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Dissertations / Theses on the topic 'DEEP LEARNING MODEL'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Meng, Zhaoxin. "A deep learning model for scene recognition." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.

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Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene rec

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Zeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.

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fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the i

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Giovanelli, Francesco. "Model Agnostic solution of CSPs with Deep Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18633/.

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Negli ultimi anni, le tecniche di Deep Learning sono state notevolmente migliorate, permettendo di affrontare con successo numerosi problemi. Il Deep Learning ha un approccio sub-simbolico ai problemi, perciò non si rende necessario descrivere esplicitamente informazioni sulla struttura del problema per fare sì che questo possa essere affrontato con successo; l'idea è quindi di utilizzare reti neurali di Deep Learning per affrontare problemi con vincoli (CSPs), senza dover fare affidamento su conoscenza esplicita riguardo ai vincoli dei problemi. Chiamiamo questo approccio Model Agnostic; esso

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Matsoukas, Christos. "Model Distillation for Deep-Learning-Based Gaze Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261412.

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With the recent advances in deep learning, the gaze estimation models reached new levels, in terms of predictive accuracy, that could not be achieved with older techniques. Nevertheless, deep learning consists of computationally and memory expensive algorithms that do not allow their integration for embedded systems. This work aims to tackle this problem by boosting the predictive power of small networks using a model compression method called "distillation". Under the concept of distillation, we introduce an additional term to the compressed model’s total loss which is a bounding term between

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Lim, Steven. "Recommending TEE-based Functions Using a Deep Learning Model." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104999.

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Trusted execution environments (TEEs) are an emerging technology that provides a protected hardware environment for processing and storing sensitive information. By using TEEs, developers can bolster the security of software systems. However, incorporating TEE into existing software systems can be a costly and labor-intensive endeavor. Software maintenance—changing software after its initial release—is known to contribute the majority of the cost in the software development lifecycle. The first step of making use of a TEE requires that developers accurately identify which pieces of code would

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Del, Vecchio Matteo. "Improving Deep Question Answering: The ALBERT Model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20414/.

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Natural Language Processing is a field of Artificial Intelligence referring to the ability of computers to understand human speech and language, often in a written form, mainly by using Machine Learning and Deep Learning methods to extract patterns. Languages are challenging by definition, because of their differences, their abstractions and their ambiguities; consequently, their processing is often very demanding, in terms of modelling the problem and resources. Retrieving all sentences in a given text is something that can be easily accomplished with just few lines of code, but what about c

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Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

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Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat

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Kayesh, Humayun. "Deep Learning for Causal Discovery in Texts." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/415822.

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Causality detection in text data is a challenging natural language processing task. This is a trivial task for human beings as they acquire vast background knowledge throughout their lifetime. For example, a human knows from their experience that heavy rain may cause flood or plane accidents may cause death. However, it is challenging to automatically detect such causal relationships in texts due to the availability of limited contextual information and the unstructured nature of texts. The task is even more challenging for social media short texts such as Tweets as often they are informal, sh

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Зайяд, Абдаллах Мухаммед. "Ecrypted Network Classification With Deep Learning." Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/34069.

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Дисертація складається з 84 сторінок, 59 Цифри та 29 джерел у довідковому списку. Проблема: Оскільки світ стає більш безпечним, для забезпечення належної передачі даних між сторонами, що спілкуються, було використано більше протоколів шифрування. Класифікація мережі стала більше клопоту з використанням деяких прийомів, оскільки перевірка зашифрованого трафіку в деяких країнах може бути незаконною. Це заважає інженерам мережі мати можливість класифікувати трафік, щоб відрізняти зашифрований від незашифрованого трафіку. Мета роботи: Ця стаття спрямована на проблему, спричинену попередні

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Zhao, Yajing. "Chaotic Model Prediction with Machine Learning." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.

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Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and

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Saitas-Zarkias, Konstantinos. "Insights into Model-Agnostic Meta-Learning on Reinforcement Learning Tasks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290903.

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Meta-learning has been gaining traction in the Deep Learning field as an approach to build models that are able to efficiently adapt to new tasks after deployment. Contrary to conventional Machine Learning approaches, which are trained on a specific task (e.g image classification on a set of labels), meta-learning methods are meta-trained across multiple tasks (e.g image classification across multiple sets of labels). Their end objective is to learn how to solve unseen tasks with just a few samples. One of the most renowned methods of the field is Model-Agnostic Meta-Learning (MAML). The objec

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Lindespång, Victor. "Bildklassificering av bilar med hjälp av deep learning." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-58361.

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Den här rapporten beskriver hur en bildklassificerare skapades med förmågan att via en given bild på en bil avgöra vilken bilmodell bilen är av. Klassificeringsmodellen utvecklades med hjälp av bilder som företaget CAB sparat i samband med försäkringsärenden som behandlats via deras nuvarande produkter. Inledningsvis i rapporten så beskrivs teori för maskininlärning och djupinlärning på engrundläggande nivå för att leda in läsaren på ämnesområdet som rör rapporten, och fortsätter sedan med problemspecifika metoder som var till nytta för det aktuella problemet. Rapporten tar upp metoder för hur

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Hellström, Terese. "Deep-learning based prediction model for dose distributions in lung cancer patients." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-196891.

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Background To combat one of the leading causes of death worldwide, lung cancer treatment techniques and modalities are advancing, and the treatment options are becoming increasingly individualized. Modern cancer treatment includes the option for the patient to be treated with proton therapy, which can in some cases spare healthy tissue from excessive dose better than conventional photon radiotherapy. However, to assess the benefit of proton therapy compared to photon therapy, it is necessary to make both treatment plans to get information about the Tumour Control Probability (TCP) and the Norm

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Viebke, André. "Accelerated Deep Learning using Intel Xeon Phi." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-45491.

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Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in the industry and research community recently. State-of-the-art applications in the area of computer vision and speech recognition (among others) are built using deep learning algorithms. In contrast to traditional algorithms, where the developer fully instructs the application what to do, deep learning algorithms instead learn from experience when performing a task. However, for the algorithm to learn require training, which is a high computational challenge. High Performance Computing can help

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Iannello, Michele. "Deep Learning and Constrained Optimization for Epidemic Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25815/.

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The SARS-CoV-2 pandemic has galvanized the interest of the scientific community. Particular interest has been posed on methodologies apt at predicting the trend of the epidemiological curve, i.e. the daily number of infected individuals in the population. In this work, we argue for a model capable of producing intervention plans focused on counteracting the negative effects of an outbreak, with real applications on the ongoing pandemic. To do so, we relied on the use of Machine Learning models and Combinatorial Optimization approaches. The project entails the development of a ne

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Wang, Junpeng. "Interpreting and Diagnosing Deep Learning Models: A Visual Analytics Approach." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555499299957829.

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Wang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.

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In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were

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Di, Giacomo Emanuele. "A Deep Learning approach for predicting COSMO-Model's execution time." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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I modelli di previsione meteorologica sono programmi che permettono di simulare il tempo meteorologico futuro e formularne dunque una previsione. Il tempo di esecuzione di questi modelli è un aspetto critico, in quanto la loro utilità è basata sulle tempistiche con cui vengono prodotti i risultati. La previsione del tempo di esecuzione di un modello numerico di previsione meteorologica permette di ottimizzare sia la pianificazione dell'esecuzione del modello stesso che l'allocazione delle risorse a disposizione, nonché di individuare eventuali anomalie che si possono presentare e a fronte dell

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Beaudoin, Jean-Michel. "Growing deep roots : learning from the Essipit's culturally adapted model of Aboriginal forestry." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46590.

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Aboriginal peoples are seeking sustainable ways to steward and develop forests. Sustainable forestry is central to Aboriginal life and culture. Research indicates that the industrial forestry model has failed to address their socio-economic needs. To date, Aboriginal involvement in forestry is characterized by a limited economic role in forest development, limited influence over forest management, and an inability to integrate Aboriginal culture and values. The case study of Essipit (Quebec, Canada) provides new insight on how Aboriginal communities can contribute to sustainable forestry. Gro

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Li, Mengtong. "An intelligent flood evacuation model based on deep learning of various flood scenarios." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263634.

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Miao, Yishu. "Deep generative models for natural language processing." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258.

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Deep generative models are essential to Natural Language Processing (NLP) due to their outstanding ability to use unlabelled data, to incorporate abundant linguistic features, and to learn interpretable dependencies among data. As the structure becomes deeper and more complex, having an effective and efficient inference method becomes increasingly important. In this thesis, neural variational inference is applied to carry out inference for deep generative models. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here

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Zarrinkoub, Sahand. "Transfer Learning in Deep Structured Semantic Models for Information Retrieval." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286310.

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Recent approaches to IR include neural networks that generate query and document vector representations. The representations are used as the basis for document retrieval and are able to encode semantic features if trained on large datasets, an ability that sets them apart from classical IR approaches such as TF-IDF. However, the datasets necessary to train these networks are not available to the owners of most search services used today, since they are not used by enough users. Thus, methods for enabling the use of neural IR models in data-poor environments are of interest. In this work, a bag

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Moitra, Dipanjan. "Deep learning model for prediction of malignant tumors in human body with special reference to multimodel imaging techniques." Thesis, University of North Bengal, 2020. http://ir.nbu.ac.in/handle/123456789/4347.

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Miserocchi, Andrea. "The Fokker-Planck equation as model for the stochastic gradient descent in deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18290/.

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La discesa stocastica del gradiente (SGD) è alla base degli algoritmi di ottimizzazione di reti di Deep Learning più usati in AI, dal riconoscimento delle immagini all’elaborazione del linguaggio naturale. Questa tesi si propone di descrivere un modello basato sull’equazione di Fokker-Planck della dinamica del SGD. Si introduce la teoria dei processi stocastici, con particolare enfasi sulle equazioni di Langevin e sull’equazione di Fokker-Planck. Si mostra come il SGD minimizzi un funzionale sulla densità di probabilità dei pesi, non dipendente direttamente dalla funzione di costo. Infine si

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Sievert, Rolf. "Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175173.

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Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. Land-based litter instance segmentation is a relatively unexplored field, and this study aims to give a comparison of the instance segmentation models Mask R-CNN and DetectoRS using the multiclass litter dataset called Trash Annotations in Context (TACO) i

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Chen, Kuang-Yu, and 陳廣瑜. "Deep Learning Model Compression with Information Guide." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/w26d3z.

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WU, GUAN-WEI, and 吳冠瑋. "Applying Deep Learning Model to Medicine Discrimination." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/sz7t8v.

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碩士<br>逢甲大學<br>資訊工程學系<br>107<br>Medicines dispensing verification means that whether the medicines in the patient’s medicine bag are the same as the prescription prescribed by the doctor. The correct of medicines dispensing can be said to be the most basic safe drug condition for the patient. However, there are various kinds medicines but they may have a similar appearance and packaging. Therefore, the possibility of human error is greatly increased. Although many medical units have set up an online medicines discriminator system and provide people with using text or photos to get information a

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Hsu, Chun-Wei, and 徐莙惟. "Deep Learning Enabled Process Independent Lithographic Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/utrw8z.

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Liu, Zheng-Wei, and 劉政威. "Waterfall Model for Deep Reinforcement Learning Based Scheduling." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/a3yn5q.

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碩士<br>國立中央大學<br>通訊工程學系在職專班<br>107<br>The fourth generation of communication systems has been able to meet the multimedia application needs of mobile devices. Through the scheduling service provided by the base station, the user equipment can obtain the data packets required by the downlink of the communication system to meet and obtain better application services, so the channel resources are allocated and the calculation of the user group scheduling service is provided. The law is quite critical. This paper implements a mobile communication scheduling learning platform, and proposes a Deep De

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Yen, Yi-Tung, and 顏逸東. "Enhanced Car-Following Model with Deep Reinforcement Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/499erb.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>With the rapid evolution of artificial intelligence and technology, autonomous vehicle is regarded as the future of transportation. One of the important functions that autonomous vehicle should be equipped is a well-designed car-following model. With a well-designed car-following model, autonomous vehicle can drive in a safe, comfortable and efficient manner. This will increase driving safety, passenger comfort and improve road efficiency. This thesis designs and implements an enhanced car-following model. According to the laws, regulations and standards, we

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TIWARI, AVANISH. "IMAGE PARAGRAPH GENERATION USING DEEP LEARNING." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19204.

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Recently, a neural network based approach to automatic generation of image descriptions has become popular. Originally introduced as neural image captioning, it refers to a family of models where several neural network components are connected end-to-end to infer the most likely caption given an input image. Neural image captioning models usually comprise a Convolutional Neural Network (CNN) based image encoder and a Recurrent Neural Network (RNN) language model for generating image captions based on the output of the CNN. Generating long image captions – commonly referred to as par

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Shukla, Aditya. "Model Extraction and Active Learning." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4420.

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Machine learning models are increasingly being offered as a service by big companies such as Google, Microsoft and Amazon. They use Machine Learning as a Service (MLaaS) to expose these machine learning models to the end-users through cloud-based Application Programming Interface (API). Such APIs allow users to query ML models with data samples in a black-box fashion, returning only the corresponding output predictions. MLaaS models are generally monetized by billing the user for each query made. Prior work has shown that it is possible to extract these models. They developed model extraction

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CHEN, CHIA-HSI, and 陳家羲. "Develop Forecasting Model for Financial Crisis with Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3kp4yb.

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碩士<br>東吳大學<br>會計學系<br>106<br>Financial crisis forecasting of a company is extremely important for both investigators and company manager. For investigators, they are able to take actions before the crisis happens and therefore prevent assets loss. For company managers, they can adjust the management policy or direction according to the forecasting results so that the crisis would not happen.In this paper, we develop a deep neural network and train it using TEJ Financial database to obtain a forecasting model for financial crisis. Our model outperforms traditional shallow neural network by 10%

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Liu, Wen-Cheng, and 劉文誠. "A Web Service for Automatic Deep Learning Model Generation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/utec82.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>As technology advances, deep learning has changed the way many industries produce, such as detecting defects, identifying objects, and so on. The core network model is the core of the algorithm and the essence of the training after big data. However, for most operators, how to train a usable model from scratch is a major difficulty in introducing artificial intelligence on the production line. How to quickly and easily complete a usable deep learning model becomes an issue that most non-employed workers want to know. Usually, training a highly accurate deep lea

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Pradhan, Sipun Kumar, and 施庫瑪. "A Rapid Deep Learning Model for Goal-Oriented Dialog." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/6y6zpd.

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碩士<br>國立中央大學<br>資訊工程學系<br>104<br>Open-domain Question Answering (QA) systems aim at providing the exact answer(s) to questions formulated in natural language, without restriction of domain. My research goal in this thesis is to develop learning models that can automatically induce new facts without having to be re-trained, in particular its structure and meaning in order to solve multiple Open-domain QA tasks. The main advantage of this framework is that it requires little feature engineering and domain specificity whilst matching or surpassing state-of-the-art results. Furthermore, it can eas

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Tsao, Yeh-Wen, and 曹爗文. "A Fast Deep Learning Model for Time Series Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/94h345.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>Time series forecasting is an important research area across many domains, such as predictions of financial market, weather, electricity consumption, and traffic jam situation. However, most of recent works are usually time-consuming and complex. In this paper, we propose a deep learning model to tackle this issue, and deliver efficient performance. Our model uses purely Convolutional Neural Network (CNN) structure to capture both long-term and short-term features. Thorough empirical studies based upon the total seven different dataset demonstrate that the ou

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Chang, Chao-Mei, and 張昭美. "Taiwanese speech commands recognition model based on deep learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/knb7ws.

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碩士<br>國立交通大學<br>資訊學院資訊學程<br>107<br>Most of the recent machine learning papers are aimed at images and videos, such as face recognition, large image database identification, unmanned autopilot, object recognition, AlphaGo, object movement trajectory prediction, changing image style and creating virtual portrait style-based GAN. However, due to the development trend of voice assistants, it is necessary to closely cooperate with local language materials and culture habits. Therefore, the focus is on local language audio processing and machine learning . Benefiting from the prosperous deep learnin

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Lin, Yi-Hsiu, and 林怡秀. "Question Generation from Knowledge Base Using Deep Learning Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/mkj9vx.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>106<br>With the advancement of data-driven approach, the lack of corpora has become the main obstacle of the natural language processing research. Compared with English corpora, publicly available Mandarin corpora is even more lacking. Our paper purposes to solve this problem by using existing question answering dataset and knowledge base to create a new Mandarin question answering dataset. In this study, we first collect the data from CN-DBpedia and question answering dataset from WebQA and web crawler, and propose a method to combine them in the form of pairs as

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BAINS, INDERPREET SINGH. "WEB SECURITY IN IoT NETWORKS USING DEEP LEARNING MODEL." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18061.

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The vision of IoT is to interface day by day utilized items (which have the capacity of detecting and activation) to the Internet. This may or might possibly include human. IoT field is as yet developing and has many open issues. We develop on the digital security issues. The Web of things (IoT) is still in its beginning phases and has pulled in much enthusiasm for some mechanical parts including clinical fields, coordination’s following, savvy urban communities and autos. Anyway, as a paradigm, it is defenseless to a scope of significant intrusion threats. In IoT whenever there is a web

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"Efficient and Online Deep Learning through Model Plasticity and Stability." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.62959.

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abstract: The rapid advancement of Deep Neural Networks (DNNs), computing, and sensing technology has enabled many new applications, such as the self-driving vehicle, the surveillance drone, and the robotic system. Compared to conventional edge devices (e.g. cell phone or smart home devices), these emerging devices are required to deal with much more complicated and dynamic situations in real-time with bounded computation resources. However, there are several challenges, including but not limited to efficiency, real-time adaptation, model stability, and automation of architecture design. To

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Frazão, Xavier Marques. "Deep learning model combination and regularization using convolutional neural networks." Master's thesis, 2014. http://hdl.handle.net/10400.6/5605.

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Convolutional neural networks (CNNs) were inspired by biology. They are hierarchical neural networks whose convolutional layers alternate with subsampling layers, reminiscent of simple and complex cells in the primary visual cortex [Fuk86a]. In the last years, CNNs have emerged as a powerful machine learning model and achieved the best results in many object recognition benchmarks [ZF13, HSK+12, LCY14, CMMS12]. In this dissertation, we introduce two new proposals for convolutional neural networks. The first, is a method to combine the output probabilities of CNNs which we call Weighted

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HUANG, PO-YU, and 黃柏毓. "Predicting Social Insurance Payment Behavior Based on Deep Learning Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/04210692742079168717.

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碩士<br>逢甲大學<br>資訊工程學系<br>105<br>The social insurance is an important part of the social security system. In Taiwan, the social insurance system is classified by occupational groups and managed by different government agencies. According to the Executive Yuan of Taiwan, this pension system includes five separate social insurance programs covering public servants and teachers, laborers, military personnel, farmers, and a national pension insurance program for those not covered by the above four employment-based categories. Ministry of Health and Welfare in Taiwan is responsible for many types of

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Lee, Yi-Nan, and 李奕男. "Deep Visual Semantic Transform Model Learning from Multi-Label Images." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/48kv54.

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碩士<br>國立臺灣師範大學<br>資訊工程學系<br>105<br>Learning the relation between images and text semantics has been an important problem in the field of machine learning and computer vision. This paper addresses this problem. We observe that there is a semantic relation between texts, for example, “sky” and “cloud” have a close semantic relation, and “sky” and “car” have a weak semantic relation. We suppose the semantic relation between texts can be different depending on images. For example, an image contains both sky and car. The word “sky” and “car” are initially semantically irrelevant, but may have a con

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LEE, JHONG-TING, and 李仲庭. "Apply TensorFlow deep learning model for time series forecasting problem." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/y6cses.

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碩士<br>開南大學<br>資訊學院碩士在職專班<br>106<br>This study takes the TensorFlow as a backend engine for deep learning. The Multi-Layer Perceptron (MLP) is built to solve the time series forecasting problems. The case study are daily stock closing prices in Taiwan, i.e. the Taiwan Semiconductor Manufacturing Company Limited (TSMC), Uni-President Enterprises Corporation (Uni-President), and Largan Precision Company Limited (LARGAN Precision). We collect 120 daily records from 2017/01/03 to 2017/07/04. Around 20 input features we used are: the Trade Volume, the Trade Value, the Opening Price, the Highest Pric

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LAI, HUNG-JU, and 賴泓儒. "STT-MRAM Co-design Deep Learning Model for IoT Applications." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/c2a8z5.

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碩士<br>逢甲大學<br>通訊工程學系<br>107<br>At present, there are fewer STT-MRAM applications. The mian memories on the current market are SRAM, DRAM, and Flash Memory. However, these memories consume more power than STT-MRAM, which are not suitable for resource-limited IoT devices. The memories equipped with IoT devices must be low energy consumption, rapidly operatrion , access endurace, and samll area. STT-MARM just meets these requirements. In particular, STT-MARM is non-volatile. After the power turns off, the data are still reserved. Therefore, it is an emergent memory for IoT applications. In this

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Chung, Hao-Ting, and 鐘皓廷. "Building Student Course Performance Prediction Model Based on Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m2z8n3.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>106<br>The deferral of graduation rate in Taiwan’s universities is estimated 16%, which will affect the scheduling of school resources. Therefore, if we can expect to take notice of students’ academic performance and provide guidance to students who cannot pass the threshold as expected, we can effectively reduce the waste of school resources. In this research, we use recent years’ student data attributes and course results as training data to construct student performance prediction model. The K-Means algorithm was used to classify all courses from the freshman to t

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Teng, Yu-Han, and 鄧鈺翰. "Using a multimodal architecture Research on Deep Learning Model Analysis." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7het95.

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碩士<br>國立中央大學<br>資訊管理學系<br>107<br>With the popularity of social networks and e-commerce sites, users have switched from passively receiving messages to actively disseminating messages. The value of comments and online messages is also becoming more and more important. Analysis and research over the past few years. Trying to analyze trends about specific product products, topics, reviews, and tweets. Play an important role in all aspects. This study uses different vectorization processes to verify the multimodal analysis model and confirm that the model can effectively improve the accuracy. This

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Huang, Chu-Chih, and 黃炬智. "Classification of Chinese Articulation Disorder based on Deep Learning Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3v7km6.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>107<br>Articulation disorder means having difficulties during pronunciations, leading to incorrect articulations and unclear sentences. Articulation disorder has been a common child language issue. Currently, there is no any unified sayings for articulation disorder's classification in the Taiwan's medical field. Thus, a speech therapist is required for analysis and treatment in hospitals. After a series of pronunciations, a speech therapist will make an analysis based on children's pronunciations. Children will return to the hospitals for months continuously to impr

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LIN, HAN-LONG, and 林翰隆. "Building Graduate Salary Grading Prediction Model Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/z4hkqx.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>107<br>This paper used deep learning to build a salary grading prediction model. Due to the order relationship between each grading of salary grading, this paper regards this kind of problem as an ordinal regression problem. This paper used multiple output deep neural network to solve the ordinal regression problem so that the network learns the correlation between these salary grading during training. This model is pre-trained using Stacked De-noising Autoencoder. After pre-training, the corresponding weights are taken as the initial weights of neural network. Durin

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Lin, Yung-Chien, and 林詠謙. "Predicting the Computation Time of Deep Learning Model on Accelerators." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/w2wncd.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>With the rapid development of deep learning, in order to improve the efficiency of implementation, the hardware for deep learning is becoming more and more important, but the platform with higher performance is often accompanied by high prices. Therefore, the goal of this research is to let users can quickly calculate the performance of a system, and even can easily analyze its performance before getting the target hardware. There are a lot of related researches at present, but most of them use formulas to make performance predictions, and this method often u

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