l'intelligence artificielle. Toulouse (France): Cépaduès-éditions, 1993.
Agnès, Guillot, and Daucé Emmanuel, eds. Approche dynamique de la cognition artificielle. Paris: Hermès science publications, 2002.
Alliot, Jean-Marc. Intelligence artificielle et informatique théorique. Toulouse: Cépaduès-éd., 1994.
Fischler, Martin A. Intelligence: The eye, the brain, and the computer. Reading, Mass: Addison-Wesley, 1987.
Nigel, Ford. How machines think: A general introduction to artificial intelligence ; illustrated in prolog. Chichester [West Sussex]: Wiley, 1987.
N, Kanal Laveen, Levitt Tod S, and Lemmer John F, eds. Uncertainty in artificial intelligence 3. Amsterdam: North-Holland, 1989.
1951-, Mitchell Tom M., Carbonell Jaime G, and Michalski Ryszard S. 1937-, eds. Machine learning: A guideto current research. Boston, (Mass.): Kluwer Academic, 1986.
1951-, Mitchell Tom M., Carbonell Jaime G, and Michalski Ryszard Stanislaw 1937-, eds. Machine learning: A guide to current research. Boston: Kluwer Academic Publishers, 1986.
Pratt, Vernon. Thinking machines: The evolution of artificial intelligence. Oxford, UK: B. Blackwell, 1987.
Частини книг з теми "Apprentissage de représentations (intelligence artificielle)":
Bastien, Claude. "Apprentissage : modèles et représentation." In Intelligence naturelle, intelligence artificielle, 257–68. Presses Universitaires de France, 1993. http://dx.doi.org/10.3917/puf.lenyj.1993.01.0257.
Ganascia, Jean-Gabriel. "Approches du morcelage en apprentissage symbolique." In Intelligence naturelle, intelligence artificielle, 207–30. Presses Universitaires de France, 1993. http://dx.doi.org/10.3917/puf.lenyj.1993.01.0207.
"Mégadonnées, analyse de données, intelligence artificielle et apprentissage automatique." In Le rôle des technologies avancées dans le commerce transfrontières, 36–45. WTO, 2022. http://dx.doi.org/10.30875/9789287073624c005.
Cárdenas, Janina Di Pierro, and Renata De Rugeriis Juárez. "Inteligencia artificial y SoftPower de la traducción asistida y automática: perspectivas en el proceso de enseñanza-aprendizaje de idiomas." In Traduction automatique et usages sociaux des langues. Quelle conséquences pour la diversité linguistique ?, 83–99. Observatoire européen du plurilinguisme, 2021. http://dx.doi.org/10.3917/oep.beacc.2021.01.0083.