Bibliografías temáticas / Super learning

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Literatura académica sobre el tema "Super learning"

Autor: Grafiati
Publicado: 6 de julio de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Super learning"

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Long, Jun, Jinhuan Zhang, and Ping Du. "Super-sampling by learning-based super-resolution." International Journal of Computational Science and Engineering 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijcse.2019.10020177.

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Du, Ping, Jinhuan Zhang, and Jun Long. "Super-sampling by learning-based super-resolution." International Journal of Computational Science and Engineering 21, no. 2 (2020): 249. http://dx.doi.org/10.1504/ijcse.2020.105731.

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Haris, Muhammad, M. Rahmat Widyanto, and Hajime Nobuhara. "Inception learning super-resolution." Applied Optics 56, no. 22 (2017): 6043. http://dx.doi.org/10.1364/ao.56.006043.

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GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.

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This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate mol
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Aitken, Michael R. F., Mark J. W. Larkin, and Anthony Dickinson. "Super-learning of Causal Judgements." Quarterly Journal of Experimental Psychology B 53, no. 1 (2000): 59–81. http://dx.doi.org/10.1080/027249900392995.

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Lim, Alane. "Machine learning method puts the “super” in super-resolution spectroscopy." Scilight 2021, no. 49 (2021): 491108. http://dx.doi.org/10.1063/10.0009031.

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Han, Tong, Li Zhao, and Chuang Wang. "Research on Super-resolution Image Based on Deep Learning." International Journal of Advanced Network, Monitoring and Controls 8, no. 1 (2023): 58–65. http://dx.doi.org/10.2478/ijanmc-2023-0046.

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Abstract Image super-resolution is a kind of important image processing technology in computer vision and image processing. It refers to the process of recovering high-resolution image from low-resolution image. It has a wide range of real-world applications, such as medical imaging, security and others. In addition to improving image perception quality, it also helps improve other computer vision tasks. Compared with traditional methods, deep learning methods show better reconstruction results in the field of image super-resolution reconstruction, and have gradually developed into the mainstr
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Jiang, Jingyu, Li Zhao, and Yan Jiao. "Research on Image Super-resolution Reconstruction Based on Deep Learning." International Journal of Advanced Network, Monitoring and Controls 7, no. 1 (2022): 1–21. http://dx.doi.org/10.2478/ijanmc-2022-0001.

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Abstract Image super-resolution reconstruction (SR) aims to use a specific algorithm to restore a low-resolution blurred image in the same scene into a high-resolution clear image. Due to its wide application value and theoretical value, image super-resolution reconstruction technology has become a research hotspot in the field of computer vision and image processing, and has attracted widespread attention from researchers. Compared with traditional methods, deep learning methods have shown better reconstruction effects in the field of image super-resolution reconstruction, and have gradually
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Singh, Kajol, and Manish Saxena. "A Review on Medical Image Super Resolution with Application of Deep Learning." SMART MOVES JOURNAL IJOSCIENCE 7, no. 2 (2021): 25–29. http://dx.doi.org/10.24113/ijoscience.v7i2.368.

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Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, w
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R. Mhatre, Sneha, and Jagdish W. Bakal. "A Review of Image Super Resolution using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 145–49. http://dx.doi.org/10.17762/ijritcc.v11i5s.6638.

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The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designe
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Tesis sobre el tema "Super learning"

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Lindberg, Magnus. "An Imitation-Learning based Agentplaying Super Mario." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4529.

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Context. Developing an Artificial Intelligence (AI) agent that canpredict and act in all possible situations in the dynamic environmentsthat modern video games often consists of is on beforehand nearly im-possible and would cost a lot of money and time to create by hand. Bycreating a learning AI agent that could learn by itself by studying itsenvironment with the help of Reinforcement Learning (RL) it wouldsimplify this task. Another wanted feature that often is required is AIagents with a natural acting behavior and a try to solve that problemcould be to imitating a human by using Imitation L
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Kumar, Sanjeev. "Priors and learning based methods for super-resolution." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397852.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed April 14, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 96-102).
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Pickup, Lyndsey C. "Machine learning in multi-frame image super-resolution." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:88c6968f-1e62-4d89-bd70-604bf1f41007.

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Multi-frame image super-resolution is a procedure which takes several noisy low-resolution images of the same scene, acquired under different conditions, and processes them together to synthesize one or more high-quality super-resolution images, with higher spatial frequency, and less noise and image blur than any of the original images. The inputs can take the form of medical images, surveillance footage, digital video, satellite terrain imagery, or images from many other sources. This thesis focuses on Bayesian methods for multi-frame super-resolution, which use a prior distribution over the
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Ouyang, Wei. "Deep Learning for Advanced Microscopy." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC174/document.

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Contexte: La microscopie joue un rôle important en biologie depuis plusieurs siècles, mais sa résolution a longtemps été limitée à environ 250 nm, de sorte que nombre de structures biologiques (virus, vésicules, pores nucléaires, synapses) ne pouvaient être résolues. Au cours de la dernière décennie, plusieurs méthodes de super-résolution ont été développées pour dépasser cette limite. Parmi ces techniques, les plus puissantes et les plus utilisées reposent sur la localisation de molécules uniques (microscopie à localisation de molécule unique, ou SMLM), comme PALM et STORM. En localisant préc
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Yelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.

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We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters represen
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Bégin, Isabelle. "Camera-independent learning and image quality assessment for super-resolution." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102957.

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An increasing number of applications require high-resolution images in situations where the access to the sensor and the knowledge of its specifications are limited. In this thesis, the problem of blind super-resolution is addressed, here defined as the estimation of a high-resolution image from one or more low-resolution inputs, under the condition that the degradation model parameters are unknown. The assessment of super-resolved results, using objective measures of image quality, is also addressed.<br>Learning-based methods have been successfully applied to the single frame super-resolution
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Jain, Vinit. "Deep Learning based Video Super- Resolution in Computer Generated Graphics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292687.

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Super-Resolution is a widely studied problem in the field of computer vision, where the purpose is to increase the resolution of, or super-resolve, image data. In Video Super-Resolution, maintaining temporal coherence for consecutive video frames requires fusing information from multiple frames to super-resolve one frame. Current deep learning methods perform video super-resolution, yet most of them focus on working with natural datasets. In this thesis, we use a recurrent back-projection network for working with a dataset of computer-generated graphics, with example applications including ups
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Donnot, Benjamin. "Deep learning methods for predicting flows in power grids : novel architectures and algorithms." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS060/document.

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Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour ai
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Kim, Max. "Improving Knee Cartilage Segmentation using Deep Learning-based Super-Resolution Methods." Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297900.

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Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the
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Ceccarelli, Mattia. "Optimization and applications of deep learning algorithms for super-resolution in MRI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21694/.

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The increasing amount of data produced by modern infrastructures requires instruments of analysis more and more precise, quick, and efficient. For these reasons in the last decades, Machine Learning (ML) and Deep Learning (DL) techniques saw exponential growth in publications and research from the scientific community. In this work are proposed two new frameworks for Deep Learning: Byron written in C++, for fast analysis in a parallelized CPU environment, and NumPyNet written in Python, which provides a clear and understandable interface on deep learning tailored around readability. Byron wil
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Libros sobre el tema "Super learning"

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1942-, McDonough Christopher James, ed. Commentum super Martianum. SISMEL edizioni del Galluzzo, 2006.

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Kenyon, Mary Potter. Home schooling from scratch: Simple living, super learning. Gazelle Publications, 1996.

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Super Learning. Brain Sync, 1995.

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Stockport, Focus School. Super Learning Day. Blurb, 2014.

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Super Speed Learning. Zygon International, Inc., 1994.

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Giordano, Richard J. Super-Charged Learning. Rowman & Littlefield Publishers, Incorporated, 2014.

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Super Learning for Superheroes. Independently Published, 2021.

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Super Chunky! Early Learning Boardbooks by Paradise Press (Super Chunky! Early Learning). Paradise Press, 2004.

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Super Q: Wand (Super Q). Price Stern Sloan, 1991.

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Papers, Josephine's. Preschool Super STEAM Learning Activities. Independently Published, 2022.

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Capítulos de libros sobre el tema "Super learning"

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Polley, Eric C., Sherri Rose, and Mark J. van der Laan. "Super Learning." In Targeted Learning. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_3.

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van der Laan, Mark J., and David Benkeser. "Online Super Learning." In Springer Series in Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4_18.

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Rose, Sherri, and Mark J. van der Laan. "Sequential Super Learning." In Springer Series in Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4_3.

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Du, Ping, Jinhuan Zhang, and Jun Long. "Super-Sampling by Learning-Based Super-Resolution." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05234-8_10.

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Polley, Eric C., and Mark J. van der Laan. "Super Learning for Right-Censored Data." In Targeted Learning. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_16.

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Barak-Pelleg, Dina, Daniel Berend, and Aryeh Kontorovich. "Super-Teaching in Machine Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76934-4_24.

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Li, Qiang, and Qi Wang. "Super-Resolution for Spectral Image." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68106-6_9.

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Ye, Chuyang, Yu Qin, Chenghao Liu, Yuxing Li, Xiangzhu Zeng, and Zhiwen Liu. "Super-Resolved q-Space Deep Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32248-9_65.

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Li, Qi, Shutian Liu, and Yutong Li. "Deep Learning for Super-Resolution Imaging." In Advances in Optics and Optoelectronics. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1929-0_17.

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Schneider, Jan, Dirk Börner, Peter van Rosmalen, and Marcus Specht. "The Booth: Bringing Out the Super Hero in You." In Adaptive and Adaptable Learning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45153-4_56.

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Actas de conferencias sobre el tema "Super learning"

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Chiapputo, Nicholas, and Colleen P. Bailey. "Improving single-image super-resolution through hyperparameter tuning." In Multimodal Image Exploitation and Learning 2025, edited by Sos S. Agaian, Stephen P. DelMarco, and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3054009.

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Vašinka, Dominik, Filip Juráň, Jaromír Běhal, and Miroslav Ježek. "Device-Agnostic Single-Emitter Super-Resolution using Deep Learning." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qtu3a.22.

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We demonstrate a device-agnostic deep learning approach for super-resolution single-emitter microscopy. The presented method is calibration-free and directly predicts super-resolved images independently of the optical system.
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Li, Hao, Quanwei Liu, Jianan Liu, et al. "Unpaired MRI Super Resolution with Contrastive Learning." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635265.

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Sen, Shubhojit, and Bharat Bhushan. "Image Quality Comparison Between Nvidia’s Deep Learning Super Sampling and AMD’s FidelityFX Super Resolution." In 2024 International BIT Conference (BITCON). IEEE, 2024. https://doi.org/10.1109/bitcon63716.2024.10984458.

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Li, Qian, Bifeng Cui, Pengyu Liu, and Yanxu Zhu. "Image Quality Assessment Characteristics for Super-Resolution." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957957.

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Landsborough, Jason, Stephen Harding, and Sunny Fugate. "Learning from super-mutants." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3082525.

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Karambelkar, Dattatray L., and P. J. Kulkarni. "Super-Resolution Using Manifold Learning." In 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.154.

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Changhyun Kim, Kyuha Choi, Ho-young Lee, Kyuyoung Hwang, and Jong Beom Ra. "Robust learning-based super-resolution." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651057.

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McDonald, Andrew W. E., and Ali Shokoufandeh. "Sparse Super-Regular Networks." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00286.

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Chiapputo, Nicholas, and Colleen P. Bailey. "Memory-efficient single-image super-resolution." In Big Data IV: Learning, Analytics, and Applications, edited by Fauzia Ahmad, Panos P. Markopoulos, and Bing Ouyang. SPIE, 2022. http://dx.doi.org/10.1117/12.2619142.

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Informes sobre el tema "Super learning"

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Rosencrans, David, Brandon Benton, Grant Buster, et al. Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1984839.

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Withers, Denissia. Engaging Community Food Systems through Learning Garden Programs: Oregon Food Bank's Seed to Supper Program. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.609.

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SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGH- TEMPERATURE SCENES BASED ON NEURAL NETWORK. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.

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Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting ther
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