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Dissertations / Theses on the topic 'Probabilistic deep models'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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1

Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can b
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2

Zhai, Menghua. "Deep Probabilistic Models for Camera Geo-Calibration." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/74.

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The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly con
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3

Wu, Di. "Human action recognition using deep probabilistic graphical models." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/6603/.

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Building intelligent systems that are capable of representing or extracting high-level representations from high-dimensional sensory data lies at the core of solving many A.I. related tasks. Human action recognition is an important topic in computer vision that lies in high-dimensional space. Its applications include robotics, video surveillance, human-computer interaction, user interface design, and multi-media video retrieval amongst others. A number of approaches have been proposed to extract representative features from high-dimensional temporal data, most commonly hard wired geometric or
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4

Rossi, Simone. "Improving Scalability and Inference in Probabilistic Deep Models." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS042.

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Au cours de la dernière décennie, l'apprentissage profond a atteint un niveau de maturité suffisant pour devenir le choix privilégié pour résoudre les problèmes liés à l'apprentissage automatique ou pour aider les processus de prise de décision.En même temps, l'apprentissage profond n'a généralement pas la capacité de quantifier avec précision l'incertitude de ses prédictions, ce qui rend ces modèles moins adaptés aux applications critiques en matière de risque.Une solution possible pour résoudre ce problème est d'utiliser une formulation bayésienne ; cependant, bien que cette solution soit él
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5

Hager, Paul Andrew. "Investigation of connection between deep learning and probabilistic graphical models." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119552.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 21).<br>The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the
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6

Farouni, Tarek. "An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539.

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7

Morales, quinga Katherine Tania. "Generative Markov models for sequential bayesian classification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS019.

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Cette thèse vise à modéliser des données séquentielles à travers l'utilisation de modèles probabilistes à variables latentes et paramétrés par des architectures de type réseaux de neurones profonds. Notre objectif est de développer des modèles dynamiques capables de capturer des dynamiques temporelles complexes inhérentes aux données séquentielles tout en étant applicables dans des domaines variés tels que la classification, la prédiction et la génération de données pour n'importe quel type de données séquentielles. Notre approche se concentre sur plusieurs problématiques liés à la modélisatio
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8

Qian, Weizhu. "Discovering human mobility from mobile data : probabilistic models and learning algorithms." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA025.

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Les données d'utilisation des smartphones peuvent être utilisées pour étudier la mobilité humaine que ce soit en environnement extérieur ouvert ou à l'intérieur de bâtiments. Dans ce travail, nous étudions ces deux aspects de la mobilité humaine en proposant des algorithmes de machine learning adapté aux sources d'information disponibles dans chacun des contextes.Pour l'étude de la mobilité en environnement extérieur, nous utilisons les données de coordonnées GPS collectées pour découvrir les schémas de mobilité quotidiens des utilisateurs. Pour cela, nous proposons un algorithme de clustering
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9

SYED, MUHAMMAD FARRUKH SHAHID. "Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1048543.

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Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every field of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT surfers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Artificial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attac
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10

El-Shaer, Mennat Allah. "An Experimental Evaluation of Probabilistic Deep Networks for Real-time Traffic Scene Representation using Graphical Processing Units." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546539166677894.

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11

Hu, Xu. "Towards efficient learning of graphical models and neural networks with variational techniques." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1037.

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Dans cette thèse, je me concentrerai principalement sur l’inférence variationnelle et les modèles probabilistes. En particulier, je couvrirai plusieurs projets sur lesquels j'ai travaillé pendant ma thèse sur l'amélioration de l'efficacité des systèmes AI / ML avec des techniques variationnelles. La thèse comprend deux parties. Dans la première partie, l’efficacité des modèles probabilistes graphiques est étudiée. Dans la deuxième partie, plusieurs problèmes d’apprentissage des réseaux de neurones profonds sont examinés, qui sont liés à l’efficacité énergétique ou à l’efficacité des échantillo
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12

Balikas, Georgios. "Explorer et apprendre à partir de collections de textes multilingues à l'aide des modèles probabilistes latents et des réseaux profonds." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM054/document.

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Le texte est l'une des sources d'informations les plus répandues et les plus persistantes. L'analyse de contenu du texte se réfère à des méthodes d'étude et de récupération d'informations à partir de documents. Aujourd'hui, avec une quantité de texte disponible en ligne toujours croissante l'analyse de contenu du texte revêt une grande importance parce qu' elle permet une variété d'applications. À cette fin, les méthodes d'apprentissage de la représentation sans supervision telles que les modèles thématiques et les word embeddings constituent des outils importants.L'objectif de cette dissertat
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13

Prencipe, Michele Pio. "Elaborazione del Linguaggio Naturale con Metodi Probabilistici e Reti Neurali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24312/.

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L'elaborazione del linguaggio naturale (NLP) è il processo per il quale la macchina tenta di imparare le informazioni del parlato o dello scritto tipico dell'essere umano. La procedura è resa particolarmente complessa dalle numerose ambiguità tipiche della lingua o del testo: ironia, metafore, errori ortografici e così via. Grazie all'apprendimento profondo, il Deep Learning, che ha permesso lo sviluppo delle reti neurali, si è raggiunto lo stato dell'arte nell'ambito NLP, tramite l'introduzione di architetture quali Encoder-Decoder, Transformers o meccanismi di attenzione. Le reti neurali
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14

GARBARINO, DAVIDE. "Acknowledging the structured nature of real-world data with graphs embeddings and probabilistic inference methods." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1092453.

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In the artificial intelligence community there is a growing consensus that real world data is naturally represented as graphs because they can easily incorporate complexity at several levels, e.g. hierarchies or time dependencies. In this context, this thesis studies two main branches for structured data. In the first part we explore how state-of-the-art machine learning methods can be extended to graph modeled data provided that one is able to represent graphs in vector spaces. Such extensions can be applied to analyze several kinds of real-world data and tackle different problems. Here we
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15

Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

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Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen gr
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16

Salakhutdinov, Ruslan. "Learning Deep Generative Models." Thesis, 2009. http://hdl.handle.net/1807/19226.

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Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that th
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17

Tran, Dustin. "Probabilistic Programming for Deep Learning." Thesis, 2020. https://doi.org/10.7916/d8-95c9-sj96.

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We propose the idea of deep probabilistic programming, a synthesis of advances for systems at the intersection of probabilistic modeling and deep learning. Such systems enable the development of new probabilistic models and inference algorithms that would otherwise be impossible: enabling unprecedented scales to billions of parameters, distributed and mixed precision environments, and AI accelerators; integration with neural architectures for modeling massive and high-dimensional datasets; and the use of computation graphs for automatic differentiation and arbitrary manipulation of probabilist
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18

Dinh, Laurent. "Reparametrization in deep learning." Thèse, 2018. http://hdl.handle.net/1866/21139.

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19

"Can Knowledge Rich Sentences Help Language Models To Solve Common Sense Reasoning Problems?" Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55573.

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abstract: Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language.
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20

Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.

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Abstract In recent years, deep neural networks have achieved extraordinary performance on supervised learning tasks. Convolutional neural networks (CNN) have vastly improved the state of the art for most computer vision tasks including object recognition and segmentation. However, their success relies on the presence of a large amount of labeled data. In contrast, relatively fewer work has been done in deep learning to handle scenarios when access to ground truth is limited, partial or completely absent. In this thesis, we propose models to handle challenging problems with limited labeled inf
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21

Almahairi, Amjad. "Advances in deep learning with limited supervision and computational resources." Thèse, 2018. http://hdl.handle.net/1866/23434.

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Les réseaux de neurones profonds sont la pierre angulaire des systèmes à la fine pointe de la technologie pour une vaste gamme de tâches, comme la reconnaissance d'objets, la modélisation du langage et la traduction automatique. Mis à part le progrès important établi dans les architectures et les procédures de formation des réseaux de neurones profonds, deux facteurs ont été la clé du succès remarquable de l'apprentissage profond : la disponibilité de grandes quantités de données étiquetées et la puissance de calcul massive. Cette thèse par articles apporte plusieurs contributions à l'avanc
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22

Tan, Shawn. "Latent variable language models." Thèse, 2018. http://hdl.handle.net/1866/22131.

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