Literatura académica sobre el tema "Online deep learning"
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Artículos de revistas sobre el tema "Online deep learning"
Wu, Sheng, Ancong Wu y Wei-Shi Zheng. "Online deep transferable dictionary learning". Pattern Recognition 118 (octubre de 2021): 108007. http://dx.doi.org/10.1016/j.patcog.2021.108007.
Texto completoMurray‐Johnson, Kayon, Andrea Munro y Racheal Popoola. "Immersive deep learning activities online". New Directions for Adult and Continuing Education 2021, n.º 169 (marzo de 2021): 35–49. http://dx.doi.org/10.1002/ace.20412.
Texto completoLee, Hea-Jin y Eun-ok Baek. "Facilitating Deep Learning in a Learning Community". International Journal of Technology and Human Interaction 8, n.º 1 (enero de 2012): 1–13. http://dx.doi.org/10.4018/jthi.2012010101.
Texto completoXu, Jie, Yang Liu, Jinzhong Liu y Zuguang Qu. "Effectiveness of English Online Learning Based on Deep Learning". Computational Intelligence and Neuroscience 2022 (13 de abril de 2022): 1–10. http://dx.doi.org/10.1155/2022/1310194.
Texto completoZhang, Si-si, Jian-wei Liu, Xin Zuo, Run-kun Lu y Si-ming Lian. "Online deep learning based on auto-encoder". Applied Intelligence 51, n.º 8 (9 de enero de 2021): 5420–39. http://dx.doi.org/10.1007/s10489-020-02058-8.
Texto completoDu, Bingqian, Zhiyi Huang y Chuan Wu. "Adversarial Deep Learning for Online Resource Allocation". ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, n.º 4 (31 de diciembre de 2021): 1–25. http://dx.doi.org/10.1145/3494526.
Texto completoZinovyeva, Elizaveta, Wolfgang Karl Härdle y Stefan Lessmann. "Antisocial online behavior detection using deep learning". Decision Support Systems 138 (noviembre de 2020): 113362. http://dx.doi.org/10.1016/j.dss.2020.113362.
Texto completoJain, Prisha y Chaya Ravindra. "Classifying Emotional Engagement in Online Learning Via Deep Learning Architecture". International Journal of Advanced Engineering, Management and Science 10, n.º 5 (2024): 063–70. http://dx.doi.org/10.22161/ijaems.105.2.
Texto completoD, Swaroop Gowda y Ravi Dandu. "Machine Learning and Deep Learning Algorithm for Online Bullying Identification". International Journal for Research in Applied Science and Engineering Technology 11, n.º 6 (30 de junio de 2023): 2708–11. http://dx.doi.org/10.22214/ijraset.2023.53951.
Texto completoMusa Yaagoup, Khalid Mohammed y Mohamed Elhafiz Mustafa. "Online Arabic Handwriting Characters Recognition using Deep Learning". IJARCCE 9, n.º 10 (30 de octubre de 2020): 83–92. http://dx.doi.org/10.17148/ijarcce.2020.91014.
Texto completoTesis sobre el tema "Online deep learning"
Fourie, Aidan. ""Online Platform for Deep Learning Education"". Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31381.
Texto completoGuo, Song. "Online Multiple Object Tracking with Cross-Task Synergy". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24681.
Texto completoPham, Cuong X. "Advanced techniques for data stream analysis and applications". Thesis, Griffith University, 2023. http://hdl.handle.net/10072/421691.
Texto completoThesis (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.
Texto completoZhang, Xuan. "Product Defect Discovery and Summarization from Online User Reviews". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85581.
Texto completoPh. 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.
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.
Texto completoAdewopo, Victor A. "Exploring Open Source Intelligence for cyber threat Prediction". University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753.
Texto completoAl, 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.
Texto completoMeyer, 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.
Texto completoMany 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
Qiao, Zhilei. "Consumer-Centric Innovation for Mobile Apps Empowered by Social Media Analytics". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95983.
Texto completoPHD
Libros sobre el tema "Online deep learning"
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.
Buscar texto completoBlankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.
Buscar texto completoBlankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.
Buscar texto completoBlankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.
Buscar texto completoBlankenship, Rebecca J. Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.
Buscar texto completoBlankenship, Rebecca J. Handbook of Research on Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021.
Buscar texto completoYong, Liu y Matei Zaharia. Practical Deep Learning at Scale with MLflow: Bridge the Gap Between Offline Experimentation and Online Production. Packt Publishing, Limited, 2022.
Buscar texto completoMehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh y 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.
Texto completoMitchell, 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.
Buscar texto completoFisher, Elizabeth, Bettina Lange y Eloise Scotford. Environmental Law. 2a ed. Oxford University Press, 2019. http://dx.doi.org/10.1093/he/9780198811077.001.0001.
Texto completoCapítulos de libros sobre el tema "Online deep learning"
Lu, Huchuan y Dong Wang. "Visual Tracking Based on Deep Learning". En Online Visual Tracking, 101–26. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9_7.
Texto completoBharadi, Vinayak Ashok, Kaushal K. Prasad y Yogesh G. Mulye. "Classification of Slow and Fast Learners Using a Deep Learning Model". En Online Learning Systems, 1–11. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003272823-1.
Texto completoKang, Ke y Richard O. Sinnott. "Improving Online Argumentation Through Deep Learning". En 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.
Texto completoBisong, Ekaba. "Batch vs. Online Learning". En 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.
Texto completoSurma, Jerzy y Krzysztof Jagiełło. "Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks". En AI, Machine Learning and Deep Learning, 277–85. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158-21.
Texto completoFlynn, James, Stephen McKenzie y Jennifer Chung. "Back to the Education Future—Deep Online Learning Opportunities". En Tertiary Online Teaching and Learning, 219–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8928-7_20.
Texto completoHesham, Alaa y Abeer Hamdy. "Personality Traits of Twitter Bullies Using Deep Learning". En Artificial Intelligence and Online Engineering, 446–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17091-1_45.
Texto completoJayasekara, T. K., A. R. Weerasinghe y W. V. Welgama. "A Comprehensive Analysis of Aspect-Oriented Suggestion Extraction from Online Reviews". En Deep Learning Applications, Volume 4, 111–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6153-3_5.
Texto completoKe, Fengfeng y Alicia Fedelina Chávez. "Promoting Inclusive, Deep Learning in Online Contexts". En 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.
Texto completoGoyal, S. B., Kamarolhizam Bin Besah y Ashish Khanna. "Online Recommendation System Using Collaborative Deep Learning". En 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.
Texto completoActas de conferencias sobre el tema "Online deep learning"
Obeidat, Raghad, Rehab Duwairi y Ahmad Al-Aiad. "A Collaborative Recommendation System for Online Courses Recommendations". En 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.
Texto completoSahoo, Doyen, Quang Pham, Jing Lu y Steven C. H. Hoi. "Online Deep Learning: Learning Deep Neural Networks on the Fly". En 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.
Texto completoKoeshidayatullah, Ardiansyah, Jonathan L. Payne, Daniel J. Lehrmann, Michele Morsilli y Khalid Al-Ramadan. "REAL-TIME CARBONATE PETROGRAPHY WITH DEEP LEARNING". En GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-356742.
Texto completoKlochikhina, E., S. Frolov y N. Chemingui. "Deep Learning for Migration Artifacts Attenuation". En EAGE 2020 Annual Conference & Exhibition Online. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011932.
Texto completoAppling, Alison, Xiaowei Jia, Jared Willard, Samantha K. Oliver, Jeffrey M. Sadler, Jacob A. Zwart, Jordan S. Read y Vipin Kumar. "PROCESS-GUIDED DEEP LEARNING FOR WATER TEMPERATURE PREDICTION". En GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-354799.
Texto completoWunsch, Donald C. "Lifelong context recognition via online deep clustering". En Applications of Machine Learning 2023, editado por Barath Narayanan Narayanan, Michael E. Zelinski, Tarek M. Taha y Jonathan Howe. SPIE, 2023. http://dx.doi.org/10.1117/12.2683610.
Texto completoDuraisamy, Prakash, James Van Haneghan, Jude Thomas, Ramya Sri Gadaley y Jackson. "Online Classroom Enagement Observation using Deep Learning". En 2020 IEEE Learning with MOOCS (LWMOOCS). IEEE, 2020. http://dx.doi.org/10.1109/lwmoocs50143.2020.9234339.
Texto completoBloch, Anthony. "Online deep learning for behavior prediction". En Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2022, editado por Raja Suresh. SPIE, 2022. http://dx.doi.org/10.1117/12.2619359.
Texto completoAlali, A., V. Kazei, B. Altaf, X. Zhang y T. Alkhalifah. "Time-Lapse Cross-Equalization by Deep Learning". En EAGE 2020 Annual Conference & Exhibition Online. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011720.
Texto completoIunes Venturott, Lígia y Ruslan Mitkov. "Fake News Detection for Portuguese with Deep Learning". En TRanslation and Interpreting Technology ONline. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-071-7_016.
Texto completoInformes sobre el tema "Online deep learning"
Alhasson, Haifa F. y 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, noviembre de 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.
Texto completoPikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko y Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], junio de 2021. http://dx.doi.org/10.31812/123456789/4431.
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