Relevant bibliographies by topics / Online deep learning

Academic literature on the topic 'Online deep learning'

Author: Grafiati
Published: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Online deep learning":

1

Wu, Sheng, Ancong Wu, and Wei-Shi Zheng. "Online deep transferable dictionary learning." Pattern Recognition 118 (October 2021): 108007. http://dx.doi.org/10.1016/j.patcog.2021.108007.

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Murray‐Johnson, Kayon, Andrea Munro, and Racheal Popoola. "Immersive deep learning activities online." New Directions for Adult and Continuing Education 2021, no. 169 (March 2021): 35–49. http://dx.doi.org/10.1002/ace.20412.

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Lee, Hea-Jin, and Eun-ok Baek. "Facilitating Deep Learning in a Learning Community." International Journal of Technology and Human Interaction 8, no. 1 (January 2012): 1–13. http://dx.doi.org/10.4018/jthi.2012010101.

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The purpose of this study is to explore how the integration of online discussion into a mathematics methods course affected pre-service teachers’ learning. Students’ transcription of online discussion was analyzed using a mixed methods approach, combining computer-mediated discourse analysis and Chi-square test analysis. The data revealed that the online discussion helped pre-service teachers not only deepen their learning of mathematics methods, but also demonstrated their abilities to teach mathematics in different ways. It also indicated that the depth of their learning depended on the levels of threads and topics of discussion. Deep learning occurs 1) more often in the first level thread than subsequent level threads, and 2) in discussion topics, primarily those related to practice-based issues rather than theory-based topics.
4

Xu, Jie, Yang Liu, Jinzhong Liu, and Zuguang Qu. "Effectiveness of English Online Learning Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/1310194.

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With the popularization of the Internet lifestyle and the innovation of learning methods, more and more online learning systems have emerged, allowing users to study in the system anytime and anywhere. While providing convenience to users, online learning systems also bring troubles to users, who cannot quickly find the resources they are interested in from the huge amount of learning resources. In this paper, we apply deep learning to an English online learning platform and analyze learners and learning contents by clustering algorithm and association rules. Based on this, a content organization system is developed using genetic algorithms, which is applied to the case of this paper to provide learners with personalized learning content. With the hope that the system can be extended to other online learning platforms in the future, three data mining techniques were selected to solve the problems found in the English online learning platform, and we designed how these techniques should be applied to the online learning platform. The first technique is the cluster mining technique, which is used to analyze learners’ profiles, classify learners in different categories, provide them with personalized learning content, and organize group learning. The second technique is association rules, which is used to analyze the correlation between learning contents. For the adaptive student-teacher knowledge migration strategy, the teacher model can guide the student model to track online and migrate the task-specific knowledge to the online tracking student model through the network parameters. Finally, a case study is selected and the above design is applied to this case study, and the results are analyzed in detail. The data mining technology is applied to the English online learning platform, and an innovative English learning content organization system is developed. It is hoped that the results of this study will have some practical value for promotion and provide an idea for the construction of the online learning platform, and it is also expected that the idea can improve the quality of online learning to a certain extent. Specifically, the online student model is adaptively updated by the teacher model parameters and the online student model parameters together.
5

Zhang, Si-si, Jian-wei Liu, Xin Zuo, Run-kun Lu, and Si-ming Lian. "Online deep learning based on auto-encoder." Applied Intelligence 51, no. 8 (January 9, 2021): 5420–39. http://dx.doi.org/10.1007/s10489-020-02058-8.

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Du, Bingqian, Zhiyi Huang, and Chuan Wu. "Adversarial Deep Learning for Online Resource Allocation." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 4 (December 31, 2021): 1–25. http://dx.doi.org/10.1145/3494526.

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Online algorithms are an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usually relies on problem-specific assumptions. Inspired by adversarial training from Generative Adversarial Net and the fact that the competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks (NNs) to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input. Specifically, we leverage two NNs as the algorithm and the adversary, respectively, and let them play a zero sum game, with the adversary being responsible for generating worst-case input while the algorithm learns the best strategy based on the input provided by the adversary. To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action instead of only sampled actions. To the best of our knowledge, our work is the first using deep NNs to design an online algorithm from the perspective of worst-case performance guarantee. Empirical studies show that our updating methods ensure convergence to Nash equilibrium and the learned algorithm outperforms state-of-the-art online algorithms under various settings.
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Zinovyeva, Elizaveta, Wolfgang Karl Härdle, and Stefan Lessmann. "Antisocial online behavior detection using deep learning." Decision Support Systems 138 (November 2020): 113362. http://dx.doi.org/10.1016/j.dss.2020.113362.

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Jain, Prisha, and Chaya Ravindra. "Classifying Emotional Engagement in Online Learning Via Deep Learning Architecture." International Journal of Advanced Engineering, Management and Science 10, no. 5 (2024): 063–70. http://dx.doi.org/10.22161/ijaems.105.2.

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The world has seen a phenomenal rise in online learning over the past decade, with universities shifting courses to online modes, MOOCs(Massive Open Online Course) emerging and laptop and tab-based initiatives being extensively promoted. However, educators face significant challenges in analyzing learning environments due to issues like lack of in-person cues, small video size, etc. To address these challenges, it is crucial to analyze the engagement levels of online classes. Out of the various subcategories of engagement, emotional engagement is one that is overlooked, but integral to analysis and deterministic in its approach. In response, we developed a deep learning architecture to analyze emotional engagement in online classes. Our method utilizes a ResNet50-based algorithm, refined through experimentation with various techniques such as transfer learning, optimizers, and pre-trained weights. The model adds a unique layer to the analysis of different algorithms used for engagement detection in academia while also achieving stellar rates of 81.34% validation accuracy and 81.04% training accuracy. Unlike other models, our approach employs high-quality image data for training, ensuring more reliable results. Moreover, we constructed a novel framework for applying emotional engagement to real-world scenarios, thus bridging the pre-existing gap between implementation and academia. The integration of this technology into online learning has immense potential, and can bring with it a shift in the quality of education. By fostering a safe and healthy learning space for every student, we can significantly enhance the effectiveness of online education systems.
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D, Swaroop Gowda, and Ravi Dandu. "Machine Learning and Deep Learning Algorithm for Online Bullying Identification." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2708–11. http://dx.doi.org/10.22214/ijraset.2023.53951.

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Abstract: The popularity of information technologies has led to bullying in cyberbullying and social media has become its main place compared to mobile phones, gaming platforms and messaging. Cyberbullying can take many forms, including sexual harassment, threats, hate mail, and posting false information about a person that millions of people can see and read. Compared to bullying, cyberbullying has a longer-term impact on victims, which can affect them physically, emotionally, psychologically or in combination. Suicides due to cyberbullying have been on the rise in recent years, and India is among the four countries with the highest incidence.
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Musa Yaagoup, Khalid Mohammed, and Mohamed Elhafiz Mustafa. "Online Arabic Handwriting Characters Recognition using Deep Learning." IJARCCE 9, no. 10 (October 30, 2020): 83–92. http://dx.doi.org/10.17148/ijarcce.2020.91014.

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You might also be interested in the extended bibliographies on the topic 'Online deep learning' for particular source types:
Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Online deep learning":

1

Fourie, Aidan. ""Online Platform for Deep Learning Education"." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31381.

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My thesis is going to focus on the development of a standalone, web based, machine learning educational platform. This platform will have a specific focus on neural networks. This tool will have the primary intention to provide a theoretical background to the mathematics of neural networks and thereafter to allow users to train their own networks on regression problems of their own creation. This is so as to provide the user with both theoretical, and first-hand, experience in the applications and functions of artificial intelligence. The primary success metric of this project will be how informative it is to the user. The key deliverable will be a fully functional prototype in additional to a written piece inclusive of a literature review and any other relevant findings and conclusions.
2

Guo, Song. "Online Multiple Object Tracking with Cross-Task Synergy." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24681.

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Multiple object tracking (MOT) plays an important role in computer vision and has been studied for years. It aims to identify and keep track of objects appear in a given video sequence, which is useful for applications like autonomous driving. Modern online MOT methods usually adopt tracking-by-detection paradigm, where detections of objects are performed first, followed by data association between the found detections to establish identities of tracked targets. Current main challenge of MOT is from the occlusions incurred by interactions among moving objects, where detections may fail to be produced and lead to gaps in tracking. The two stages in the tracking-by-detection naturally shows two possible directions to tackle the problem, and most methods usually focus on one of them, either to improve tracking performance through conducting prediction on targets' new locations in a new frame, or to enhance robustness of embeddings used for similarity metrics in data association. However, occlusion is a shared problem in both of the two tasks, thus improving them individually is less optimal than linking them together to deal with the issue. To address the problem, a novel unified model is proposed to bring synergy between the two tasks of position prediction and embedding association and its remarkable ability in dealing with occlusions is shown. The two tasks are linked by temporal-aware target attention and distractor attention as well as discriminative embedding aggregation. With attentions generated from identity-specific aggregated embeddings, position prediction focuses more on targets and less on distractors, therefore make more correct predictions. This enables refined embedding extraction under occlusion with the help of attention modules, to produce enriched embeddings for association and aggregation. In this way, the model involves the two tasks with each other to create synergy in between and shows stronger robustness to occlusions. The method is evaluated on MOTChallenge benchmarks to show its state-of-the-art performance. Extensive analyses are conducted to verify the effectiveness of each designed component.
3

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. Most of the current deep learning research work has focused on designing a deep architecture working in a static environment where the whole training set is known in advance. However, in many real-world applications like predicting financial markets, autonomous cars, and sensor networks, the data often comes in the form of streams with massive volume, and high velocity has affected the scalability of deep learning models. Learning from such data is called continual, incremental, or online learning. When learning a deep model in dynamic environments where the data come from streams, the modern deep learning models usually suffer the so-called catastrophic forgetting problem, one of the most challenging issues that have not been solved yet. Catastrophic forgetting occurs when a model learns new knowledge, i.e., new objects or classes, but its performance in the previously learned classes reduces significantly. The cause of catastrophic forgetting in the deep learning model has been identified and is related to the weight-sharing property. In detail, the model updating the corresponding weights to capture knowledge of the new tasks may push the learned weights of the past tasks away and cause the model performance to degrade. According to the stability-plasticity dilemma [17], if the model weights are too stable, it will not be able to acquire new knowledge, while a model with high plasticity can have large weight changes leading to significant forgetting of the previously learned patterns. Many approaches have been proposed to tackle this issue, like imposing constraints on weights (regularizations) or rehearsal from experience, but significant research gap still exists. First, current regularization methods often do not simultaneously consider class imbalance and catastrophic forgetting. Moreover, these methods usually require more memory to store previous versions of the model, which sometimes is not able to hold a copy of a substantial deep model due to memory constraints. Second, existing rehearsal approaches pay little attention to selecting and storing critical instances that help the model to retain as much knowledge of the learned tasks. This study focuses on dealing with these challenges by proposing several novel methods. We first proposed a new loss function that combines two loss terms to deal with class imbalance data and catastrophic forgetting simultaneously. The former is a modification of a widely used loss function for class imbalance learning, called Focal loss, to handle the exploding gradient (loss goes to NaN) and the ability to learn from highly confident data points. At the same time, the latter is a novel loss term that addresses the catastrophic forgetting within the current mini-batch. In addition, we also propose an online convolution neural network (OCNN) architecture for tabular data that act as a base classifier in an ensemble system (OECNN). Next, we introduce a rehearsal-based method to prevent catastrophic forgetting. In which we select a triplet of instances within each mini-batch to store in the memory buffer. We find that these instances are identified as crucial instances that can help either remind the model of easy tasks or revise for the hard ones. We also propose a class-wise forgetting detector that monitors the performance of each class encountered so far in a stream. If a class’s performance drops below a predefined threshold, that class is identified as a forgetting class. Finally, based on the nature of data which often comprises many modalities, we study online multi-modal multi-task (M3T) learning problems. Unlike the traditional methods in stable environments, online M3T learning need to be considered in many scenarios like missing modalities and incremental tasks. We establish the setting for six frequently happened scenarios for M3T. Most of the existing works in M3T fail to run on all of these scenarios. Therefore, we propose a novel M3T deep learning model called UniCNet that can work on all of these scenarios and achieves superior performance compared with state-of-the-art M3T methods. To conclude, this dissertation contributes to novel computational techniques that deal with catastrophic forgetting problem in continual deep learning.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Idani, Arman. "Assessment of individual differences in online social networks using machine learning." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270109.

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The services that define our personal and professional lives are increasingly accessed through digital devices, which store extensive records of our behaviour. An individual's psychological profile can be accurately assessed using offline behaviour, and I investigate if an automated machine learning system can measure the same psychological factors, only from observing the footprints of online behaviour, without observing any offline behaviour or any direct input from the individual. Prior research shows that psychological traits such as personality can be predicted using these digital footprints, although current state-of-the-art accuracy is below psychometric standards of reliability and self-reports consistently outperform machine-ratings in external validity. I introduce a new machine learning system that is capable of doing five-factor personality assessments, as well as other psychological assessments, from online data as accurately as self-report questionnaires in terms of reliability, internal consistency and external and discriminant validity, and demonstrate that passive psychological assessment can be a realistic option in addition to self-report questionnaires for both research and practice. Achieving this goal is not possible using conventional dimensionality reduction and linear regression models. Here I develop a supervised dimensionality reduction method capable of intelligently selecting only useful parts of data for the relevant prediction at hand which also does not lose variance when eliminating redundancies. In the learning stage, instead of linear regression models, I use an ensemble of decision trees which are able to distinguish scenarios where the same observations on digital data can mean different things for different individuals. This work highlights the interesting idea that similar to how a human expert who is able to assess personality from offline behaviour, an expert machine learning system is able to assess personality from online behaviour. It also demonstrates that big-5 personality are predictors of how predictable users are in social media, with neuroticism having the greatest correlation with unpredictability, while openness having the greatest correlation with predictability.
5

Zhang, Xuan. "Product Defect Discovery and Summarization from Online User Reviews." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85581.

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Product defects concern various groups of people, such as customers, manufacturers, government officials, etc. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. As a kind of opinion mining research, existing defect discovery methods mainly focus on how to classify the type of product issues, which is not enough for users. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. These challenges cannot be solved by existing aspect-oriented opinion mining models, which seldom consider the defect entities mentioned above. Furthermore, users also want to better capture the semantics of review text, and to summarize product defects more accurately in the form of natural language sentences. However, existing text summarization models including neural networks can hardly generalize to user review summarization due to the lack of labeled data. In this research, we explore topic models and neural network models for product defect discovery and summarization from user reviews. Firstly, a generative Probabilistic Defect Model (PDM) is proposed, which models the generation process of user reviews from key defect entities including product Model, Component, Symptom, and Incident Date. Using the joint topics in these aspects, which are produced by PDM, people can discover defects which are represented by those entities. Secondly, we devise a Product Defect Latent Dirichlet Allocation (PDLDA) model, which describes how negative reviews are generated from defect elements like Component, Symptom, and Resolution. The interdependency between these entities is modeled by PDLDA as well. PDLDA answers not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, the problem of how to summarize user reviews more accurately, and better capture the semantics in them, is studied using deep neural networks, especially Hierarchical Encoder-Decoder Models. For each of the research topics, comprehensive evaluations are conducted to justify the effectiveness and accuracy of the proposed models, on heterogeneous datasets. Further, on the theoretical side, this research contributes to the research stream on product defect discovery, opinion mining, probabilistic graphical models, and deep neural network models. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.
Ph. D.
Product defects concern various groups of people, such as customers, manufacturers, and government officials. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. Furthermore, users also want to better summarize product defects more accurately in the form of natural language sentences. These requirements cannot be satisfied by existing methods, which seldom consider the defect entities mentioned above, or hardly generalize to user review summarization. In this research, we develop novel Machine Learning (ML) algorithms for product defect discovery and summarization. Firstly, we study how to identify product defects and their related attributes, such as Product Model, Component, Symptom, and Incident Date. Secondly, we devise a novel algorithm, which can discover product defects and the related Component, Symptom, and Resolution, from online user reviews. This method tells not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, we address the problem of how to summarize user reviews in the form of natural language sentences using a paraphrase-style method. On the theoretical side, this research contributes to multiple research areas in Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.
6

Nguyen, Trang Pham Ngoc. "A privacy preserving online learning framework for medical diagnosis applications." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2503.

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Electronic Health records are an important part of a digital healthcare system. Due to their significance, electronic health records have become a major target for hackers, and hospitals/clinics prefer to keep the records at local sites protected by adequate security measures. This introduces challenges in sharing health records. Sharing health records however, is critical in building an accurate online diagnosis framework. Most local sites have small data sets, and machine learning models developed locally based on small data sets, do not have knowledge about other data sets and learning models used at other sites. The work in this thesis utilizes the framework of coordinating the blockchain technology and online training mechanism in order to address the concerns of privacy and security in a methodical manner. Specifically, it integrates online learning with a permissioned blockchain network, using transaction metadata to broadcast a part of models while keeping patient health information private. This framework can treat different types of machine learning models using the same distributed dataset. The study also outlines the advantages and drawbacks of using blockchain technology to tackle the privacy-preserving predictive modeling problem and to improve interoperability amongst institutions. This study implements the proposed solutions for skin cancer diagnosis as a representative case and shows promising results in preserving security and providing high detection accuracy. The experimentation was done on ISIC dataset, and the results were 98.57, 99.13, 99.17 and 97,18 in terms of precision, accuracy, F1-score and recall, respectively.
7

Adewopo, Victor A. "Exploring Open Source Intelligence for cyber threat Prediction." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753.

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Al, Rawashdeh Khaled. "Toward a Hardware-assisted Online Intrusion Detection System Based on Deep Learning Algorithms for Resource-Limited Embedded Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535464571843315.

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Meyer, Lucas. "Deep Learning en Ligne pour la Simulation Numérique à Grande Échelle." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALM001.

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Nombreuses applications d’ingénieries et recherches scientifiques nécessitent la simulation fidèle de phénomènes complexes et dynamiques, transcrits mathématiquement en Équations aux Dérivées Partielles (EDP). Les solutions de ces EDP sont généralement approximées au moyen de solveurs qui effectuent des calculs intenses et génèrent des quantités importantes de données. Les applications requièrent rarement une unique simulation, mais plutôt un ensemble d’exécutions pour différents paramètres afin d’analyser la sensibilité du phénomène ou d’en trouver une configuration optimale. Ces larges ensembles de simulations sont limités par des temps de calcul importants et des capacités de stockage mémoire finies. La problématique du coût de calcul a jusqu’à présent encouragé le développement du calcul haute-performance (HPC) et de techniques de réductions de modèles. Récemment, le succès de l'apprentissage profond a poussé la communauté scientifique à considérer son usage pour accélérer ces ensembles de simulations. Cette thèse s'inscrit dans ce cadre en explorant tout d'abord deux techniques d’apprentissage pour la simulation numérique. La première propose d’utiliser une série de convolutions sur une hiérarchie de graphes pour reproduire le champ de vitesse d’un fluide tel que généré par le solveur à tout pas de temps de la simulation. La seconde hybride des algorithmes de régression avec des techniques classiques de réduction de modèles pour prédire les coefficients de toute nouvelle simulation dans une base réduite obtenue par analyse en composantes principales. Ces deux approches, comme la majorité de celles présentées dans la littérature, sont supervisées. Leur entraînement nécessite de générer a priori de nombreuses simulations. Elles souffrent donc du même problème qui a motivé leur développement : générer un jeu d’entraînement de simulations fidèles à grande échelle est laborieux. Nous proposons un cadre d’apprentissage générique pour l’entraînement de réseaux de neurones artificiels à partir de simulations générées à la volée tirant profit des ressources HPC. Les données sont produites en exécutant simultanément plusieurs instances d’un solveur pour différents paramètres. Le solveur peut lui-même être parallélisé sur plusieurs unités de calcul. Dès qu’un pas de temps est simulé, il est directement transmis pour effectuer l’apprentissage. Aucune donnée générée par le solveur n’est donc sauvegardée sur disque, évitant ainsi les coûteuses opérations d’écriture et de lecture et la nécessité de grands volumes de stockage. L’apprentissage se fait selon une distribution parallèle des données sur plusieurs GPUs. Comme il est désormais en ligne, cela crée un biais dans les données d’entraînement, comparativement à une situation classique où les données sont échantillonnées uniformément sur un ensemble de simulations disponibles a priori. Nous associons alors chaque GPU à une mémoire tampon en charge de mélanger les données produites. Ce formalisme a permis d’améliorer les capacités de généralisation de modèles issus de l’état de l’art, en les exposant à une diversité globale de données simulées plus riches qu’il n’aurait été faisable lors d’un entraînement classique. Des expériences montrent que l’implémentation de la mémoire tampon est cruciale pour garantir un entraînement de qualité à haut débit. Ce cadre d’apprentissage a permis d’entraîner un réseau à reproduire des simulations de diffusion thermique en moins de 2 heures sur 8TB de données générées et traitées in situ, améliorant ainsi les prédictions de 47% par rapport à un entraînement classique
Many engineering applications and scientific discoveries rely on faithful numerical simulations of complex phenomena. These phenomena are transcribed mathematically into Partial Differential Equation (PDE), whose solution is generally approximated by solvers that perform intensive computation and generate tremendous amounts of data. The applications rarely require only one simulation but rather a large ensemble of runs for different parameters to analyze the sensitivity of the phenomenon or to find an optimal configuration. Those large ensemble runs are limited by computation time and finite memory capacity. The high computational cost has led to the development of high-performance computing (HPC) and surrogate models. Recently, pushed by the success of deep learning in computer vision and natural language processing, the scientific community has considered its use to accelerate numerical simulations. The present thesis follows this approach by first presenting two techniques using machine learning for surrogate models. First, we propose to use a series of convolutions on hierarchical graphs to reproduce the velocity of fluids as generated by solvers at any time of the simulation. Second, we hybridize regression algorithms with classical reduced-order modeling techniques to identify the coefficients of any new simulation in a reduced basis computed by proper orthogonal decomposition. These two approaches, as the majority found in the literature, are supervised. Their training needs to generate a large number of simulations. Thus, they suffer the same problem that motivated their development in the first instance: generating many faithful simulations at scale is laborious. We propose a generic training framework for artificial neural networks that generate data simulations on-the-fly by leveraging HPC resources. Data are produced by running simultaneously several instances of the solver for different parameters. The solver itself can be parallelized over several processing units. As soon as a time step is computed by any simulation, it is streamed for training. No data is ever written on disk, thus overcoming slow input-output operations and alleviating the memory footprint. Training is performed by several GPUs with distributed data-parallelism. Because the training is now online, it induces a bias in the data compared to classical training, for which they are sampled uniformly from an ensemble of simulations available a priori. To mitigate this bias, each GPU is associated with a memory buffer in charge of mixing the incoming simulation data. This framework has improved the generalization capabilities of state-of-the-art architectures by exposing them during training to a richer diversity of data than would have been feasible with classical training. Experiments show the importance of the memory buffer implementation in guaranteeing generalization capabilities and high throughput training. The framework has been used to train a deep surrogate for heat diffusion simulation in less than 2 hours on 8TB of data processed in situ, thus increasing the prediction accuracy by 47% compared to a classical setting
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Qiao, Zhilei. "Consumer-Centric Innovation for Mobile Apps Empowered by Social Media Analytics." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95983.

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Due to the rapid development of Internet communication technologies (ICTs), an increasing number of social media platforms exist where consumers can exchange comments online about products and services that businesses offer. The existing literature has demonstrated that online user-generated content can significantly influence consumer behavior and increase sales. However, its impact on organizational operations has been primarily focused on marketing, with other areas understudied. Hence, there is a pressing need to design a research framework that explores the impact of online user-generated content on important organizational operations such as product innovation, customer relationship management, and operations management. Research efforts in this dissertation center on exploring the co-creation value of online consumer reviews, where consumers' demands influence firms' decision-making. The dissertation is composed of three studies. The first study finds empirical evidence that quality signals in online product reviews are predictors of the timing of firms' incremental innovation. Guided by the product differentiation theory, the second study examines how companies' innovation and marketing differentiation strategies influence app performance. The last study proposes a novel text analytics framework to discover different information types from user reviews. The research contributes theoretical and practical insights to consumer-centric innovation and social media analytics literature.
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Books on the topic "Online deep learning":

1

Alberta. Alberta Education. System Improvement Group. CTS student online assessment pilot study: An exploration of The Learning Manager (TLM) Model with Red Deer College. Edmonton, AB: Alberta Education, 2009.

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Blankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.

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Blankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.

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Blankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.

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Blankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.

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Blankenship, Rebecca J. Handbook of Research on Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.

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Yong, Liu, and Matei Zaharia. Practical Deep Learning at Scale with MLflow: Bridge the Gap Between Offline Experimentation and Online Production. Packt Publishing, Limited, 2022.

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Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, and Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.

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The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.
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Mitchell, Emily. Google Classroom for Teachers: Don't Resist the Change, Go Digital, Learn How to Teach Online Without Making Any Mistakes. Pedagogy, Deep Distance Learning, Digital Classroom Management and Much More. Independently Published, 2020.

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Fisher, Elizabeth, Bettina Lange, and Eloise Scotford. Environmental Law. 2nd ed. Oxford University Press, 2019. http://dx.doi.org/10.1093/he/9780198811077.001.0001.

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All books in this flagship series contain carefully selected substantial extracts from key cases, legislation, and academic debate, providing able students with a stand-alone resource. Environmental Law: Text, Cases & Materials provides students with a deep understanding of environmental law while also encouraging critical reflection of legal reasoning and pointing out areas of controversy and debate. The authors present a wide range of extracts from UK, EU, and international cases, legislation, and articles to help support learning and demonstrate both how the law works in practice and how it should or could work, clearly guiding students through key areas while providing insightful explanations and analysis. Topics have been carefully selected to support a wide range of environmental law courses, within law school and beyond. These include pollution control, nature conservation, climate change regulation, town planning, and water regulation, all incorporating aspects of law from local, UK, EU and international legal cultures. With its unique combination of extracts and author discussion, this new edition provides a wide-ranging, stimulating, and fresh approach to environmental law, which can be relied upon throughout your course and career. This book is also accompanied by an Online Resource Centre that features updates to the law, further reading suggestions, and useful weblinks.

Book chapters on the topic "Online deep learning":

1

Lu, Huchuan, and Dong Wang. "Visual Tracking Based on Deep Learning." In Online Visual Tracking, 101–26. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9_7.

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Bharadi, Vinayak Ashok, Kaushal K. Prasad, and Yogesh G. Mulye. "Classification of Slow and Fast Learners Using a Deep Learning Model." In Online Learning Systems, 1–11. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003272823-1.

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Kang, Ke, and Richard O. Sinnott. "Improving Online Argumentation Through Deep Learning." In Computational Science and Its Applications – ICCSA 2018, 376–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95162-1_26.

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Bisong, Ekaba. "Batch vs. Online Learning." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 199–201. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_15.

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Surma, Jerzy, and Krzysztof Jagiełło. "Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks." In AI, Machine Learning and Deep Learning, 277–85. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158-21.

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Flynn, James, Stephen McKenzie, and Jennifer Chung. "Back to the Education Future—Deep Online Learning Opportunities." In Tertiary Online Teaching and Learning, 219–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8928-7_20.

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Hesham, Alaa, and Abeer Hamdy. "Personality Traits of Twitter Bullies Using Deep Learning." In Artificial Intelligence and Online Engineering, 446–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17091-1_45.

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Jayasekara, T. K., A. R. Weerasinghe, and W. V. Welgama. "A Comprehensive Analysis of Aspect-Oriented Suggestion Extraction from Online Reviews." In Deep Learning Applications, Volume 4, 111–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6153-3_5.

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Ke, Fengfeng, and Alicia Fedelina Chávez. "Promoting Inclusive, Deep Learning in Online Contexts." In Web-Based Teaching and Learning across Culture and Age, 143–54. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-0863-5_8.

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Goyal, S. B., Kamarolhizam Bin Besah, and Ashish Khanna. "Online Recommendation System Using Collaborative Deep Learning." In Proceedings of Data Analytics and Management, 267–80. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7615-5_24.

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Conference papers on the topic "Online deep learning":

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Obeidat, Raghad, Rehab Duwairi, and Ahmad Al-Aiad. "A Collaborative Recommendation System for Online Courses Recommendations." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00018.

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Sahoo, Doyen, Quang Pham, Jing Lu, and Steven C. H. Hoi. "Online Deep Learning: Learning Deep Neural Networks on the Fly." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/369.

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Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch setting, requiring the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream. We aim to address an open challenge of ``Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is more challenging as the optimization objective is non-convex, and regular DNN with standard backpropagation does not work well in practice for online settings. We present a new ODL framework that attempts to tackle the challenges by learning DNN models which dynamically adapt depth from a sequence of training data in an online learning setting. Specifically, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy on large data sets (both stationary and concept drifting scenarios).
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Koeshidayatullah, Ardiansyah, Jonathan L. Payne, Daniel J. Lehrmann, Michele Morsilli, and Khalid Al-Ramadan. "REAL-TIME CARBONATE PETROGRAPHY WITH DEEP LEARNING." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-356742.

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Klochikhina, E., S. Frolov, and N. Chemingui. "Deep Learning for Migration Artifacts Attenuation." In EAGE 2020 Annual Conference & Exhibition Online. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011932.

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Appling, Alison, Xiaowei Jia, Jared Willard, Samantha K. Oliver, Jeffrey M. Sadler, Jacob A. Zwart, Jordan S. Read, and Vipin Kumar. "PROCESS-GUIDED DEEP LEARNING FOR WATER TEMPERATURE PREDICTION." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-354799.

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Wunsch, Donald C. "Lifelong context recognition via online deep clustering." In Applications of Machine Learning 2023, edited by Barath Narayanan Narayanan, Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2023. http://dx.doi.org/10.1117/12.2683610.

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Duraisamy, Prakash, James Van Haneghan, Jude Thomas, Ramya Sri Gadaley, and Jackson. "Online Classroom Enagement Observation using Deep Learning." In 2020 IEEE Learning with MOOCS (LWMOOCS). IEEE, 2020. http://dx.doi.org/10.1109/lwmoocs50143.2020.9234339.

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Bloch, Anthony. "Online deep learning for behavior prediction." In Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2022, edited by Raja Suresh. SPIE, 2022. http://dx.doi.org/10.1117/12.2619359.

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Alali, A., V. Kazei, B. Altaf, X. Zhang, and T. Alkhalifah. "Time-Lapse Cross-Equalization by Deep Learning." In EAGE 2020 Annual Conference & Exhibition Online. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011720.

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Iunes Venturott, Lígia, and Ruslan Mitkov. "Fake News Detection for Portuguese with Deep Learning." In TRanslation and Interpreting Technology ONline. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-071-7_016.

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Reports on the topic "Online deep learning":

1

Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.
2

Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glosbe, Collins Dictionary, Longman Dictionary, Oxford Dictionary. As a result of the deep analysis of these online dictionaries, we found out they have the next standard functions like the word explanations, transcription, audio pronounce, semantic connections, and examples of use. In propose dictionaries, we also found out the additional tools of learning foreign languages (mostly English) that can be effective. In general, we described sixteen functions of the online platforms for learning that can be useful in learning a foreign language. We have compiled a comparison table based on the next functions: machine translation, multilingualism, a video of pronunciation, an image of a word, discussion, collaborative edit, the rank of words, hints, learning tools, thesaurus, paid services, sharing content, hyperlinks in a definition, registration, lists of words, mobile version, etc. Based on the additional tools of online dictionaries we created a diagram that shows the functionality of analyzed platforms.

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