Дисертації з теми "Deep Discriminative Probabilistic Models"
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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Повний текст джерелаZhai, Menghua. "Deep Probabilistic Models for Camera Geo-Calibration." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/74.
Повний текст джерелаGeorgatzis, Konstantinos. "Dynamical probabilistic graphical models applied to physiological condition monitoring." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28838.
Повний текст джерелаWu, Di. "Human action recognition using deep probabilistic graphical models." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/6603/.
Повний текст джерелаSokolovska, Nataliya. "Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection." Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00006257.
Повний текст джерелаHager, 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.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 21).
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 link between these fields. The link focuses on probabilistic graphical models in the context of reinforcement learning. Viewing certain algorithms as reinforcement learning gives one an ability to map ML concepts to statistics problems. Training a multi-layer nonlinear perceptron algorithm is equivalent to structure learning problems in probabilistic graphical models (PGMs). The technique of boosting weak rules into an ensemble is weighted sampling. Finally regularizing neural networks using the dropout technique is conditioning on certain observations in PGMs.
by Paul Andrew Hager.
M. Eng.
Azizpour, Hossein. "Visual Representations and Models: From Latent SVM to Deep Learning." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192289.
Повний текст джерелаQC 20160908
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.
Повний текст джерелаQian, Weizhu. "Discovering human mobility from mobile data : probabilistic models and learning algorithms." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA025.
Повний текст джерелаSmartphone usage data can be used to study human indoor and outdoor mobility. In our work, we investigate both aspects in proposing machine learning-based algorithms adapted to the different information sources that can be collected.In terms of outdoor mobility, we use the collected GPS coordinate data to discover the daily mobility patterns of the users. To this end, we propose an automatic clustering algorithm using the Dirichlet process Gaussian mixture model (DPGMM) so as to cluster the daily GPS trajectories. This clustering method is based on estimating probability densities of the trajectories, which alleviate the problems caused by the data noise.By contrast, we utilize the collected WiFi fingerprint data to study indoor human mobility. In order to predict the indoor user location at the next time points, we devise a hybrid deep learning model, called the convolutional mixture density recurrent neural network (CMDRNN), which combines the advantages of different multiple deep neural networks. Moreover, as for accurate indoor location recognition, we presume that there exists a latent distribution governing the input and output at the same time. Based on this assumption, we develop a variational auto-encoder (VAE)-based semi-supervised learning model. In the unsupervised learning procedure, we employ a VAE model to learn a latent distribution of the input, the WiFi fingerprint data. In the supervised learning procedure, we use a neural network to compute the target, the user coordinates. Furthermore, based on the same assumption used in the VAE-based semi-supervised learning model, we leverage the information bottleneck theory to devise a variational information bottleneck (VIB)-based model. This is an end-to-end deep learning model which is easier to train and has better performance.Finally, we validate thees proposed methods on several public real-world datasets providing thus results that verify the efficiencies of our methods as compared to other existing methods generally used
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.
Повний текст джерелаEl-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.
Повний текст джерелаAzeraf, 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.
Повний текст джерелаMany 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
Hu, Xu. "Towards efficient learning of graphical models and neural networks with variational techniques." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1037.
Повний текст джерелаIn this thesis, I will mainly focus on variational inference and probabilistic models. In particular, I will cover several projects I have been working on during my PhD about improving the efficiency of AI/ML systems with variational techniques. The thesis consists of two parts. In the first part, the computational efficiency of probabilistic graphical models is studied. In the second part, several problems of learning deep neural networks are investigated, which are related to either energy efficiency or sample efficiency
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.
Повний текст джерелаText is one of the most pervasive and persistent sources of information. Content analysis of text in its broad sense refers to methods for studying and retrieving information from documents. Nowadays, with the ever increasing amounts of text becoming available online is several languages and different styles, content analysis of text is of tremendous importance as it enables a variety of applications. To this end, unsupervised representation learning methods such as topic models and word embeddings constitute prominent tools.The goal of this dissertation is to study and address challengingproblems in this area, focusing on both the design of novel text miningalgorithms and tools, as well as on studying how these tools can be applied to text collections written in a single or several languages.In the first part of the thesis we focus on topic models and more precisely on how to incorporate prior information of text structure to such models.Topic models are built on the premise of bag-of-words, and therefore words are exchangeable. While this assumption benefits the calculations of the conditional probabilities it results in loss of information.To overcome this limitation we propose two mechanisms that extend topic models by integrating knowledge of text structure to them. We assume that the documents are partitioned in thematically coherent text segments. The first mechanism assigns the same topic to the words of a segment. The second, capitalizes on the properties of copulas, a tool mainly used in the fields of economics and risk management that is used to model the joint probability density distributions of random variables while having access only to their marginals.The second part of the thesis explores bilingual topic models for comparable corpora with explicit document alignments. Typically, a document collection for such models is in the form of comparable document pairs. The documents of a pair are written in different languages and are thematically similar. Unless translations, the documents of a pair are similar to some extent only. Meanwhile, representative topic models assume that the documents have identical topic distributions, which is a strong and limiting assumption. To overcome it we propose novel bilingual topic models that incorporate the notion of cross-lingual similarity of the documents that constitute the pairs in their generative and inference processes. Calculating this cross-lingual document similarity is a task on itself, which we propose to address using cross-lingual word embeddings.The last part of the thesis concerns the use of word embeddings and neural networks for three text mining applications. First, we discuss polylingual document classification where we argue that translations of a document can be used to enrich its representation. Using an auto-encoder to obtain these robust document representations we demonstrate improvements in the task of multi-class document classification. Second, we explore multi-task sentiment classification of tweets arguing that by jointly training classification systems using correlated tasks can improve the obtained performance. To this end we show how can achieve state-of-the-art performance on a sentiment classification task using recurrent neural networks. The third application we explore is cross-lingual information retrieval. Given a document written in one language, the task consists in retrieving the most similar documents from a pool of documents written in another language. In this line of research, we show that by adapting the transportation problem for the task of estimating document distances one can achieve important improvements
Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.
Повний текст джерелаSalakhutdinov, Ruslan. "Learning Deep Generative Models." Thesis, 2009. http://hdl.handle.net/1807/19226.
Повний текст джерелаTran, Dustin. "Probabilistic Programming for Deep Learning." Thesis, 2020. https://doi.org/10.7916/d8-95c9-sj96.
Повний текст джерела"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.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2019
Dinh, Laurent. "Reparametrization in deep learning." Thèse, 2018. http://hdl.handle.net/1866/21139.
Повний текст джерелаTan, Shawn. "Latent variable language models." Thèse, 2018. http://hdl.handle.net/1866/22131.
Повний текст джерелаAlmahairi, Amjad. "Advances in deep learning with limited supervision and computational resources." Thèse, 2018. http://hdl.handle.net/1866/23434.
Повний текст джерелаDeep 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.