Literatura académica sobre el tema "DEEP LEARNING MODEL"
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Artículos de revistas sobre el tema "DEEP LEARNING MODEL"
Wang, Yating, Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev y Min Wang. "Deep multiscale model learning". Journal of Computational Physics 406 (abril de 2020): 109071. http://dx.doi.org/10.1016/j.jcp.2019.109071.
Texto completoXu, Zongben y Jian Sun. "Model-driven deep-learning". National Science Review 5, n.º 1 (25 de agosto de 2017): 22–24. http://dx.doi.org/10.1093/nsr/nwx099.
Texto completoShlezinger, Nir y Yonina C. Eldar. "Model-Based Deep Learning". Foundations and Trends® in Signal Processing 17, n.º 4 (2023): 291–416. http://dx.doi.org/10.1561/2000000113.
Texto completoBakhtiari, Shahab. "Can Deep Learning Model Perceptual Learning?" Journal of Neuroscience 39, n.º 2 (9 de enero de 2019): 194–96. http://dx.doi.org/10.1523/jneurosci.2209-18.2018.
Texto completoWu, Chong. "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology". International Journal of Machine Learning and Computing 11, n.º 3 (mayo de 2021): 182–87. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1033.
Texto completoSrinivas, Dr Kalyanapu y Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 11 (20 de noviembre de 2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.
Texto completoEvseenko, Alla y Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum". Transaction of Scientific Papers of the Novosibirsk State Technical University, n.º 1-2 (26 de agosto de 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.
Texto completo白家納, 白家納 y 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發". 理工研究國際期刊 12, n.º 1 (abril de 2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.
Texto completoHao, Xing, Guigang Zhang y Shang Ma. "Deep Learning". International Journal of Semantic Computing 10, n.º 03 (septiembre de 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.
Texto completoDjellali, Choukri y Mehdi adda. "An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network". Journal of Ubiquitous Systems and Pervasive Networks 15, n.º 01 (1 de marzo de 2021): 35–41. http://dx.doi.org/10.5383/juspn.15.01.005.
Texto completoTesis sobre el tema "DEEP LEARNING MODEL"
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.
Texto completoZeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING". OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.
Texto completoGiovanelli, Francesco. "Model Agnostic solution of CSPs with Deep Learning". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18633/.
Texto completoMatsoukas, 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.
Texto completoDen senaste utvecklingen inom djupinlärning har hjälp till att förbättra precisionen hos gaze estimation-modeller till nivåer som inte tidigare varit möjliga. Dock kräver djupinlärningsmetoder oftast både stora mängder beräkningar och minne som därmed begränsar dess användning i inbyggda system med små minnes- och beräkningsresurser. Det här arbetet syftar till att kringgå detta problem genom att öka prediktiv kraft i små nätverk som kan användas i inbyggda system, med hjälp av en modellkomprimeringsmetod som kallas distillation". Under begreppet destillation introducerar vi ytterligare en term till den komprimerade modellens totala optimeringsfunktion som är en avgränsande term mellan en komprimerad modell och en kraftfull modell. Vi visar att destillationsmetoden inför mer än bara brus i den komprimerade modellen. Det vill säga lärarens induktiva bias som hjälper studenten att nå ett bättre optimum tack vare adaptive error deduction. Utöver detta visar vi att MobileNet-familjen uppvisar instabila träningsfaser och vi rapporterar att den destillerade MobileNet25 överträffade sin lärare MobileNet50 något. Dessutom undersöker vi nyligen föreslagna träningsmetoder för att förbättra prediktionen hos små och tunna nätverk och vi konstaterar att extremt tunna arkitekturer är svåra att träna. Slutligen föreslår vi en ny träningsmetod baserad på hint-learning och visar att denna teknik hjälper de tunna MobileNets att stabiliseras under träning och ökar dess prediktiva effektivitet.
Lim, Steven. "Recommending TEE-based Functions Using a Deep Learning Model". Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104999.
Texto completoMaster of Science
Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.
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/.
Texto completoWu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation". Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.
Texto completoKayesh, Humayun. "Deep Learning for Causal Discovery in Texts". Thesis, Griffith University, 2022. http://hdl.handle.net/10072/415822.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Зайяд, Абдаллах Мухаммед. "Ecrypted Network Classification With Deep Learning". Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/34069.
Texto completoThis dissertation consists of 84 pages, 59 Figures and 29 sources in the reference list. Problem: As the world becomes more security conscious, more encryption protocols have been employed in ensuring suecure data transmission between communicating parties. Network classification has become more of a hassle with the use of some techniques as inspecting encrypted traffic can pose to be illegal in some countries. This has hindered network engineers to be able to classify traffic to differentiate encrypted from unencrypted traffic. Purpose of work: This paper aims at the problem caused by previous techniques used in encrypted network classification. Some of which are limited to data size and computational power. This paper employs the use of deep learning algorithm to solve this problem. The main tasks of the research: 1. Compare previous traditional techniques and compare their advantages and disadvantages 2. Study previous related works in the current field of research. 3. Propose a more modern and efficient method and algorithm for encrypted network traffic classification The object of research: Simple artificial neural network algorithm for accurate and reliable network traffic classification that is independent of data size and computational power. The subject of research: Based on data collected from private traffic flow in our own network simulation tool. We use our proposed method to identify the differences in network traffic payloads and classify network traffic. It helped to separate or classify encrypted from unencrypted traffic. 6 Research methods: Experimental method. We have carried out our experiment with network simulation and gathering traffic of different unencrypted protocols and encrypted protocols. Using python programming language and the Keras library we developed a convolutional neural network that was able to take in the payload of the traffic gathered, train the model and classify the traffic in our test set with high accuracy without the requirement of high computational power.
Zhao, Yajing. "Chaotic Model Prediction with Machine Learning". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.
Texto completoLibros sobre el tema "DEEP LEARNING MODEL"
Poonkuntran, S., Balamurugan Balusamy y Rajesh Kumar Dhanraj. Object Detection with Deep Learning Models. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736.
Texto completoDeep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. New York: Apress L. P., 2021.
Buscar texto completoBisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.
Texto completoPaper, David. State-of-the-Art Deep Learning Models in TensorFlow. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8.
Texto completoCTS student online assessment pilot study: An exploration of The Learning Manager (TLM) Model with Red Deer College. Edmonton, AB: Alberta Education, 2009.
Buscar texto completoEl-Amir, Hisham y Mahmoud Hamdy. Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow. Apress, 2019.
Buscar texto completoLattery, Mark J. Deep Learning in Introductory Physics: Exploratory Studies of Model-Based Reasoning. Information Age Publishing, 2016.
Buscar texto completoUrtāns, Ēvalds. Function shaping in deep learning. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226854.
Texto completo1st, Kala K. U. y Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.
Buscar texto completoJena, Om Prakash, Alok Ranjan Tripathy, Brojo Kishore Mishra y Ahmed A. Elngar, eds. Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150404011220301.
Texto completoCapítulos de libros sobre el tema "DEEP LEARNING MODEL"
Kumar, R. Santhosh y M. Kalaiselvi Geetha. "Deep Learning Model". En Data Science, 305–22. Boca Raton : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429263798-14.
Texto completoRodriguez, Andres. "Training a Model". En Deep Learning Systems, 73–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8_4.
Texto completoRodriguez, Andres. "Reducing the Model Size". En Deep Learning Systems, 111–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8_6.
Texto completoRen, Jianfeng y Dong Xia. "Deep Learning Model Optimization". En Autonomous driving algorithms and Its IC Design, 183–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2897-2_8.
Texto completoGhayoumi, Mehdi. "Finding the Best Model". En Deep Learning in Practice, 175–87. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003025818-8.
Texto completoSanghi, Nimish. "Model-Free Approaches". En Deep Reinforcement Learning with Python, 77–122. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_4.
Texto completoSanghi, Nimish. "Model-Based Algorithms". En Deep Reinforcement Learning with Python, 49–76. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_3.
Texto completoAmaratunga, Thimira. "Building Your First Deep Learning Model". En Deep Learning on Windows, 67–100. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6431-7_4.
Texto completoAmaratunga, Thimira. "Deploying Your Model as a Web Application". En Deep Learning on Windows, 215–31. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6431-7_9.
Texto completoLei, Chen. "Unsupervised Learning: Deep Generative Model". En Cognitive Intelligence and Robotics, 183–215. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2233-5_9.
Texto completoActas de conferencias sobre el tema "DEEP LEARNING MODEL"
Karatekin, Tamer, Selim Sancak, Gokhan Celik, Sevilay Topcuoglu, Guner Karatekin, Pinar Kirci y Ali Okatan. "Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity". 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.2019.00020.
Texto completoKee Wong, Yew. "Advanced Deep Learning Model". En 5th International Conference on Computer Science and Information Technology (COMIT 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111707.
Texto completoYerushalmi, Raz, Guy Amir, Achiya Elyasaf, David Harel, Guy Katz y Assaf Marron. "Scenario-assisted Deep Reinforcement Learning". En 10th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010904700003119.
Texto completoMiaschi, Alessio, Dominique Brunato, Felice Dell’Orletta y Giulia Venturi. "What Makes My Model Perplexed? A Linguistic Investigation on Neural Language Models Perplexity". En Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.deelio-1.5.
Texto completoAckerman, Samuel, Parijat Dube, Eitan Farchi, Orna Raz y Marcel Zalmanovici. "Machine Learning Model Drift Detection Via Weak Data Slices". En 2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2021. http://dx.doi.org/10.1109/deeptest52559.2021.00007.
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 completoKatz, Guy. "Guarded Deep Learning using Scenario-based Modeling". En 8th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009097601260136.
Texto completoGatto, Nicola, Evgeny Kusmenko y Bernhard Rumpe. "Modeling Deep Reinforcement Learning Based Architectures for Cyber-Physical Systems". En 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00033.
Texto completoKong, Phutphalla, Matei Mancas, Nimol Thuon, Seng Kheang y Bernard Gosselin. "Do Deep-Learning Saliency Models Really Model Saliency?" En 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451809.
Texto completoNarayanan, Niranjhana y Karthik Pattabiraman. "TF-DM: Tool for Studying ML Model Resilience to Data Faults". En 2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2021. http://dx.doi.org/10.1109/deeptest52559.2021.00010.
Texto completoInformes sobre el tema "DEEP LEARNING MODEL"
Zheng, Jian. Relational Patterns Discovery in Climate with Deep Learning Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, enero de 2021. http://dx.doi.org/10.7546/crabs.2021.01.05.
Texto completoAihara, Shimpei, Takara Saki, Tyusei Shibata, Toshiaki Matsubara, Ryosuke Mizukami, Yudai Yoshida y Akira Shionoya. Deep Learning Model for Integrated Estimation of Wheelchair and Human Poses Using Camera Images. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317545.
Texto completoRenchon, Alexandre, Roser Matamala, Miquel Gonzalez-Meler, Zoe Cardon, Sébastien Lacube, Julie Jastrow, Beth Drewniak, Jules Cacho y James Franke. Predictabilityand feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769763.
Texto completoMaher, Nicola, Pedro DiNezio, Antonietta Capotondi y Jennifer Kay. Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769719.
Texto completoFullan, Michael y Joanne Quinn. How Do Disruptive Innovators Prepare Today's Students to Be Tomorrow's Workforce?: Deep Learning: Transforming Systems to Prepare Tomorrow’s Citizens. Inter-American Development Bank, diciembre de 2020. http://dx.doi.org/10.18235/0002959.
Texto completoSelley, Austin. Deep Learning Model Segmentations on Computed Tomography 3D Reconstructions of Coffee Beans to Determine Void Ratio (U-Net) and Roast Level (LinkNet). Office of Scientific and Technical Information (OSTI), mayo de 2023. http://dx.doi.org/10.2172/1975634.
Texto completoPatwa, B., P. L. St-Charles, G. Bellefleur y B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.
Texto completoJiang, Peishi, Xingyuan Chen, Maruti Mudunuru, Praveen Kumar, Pin Shuai, Kyongho Son y Alexander Sun. Towards Trustworthy and Interpretable Deep Learning-assisted Ecohydrological Models. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769787.
Texto completoPettit, Chris y D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), junio de 2021. http://dx.doi.org/10.21079/11681/41034.
Texto completoGastelum, Zoe, Laura Matzen, Mallory Stites, Kristin Divis, Breannan Howell, Aaron Jones y Michael Trumbo. Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report. Office of Scientific and Technical Information (OSTI), septiembre de 2021. http://dx.doi.org/10.2172/1821527.
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