Relevant bibliographies by topics / Deep Learning Applications / Dissertations / Theses
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Dissertations / Theses on the topic 'Deep Learning Applications'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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1

Mariani, Tommaso. "Deep reinforcement learning for industrial applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20548/.

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In recent years there has been a growing attention from the world of research and companies in the field of Machine Learning. This interest, thanks mainly to the increasing availability of large amounts of data, and the respective strengthening of the hardware sector useful for their analysis, has led to the birth of Deep Learning. The growing computing capacity and the use of mathematical optimization techniques, already studied in depth but with few applications due to a low computational power, have then allowed the development of a new approach called Reinforcement Learning. This thesis w
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Aktaş, Ümit Ruşen. "Learning deep representations for robotics applications." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8777/.

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In this thesis, two hierarchical learning representations are explored in computer vision tasks. First, a novel graph theoretic method for statistical shape analysis, called Compositional Hierarchy of Parts (CHOP), was proposed. The method utilises line-based features as its building blocks for the representation of shapes. A deep, multi-layer vocabulary is learned by recursively compressing this initial representation. The key contribution of this work is to formulate layerwise learning as a frequent sub-graph discovery problem, solved using the Minimum Description Length (MDL) principle. The
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Alzubaidi, Laith. "Deep learning for medical imaging applications." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227812/1/Laith_Alzubaidi_Thesis.pdf.

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This thesis investigated novel deep learning techniques for advanced medical imaging applications. It addressed three major research issues of employing deep learning for medical imaging applications including network architecture, lack of training data, and generalisation. It proposed three new frameworks for CNN network architecture and three novel transfer learning methods. The proposed solutions have been tested on four different medical imaging applications demonstrating their effectiveness and generalisation. These solutions have already been employed by the scientific community showing
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ROMDHANA, ANDREA. "Deep Reinforcement Learning Driven Applications Testing." Doctoral thesis, Università degli studi di Genova, 2023. https://hdl.handle.net/11567/1105635.

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Applications have become indispensable in our lives, and ensuring their correctness is now a critical issue. Automatic system test case generation can significantly improve the testing process for these applications, which has recently motivated researchers to work on this problem, defining various approaches. However, most state-of-the-art approaches automatically generate test cases leveraging symbolic execution or random exploration techniques. This led to techniques that lose efficiency when dealing with an increasing number of program constraints and become inapplicable when conditions ar
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Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.

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Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis
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Tatarchenko, Maxim [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Scalable 3D deep learning: methods and applications." Freiburg : Universität, 2020. http://d-nb.info/1216826706/34.

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Lamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.

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This thesis describes a project developed throughout a six months internship in the Machine Vision Laboratory of Datalogic based in Pasadena, California. The project aims to develop a deep learning system as a possible solution for industrial optical character recognition applications. In particular, the focus falls on a specific algorithm called You Only Look Once (YOLO), which is a general-purpose object detector based on convolutional neural networks that currently offers state-of-the-art performances in terms of trade-off between speed and accuracy. This algorithm is indeed well known fo
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Elkaref, Mohab. "Deep learning applications for transition-based dependency parsing." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8620/.

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Dependency Parsing is a method that builds dependency trees consisting of binary relations that describe the syntactic role of words in sentences. Recently, dependency parsing has seen large improvements due to deep learning, which enabled richer feature representations and flexible architectures. In this thesis we focus on the application of these methods to Transition-based parsing, which is a faster variant. We explore current architectures and examine ways to improve their representation capabilities and final accuracies. Our first contribution is an improvement on the basic architecture a
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Kang, Le. "Document and natural image applications of deep learning." Thesis, University of Maryland, College Park, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3726222.

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<p> A tremendous amount of digital visual data is being collected every day, and we need efficient and effective algorithms to extract useful information from that data. Considering the complexity of visual data and the expense of human labor, we expect algorithms to have enhanced generalization capability and depend less on domain knowledge. While many topics in computer vision have benefited from machine learning, some document analysis and image quality assessment problems still have not found the best way to utilize it. In the context of document images, a compelling need exists for reliab
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Banville, Hubert. "Enabling real-world EEG applications with deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG005.

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Au cours des dernières décennies, les avancées révolutionnaires en neuroimagerie ont permis de considérablement améliorer notre compréhension du cerveau. Aujourd'hui, avec la disponibilité croissante des dispositifs personnels de neuroimagerie portables, tels que l'EEG mobile " à bas prix ", une nouvelle ère s’annonce où cette technologie n'est plus limitée aux laboratoires de recherche ou aux contextes cliniques. Les applications de l’EEG dans le " monde réel " présentent cependant leur lot de défis, de la rareté des données étiquetées à la qualité imprévisible des signaux et leur résolution
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Ishaq, Omer. "Image Analysis and Deep Learning for Applications in Microscopy." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-283846.

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Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range
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Yan, Shiyang. "Visual attention mechanism in deep learning and its applications." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3028892/.

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Recently, in computer vision, a branch of machine learning, called deep learning, has attracted high attention due to its superior performance in various computer vision tasks such as image classification, object detection, semantic segmentation, action recognition and image description generation. Deep learning aims at discovering multiple levels of distributed representations, which have been validated to be discriminatively powerful in many tasks. Visual attention is an ability of the vision system to selectively focus on the salient and relevant features in a visual scene. The core objecti
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Jan, Asim. "Deep learning based facial expression recognition and its applications." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15944.

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Facial expression recognition (FER) is a research area that consists of classifying the human emotions through the expressions on their face. It can be used in applications such as biometric security, intelligent human-computer interaction, robotics, and clinical medicine for autism, depression, pain and mental health problems. This dissertation investigates the advanced technologies for facial expression analysis and develops the artificial intelligent systems for practical applications. The first part of this work applies geometric and texture domain feature extractors along with various mac
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Hou, Xianxu. "An investigation of deep learning for image processing applications." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52056/.

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Significant strides have been made in computer vision over the past few years due to the recent development in deep learning, especially deep convolutional neural networks (CNNs). Based on the advances in GPU computing, innovative model architectures and large-scale dataset, CNNs have become the workhorse behind the state of the art performance for most computer vision tasks. For instance, the most advanced deep CNNs are able to achieve and even surpass human-level performance in image classification tasks. Deep CNNs have demonstrated the ability to learn very powerful image features or repres
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Chen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.

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CAVAZZA, JACOPO. "Learning by correlation for computer vision applications: from Kernel methods to deep learning." Doctoral thesis, Università degli studi di Genova, 2018. http://hdl.handle.net/11567/929851.

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Learning to spot analogies and differences within/across visual categories is an arguably powerful approach in machine learning and pattern recognition which is directly inspired by human cognition. In this thesis, we investigate a variety of approaches which are primarily driven by correlation and tackle several computer vision applications.
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Zuffa, Flavio. "Data and ground-truth generation for industrial deep learning applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Questa tesi espone il lavoro che ho svolto durante la mia permanenza presso Datalogic USA Inc. che si trova ad Eugene(OR), USA. I sistemi OCR industriali si sono affidati per molti anni ai metodi classici della computer vision. I laboratori Datalogic stanno lavorando su sistemi basati sul deep learning per la creazione di sistemi di nuova generazione. I metodi che fanno uso di deep learning necessitano di una grande quantità di dati d'alta qualità per poter effettuare il processo di learning in modo efficace. Durante la mia permanenza ho lavorato sulla creazione di un sistema per la produzio
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Sarker, Md Mostafa Kamal. "Efficient Deep Learning Models and Their Applications to Health Informatics." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/668480.

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This thesis designed and implemented efficient deep learning methods to solve classification and segmentation problems in two major health informatics domains, namely pervasive sensing and medical imaging. In the area of pervasive sensing, this thesis focuses only on food and related scene classification for health and nutrition analysis. This thesis used deep learning models to find the answer of two important two questions, “where we eat?’’ and ‘’what we eat?’’ for properly monitoring our health and nutrition condition. This is a new research domain, so this thesis presented entire scenario
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García, López Javier. "Geometric computer vision meets deep learning for autonomous driving applications." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/672708.

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This dissertation intends to provide theoretical and practical contributions on the development of deep learning algorithms for autonomous driving applications. The research is motivated by the need of deep neural networks (DNNs) to get a full understanding of the surrounding area and to be executed on real driving scenarios with real vehicles equipped with specific hardware, such as memory constrained (DSP or GPU platforms) or multiple optical sensors, which constraints the algorithm's development forcing the designed deep networks to be accurate, with minimum number of operations and low
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Alammari, Ali. "Traffic Forecasting Applications Using Crowdsourced Traffic Reports and Deep Learning." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703305/.

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Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show t
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Rezk, Nesma. "Exploring Efficient Implementations of Deep Learning Applications on Embedded Platforms." Licentiate thesis, Högskolan i Halmstad, Centrum för forskning om inbyggda system (CERES), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41969.

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The promising results of deep learning (deep neural network) models in many applications such as speech recognition and computer vision have aroused a need for their realization on embedded platforms. Augmenting DL (Deep Learning) in embedded platforms grants them the support to intelligent tasks in smart homes, mobile phones, and healthcare applications. Deep learning models rely on intensive operations between high precision values. In contrast, embedded platforms have restricted compute and energy budgets. Thus, it is challenging to realize deep learning models on embedded platforms. In thi
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GHASSEMI, SINA. "Deep Learning for Image Analysis in Satellite and Traffic Applications." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2740594.

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Pham, Cuong X. "Advanced techniques for data stream analysis and applications." Thesis, Griffith University, 2023. http://hdl.handle.net/10072/421691.

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Deep learning (DL) is one of the most advanced AI techniques that has gained much attention in the last decade and has also been applied in many successful applications such as market stock prediction, object detection, and face recognition. The rapid advances in computational techniques like Graphic Processing Units (GPU) and Tensor Processing Units (TPU) have made it possible to train large deep learning models to obtain high accuracy surpassing human ability in some tasks, e.g., LipNet [9] achieves 93% of accuracy compared with 52% of human to recognize the word from speaker lips movement.
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Poudel, Prabesh. "Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617199500076786.

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Grünwald, Adam. "APPLICATIONS OF DEEP LEARNING IN TEXT CLASSIFICATION FOR HIGHLY MULTICLASS DATA." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385162.

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Text classification using deep learning is rarely applied to tasks with more than ten target classes. This thesis investigates if deep learning can be successfully applied to a task with over 1000 target classes. A pretrained Long Short-Term Memory language model is fine-tuned and used as a base for the classifier. After five days of training, the deep learning model achieves 80.5% accuracy on a publicly available dataset, 9.3% higher than Naive Bayes. With five guesses, the model predicts the correct class 92.2% of the time.
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Öhman, Wilhelm. "Data augmentation using military simulators in deep learning object detection applications." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264917.

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While deep learning solutions have made great progress in recent years, the requirement of large labeled datasets still limit their practical use in certain areas. This problem is especially acute for solutions in domains where even unlabeled data is a limited resource, such as the military domain. Synthetic data, or artificially generated data, has recently attracted attention as a potential solution for this problem. This thesis explores the possibility of using synthetic data in order to improve the performance of a neural network aimed at detecting and localizing firearms in images. To gen
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Lechevallier, Antoine. "Physics Informed Deep Learning : Applications to well opening and closing events." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS062.

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La réduction des émissions de CO2 dans l'atmosphère est primordiale afin d'accomplir la transition écologique. Le stockage géologique du CO2 est un instrument essentiel parmi les stratégies de capture et de stockage du CO2. Les simulations numériques fournissent la solution aux équations de l'écoulement multiphasique qui modélisent le comportement du site d'injection de CO2. Elles constituent un outil essentiel pour décider de l'exploitation ou non d'un site potentiel de stockage de CO2. Cependant, les simulations numériques d'écoulement en milieu poreux sont exigeantes en termes de calcul : i
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Ceccarelli, Mattia. "Optimization and applications of deep learning algorithms for super-resolution in MRI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21694/.

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The increasing amount of data produced by modern infrastructures requires instruments of analysis more and more precise, quick, and efficient. For these reasons in the last decades, Machine Learning (ML) and Deep Learning (DL) techniques saw exponential growth in publications and research from the scientific community. In this work are proposed two new frameworks for Deep Learning: Byron written in C++, for fast analysis in a parallelized CPU environment, and NumPyNet written in Python, which provides a clear and understandable interface on deep learning tailored around readability. Byron wil
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Marini, Michela. "Representation learning and applications in neuronal imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19776/.

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Confocal fluorescence microscopy is a microscopic technique that provides true three-dimensional (3D) optical resolution and that allows the visualization of molecular expression patterns and morphological structures. This technique has therefore become increasingly more important in neuroscience, due to its applications in image-based screening and profiling of neurons. However, in the last two decades, many approaches have been introduced to segment the neurons automatically. With the more recent advances in the field of neural networks and Deep Learning, multiple methods have been implemen
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Stock, Pierre. "Efficiency and Redundancy in Deep Learning Models : Theoretical Considerations and Practical Applications." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSEN008.

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Les réseaux de neurones profonds sont à l'origine de percées majeures en intelligence artificielle. Ce succès s'explique en partie par un passage à l'échelle en termes de puissance de calcul, d'ensembles de données d'entrainement et de taille des modèles considérés -- le dernier point ayant été rendu possible en construisant des réseaux de plus en plus profonds. Dans cette thèse, partant du constat que de tels modèles sont difficiles à appréhender et à entrainer, nous étudions l'ensemble des réseaux de neurones à travers leurs classes d'équivalence fonctionnelles, ce qui permet de les grouper
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Zhang, Zhiwang. "Deep Learning for Vision and Language Applications: from Scene Graph to Captioning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29546.

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In this thesis, we propose novel deep learning algorithms for the vision and language tasks, including 3D scene graph generation and dense video captioning. The dense video captioning task is to firstly detect multiple key events from the untrimmed video and then describe each key event using natural language. The 3D scene graph generation task is to segment the object on an indoor 3D scene and then predict the predicates between every two objects. The main contributions of this thesis are listed below. Firstly, we formulated the dense video captioning task as a new visual cue-aided sentence
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BUSTREO, MATTEO. "Coping with Data Scarcity in Deep Learning and Applications for Social Good." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1040720.

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The recent years are experiencing an extremely fast evolution of the Computer Vision and Machine Learning fields: several application domains benefit from the newly developed technologies and industries are investing a growing amount of money in Artificial Intelligence. Convolutional Neural Networks and Deep Learning substantially contributed to the rise and the diffusion of AI-based solutions, creating the potential for many disruptive new businesses. The effectiveness of Deep Learning models is grounded by the availability of a huge amount of training data. Unfortunately, data collecti
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Barbano, Carlo Alberto Maria. "Collateral-Free Learning of Deep Representations : From Natural Images to Biomedical Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT038.

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L’apprentissage profond est devenu l'un des outils prédominants pour résoudre une variété de tâches, souvent avec des performances supérieures à celles des méthodes précédentes. Les modèles d'apprentissage profond sont souvent capables d'apprendre des représentations significatives et abstraites des données sous-jacentes. Toutefois, il a été démontré qu'ils pouvaient également apprendre des caractéristiques supplémentaires, qui ne sont pas nécessairement pertinentes ou nécessaires pour la tâche souhaitée. Cela peut poser un certain nombre de problèmes, car ces informations supplémentaires peuv
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Donon, Balthazar. "Deep statistical solvers & power systems applications." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG016.

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Confrontés à l'intégration croissante d'énergies renouvelables intermittentes et à de nouveaux mécanismes de marché, les réseaux électriques sont dans une phase de mutation profonde. Ainsi, face à une complexité croissante, RTE, le gestionnaire du réseau de transport d'électricité français, étudie les opportunités offertes par les méthodes issues du Deep Learning. Les changements de topologie (façon dont les lignes sont interconnectées) étant quotidiens, il est essentiel de permettre aux réseaux de neurones de prendre en compte la structure des données, ce qui est rendu possible par l'utilisat
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Wang, Qianlong. "Blockchain-Empowered Secure Machine Learning and Applications." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1625183576139299.

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Zhang, Yi. "NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/83.

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Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice ju
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Sheikhalishahi, Seyedmostafa. "Machine learning applications in Intensive Care Unit." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339274.

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The rapid digitalization of the healthcare domain in recent years highlighted the need for advanced predictive methods particularly based upon deep learning methods. Deep learning methods which are capable of dealing with time- series data have recently emerged in various fields such as natural language processing, machine translation, and the Intensive Care Unit (ICU). The recent applications of deep learning in ICU have increasingly received attention, and it has shown promising results for different clinical tasks; however, there is still a need for the benchmark models as far as a handful
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Palangi, Hamid. "Deep learning for sequence modelling : applications in natural languages and distributed compressive sensing." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61157.

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The underlying data in many machine learning tasks have a sequential nature. For example, words generated by a language model depend on the previously generated words, behavior of a user in a social network evolves over different snapshots of the social graph over time, different speech frames in a speech recognition system depend on the previously generated frames, etc. The main question is, how can we leverage the sequential nature of data to extract better features for the target machine learning task? In an effort to address this question, this thesis presents three important applications
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PAVIGLIANITI, ANNUNZIATA. "Neural Models in Biomedical Applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2957752.

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Yan, Yongzhe. "Deep Face Analysis for Aesthetic Augmented Reality Applications." Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAC011.

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La détection précise et robuste des composants faciaux est d’une grande importance pour la bonne expérience utilisateur dans les applications de réalité augmentée à destination de l’industrie esthétique telles que le maquillage virtuel et la coloration virtuelle des cheveux. Dans ce contexte, cette thèse aborde le problème de la détection des composants faciaux via la détection des repères faciaux et la segmentation des composantes faciales. Cette thèse se concentre sur les modèles basés sur l’apprentissage profond.La première partie de cette thèse aborde le problème de la détection des repère
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Abdolghader, Pedram. "Coherent Nonlinear Raman Microscopy and the Applications of Deep Learning & Pattern Recognition Methods to the Extraction of Quantitative Information." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42677.

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Coherent Raman microscopy (CRM) is a powerful nonlinear optical imaging technique based on contrast via Raman active molecular vibrations. CRM has been used in domains ranging from biology to medicine to geology in order to provide quick, sensitive, chemical-specific, and label-free 3D sectioning of samples. The Raman contrast is usually obtained by combining two ultrashort pulse input beams, known as Pump and Stokes, whose frequency difference is adjusted to the Raman vibrational frequency of interest. CRM can be used in conjunction with other imaging modalities such as second harmonic genera
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Pathak, Prabesh. "Leveraging attention-based deep neural networks for security vetting of Android applications." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617215079338328.

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Barnabò, Andrea. "Machine learning techniques for mammography applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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During this work we will use machine learning and deep learning techniques in order to face up to some medical problems where they can play a basic role. In particular we will apply these algorithms to some mammography issues. The thesis presents three main experiments that are described below. The first one consists of a classification between nipples and non-nipples images. In this part of the work we will build a dataset composed by images belonging to these two classes. The main purpose here will be to build a classifier able to distinguish between nipple and non-nipple images. Several mac
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ROSSI, MARCO. "DEEP LEARNING APPLICATIONS TO PARTICLE PHYSICS: FROM MONTE CARLO SIMULATION ACCELERATION TO PROTODUNE RECONSTRUCTION." Doctoral thesis, Università degli Studi di Milano, 2023. https://hdl.handle.net/2434/951789.

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The thesis arises in the context of deep learning applications to particle physics. The dissertation follows two main parallel streams: the development of hardware-accelerated tools for event simulation in high-energy collider physics, and the optimization of deep learning models for reconstruction algorithms at neutrino detectors. These two topics are anticipated by a review of the literature concerning the recent advancements of artificial intelligence models in particle physics. Event generation is a central concept in high-energy physics phenomenology studies. The state-of-the-art softwar
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Cao, Haitao. "A New Topology of Wavelet Neural Network for Fast Deep Learning of Big Data Applications." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424745.

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With technological innovations progressing rapidly, big data is now produced from various applications. Due to its capability of handling complex problems, Deep Neural Network (DNN) has become one of the fastest-growing and most exciting areas of Machine Learning (ML) in the data-intensive field. However, it remains a challenge to train such a deep structure, facing the gradient problems and slow convergence speed. In big data analysis, the original dataset is usually characterized as high dimensionality and the key features are very often buried in the noise, which increases computational com
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46

Avilov, Oleksii. "Deep learning methods for motor imagery detection from raw EEG : applications to brain-computer interfaces." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0032.

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Cette thèse présente trois contributions pour améliorer la reconnaissance d’imaginations motrices utilisées par de nombreuses interfaces cerveau-ordinateur (BCI) comme moyen d'interaction. Tout d'abord, nous proposons d'estimer la qualité des images motrices en détectant des valeurs aberrantes et de les supprimer avant apprentissage. Ensuite, nous étudions la sélection des caractéristiques pour sept imaginations de mouvements. Enfin, nous présentons une architecture d'apprentissage profond reprenant les principes du réseaux EEGnet applicable directement sur des signaux électro-encéphalographiq
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Albarqouni, Shadi [Verfasser], Nassir [Akademischer Betreuer] Navab, Nassir [Gutachter] Navab, and Dan [Gutachter] Stoyanov. "Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning / Shadi Albarqouni ; Gutachter: Nassir Navab, Dan Stoyanov ; Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1150852127/34.

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48

PAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.

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La Pattern Recognition è lo studio di come le macchine osservano l'ambiente, imparano a distinguere i pattern di interesse dal loro background e prendono decisioni valide e ragionevoli sulle categorie di modelli. Oggi l'applicazione degli algoritmi e delle tecniche di Pattern Recognition è trasversale. Con i recenti progressi nella computer vision, abbiamo la capacità di estrarre dati multimediali per ottenere informazioni preziose su ciò che sta accadendo nel mondo. Partendo da questa premessa, questa tesi affronta il tema dello sviluppo di sistemi di Pattern Recognition per applicazioni real
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Martin, Alice. "Deep learning models and algorithms for sequential data problems : applications to language modelling and uncertainty quantification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS007.

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Dans ce manuscrit de thèse, nous développons de nouveaux algorithmes et modèles pour résoudre les problèmes d'apprentissage profond sur de la donnée séquentielle, en partant des problématiques posées par l'apprentissage des modèles de langage basés sur des réseaux de neurones. Un premier axe de recherche développe de nouveaux modèles génératifs profonds basés sur des méthodes de Monte Carlo Séquentielles (SMC), qui permettent de mieux modéliser la diversité du langage, ou de mieux quantifier l'incertitude pour des problèmes de régression séquentiels. Un deuxième axe de recherche vise à facilit
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"Metric learning and deep learning: applications in computer vision." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291822.

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Liao, Renjie.<br>Thesis M.Phil. Chinese University of Hong Kong 2015.<br>Includes bibliographical references (leaves 80-87).<br>Abstracts also in Chinese.<br>Title from PDF title page (viewed on 15, November, 2016).
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