Literatura académica sobre el tema "Super learning"
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Artículos de revistas sobre el tema "Super learning":
Long, Jun, Jinhuan Zhang y Ping Du. "Super-sampling by learning-based super-resolution". International Journal of Computational Science and Engineering 1, n.º 1 (2019): 1. http://dx.doi.org/10.1504/ijcse.2019.10020177.
Du, Ping, Jinhuan Zhang y Jun Long. "Super-sampling by learning-based super-resolution". International Journal of Computational Science and Engineering 21, n.º 2 (2020): 249. http://dx.doi.org/10.1504/ijcse.2020.105731.
Haris, Muhammad, M. Rahmat Widyanto y Hajime Nobuhara. "Inception learning super-resolution". Applied Optics 56, n.º 22 (21 de julio de 2017): 6043. http://dx.doi.org/10.1364/ao.56.006043.
GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP". Herald of Khmelnytskyi National University. Technical sciences 307, n.º 2 (2 de mayo de 2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.
Aitken, Michael R. F., Mark J. W. Larkin y Anthony Dickinson. "Super-learning of Causal Judgements". Quarterly Journal of Experimental Psychology B 53, n.º 1 (1 de febrero de 2000): 59–81. http://dx.doi.org/10.1080/027249900392995.
Lim, Alane. "Machine learning method puts the “super” in super-resolution spectroscopy". Scilight 2021, n.º 49 (3 de diciembre de 2021): 491108. http://dx.doi.org/10.1063/10.0009031.
Han, Tong, Li Zhao y Chuang Wang. "Research on Super-resolution Image Based on Deep Learning". International Journal of Advanced Network, Monitoring and Controls 8, n.º 1 (1 de enero de 2023): 58–65. http://dx.doi.org/10.2478/ijanmc-2023-0046.
Jiang, Jingyu, Li Zhao y Yan Jiao. "Research on Image Super-resolution Reconstruction Based on Deep Learning". International Journal of Advanced Network, Monitoring and Controls 7, n.º 1 (1 de enero de 2022): 1–21. http://dx.doi.org/10.2478/ijanmc-2022-0001.
Demontis, Ambra, Marco Melis, Battista Biggio, Giorgio Fumera y Fabio Roli. "Super-Sparse Learning in Similarity Spaces". IEEE Computational Intelligence Magazine 11, n.º 4 (noviembre de 2016): 36–45. http://dx.doi.org/10.1109/mci.2016.2601702.
Strack, Rita. "Deep learning advances super-resolution imaging". Nature Methods 15, n.º 6 (31 de mayo de 2018): 403. http://dx.doi.org/10.1038/s41592-018-0028-9.
Tesis sobre el tema "Super learning":
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.
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.
Title from first page of PDF file (viewed April 14, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 96-102).
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.
Ouyang, Wei. "Deep Learning for Advanced Microscopy". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC174/document.
Background: Microscopy plays an important role in biology since several centuries, but its resolution has long been limited to ~250nm due to diffraction, leaving many important biological structures (e.g. viruses, vesicles, nuclear pores, synapses) unresolved. Over the last decade, several super-resolution methods have been developed that break this limit. Among the most powerful and popular super-resolution techniques are those based on single molecular localization (single molecule localization microscopy, or SMLM) such as PALM and STORM. By precisely localizing positions of isolated fluorescent molecules in thousands or more sequentially acquired diffraction limited images, SMLM can achieve resolutions of 20-50 nm or better. However, SMLM is inherently slow due to the necessity to accumulate enough localizations to achieve high resolution sampling of the fluorescent structures. The drawback in acquisition speed (typically ~30 minutes per super-resolution image) makes it difficult to use SMLM in high-throughput and live cell imaging. Many methods have been proposed to address this issue, mostly by improving the localization algorithms to localize overlapping spots, but most of them compromise spatial resolution and cause artifacts.Methods and results: In this work, we applied deep learning based image-to-image translation framework for improving imaging speed and quality by restoring information from rapidly acquired low quality SMLM images. By utilizing recent advances in deep learning including the U-net and Generative Adversarial Networks, we developed our method Artificial Neural Network Accelerated PALM (ANNA-PALM) which is capable of learning structural information from training images and using the trained model to accelerate SMLM imaging by tens to hundreds folds. With experimentally acquired images of different cellular structures (microtubules, nuclear pores and mitochondria), we demonstrated that deep learning can efficiently capture the structural information from less than 10 training samples and reconstruct high quality super-resolution images from sparse, noisy SMLM images obtained with much shorter acquisitions than usual for SMLM. We also showed that ANNA-PALM is robust to possible variations between training and testing conditions, due either to changes in the biological structure or to changes in imaging parameters. Furthermore, we take advantage of the acceleration provided by ANNA-PALM to perform high throughput experiments, showing acquisition of ~1000 cells at high resolution in ~3 hours. Additionally, we designed a tool to estimate and reduce possible artifacts is designed by measuring the consistency between the reconstructed image and the experimental wide-field image. Our method enables faster and gentler imaging which can be applied to high-throughput, and provides a novel avenue towards live cell high resolution imaging. Deep learning methods rely on training data and their performance can be improved even further with more training data. One cheap way to obtain more training data is through data sharing within the microscopy community. However, it often difficult to exchange or share localization microscopy data, because localization tables alone are typically several gigabytes in size, and there is no dedicated platform for localization microscopy data which provide features such as rendering, visualization and filtering. To address these issues, we developed a file format that can losslessly compress localization tables into smaller files, alongside with a web platform called ShareLoc (https://shareloc.xyz) that allows to easily visualize and share 2D or 3D SMLM data. We believe that this platform can greatly improve the performance of deep learning models, accelerate tool development, facilitate data re-analysis and further promote reproducible research and open science
Yelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering". Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.
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.
Learning-based methods have been successfully applied to the single frame super-resolution problem in the past. However, sensor characteristics such as the Point Spread Function (PSF) must often be known. In this thesis, a learning-based approach is adapted to work without the knowledge of the PSF thus making the framework camera-independent. However, the goal is not only to super-resolve an image under this limitation, but also to provide an estimation of the best PSF, consisting of a theoretical model with one unknown parameter.
In particular, two extensions of a method performing belief propagation on a Markov Random Field are presented. The first method finds the best PSF parameter by performing a search for the minimum mean distance between training examples and patches from the input image. In the second method, the best PSF parameter and the super-resolution result are found simultaneously by providing a range of possible PSF parameters from which the super-resolution algorithm will choose from. For both methods, a first estimate is obtained through blind deconvolution and an uncertainty is calculated in order to restrict the search.
Both camera-independent adaptations are compared and analyzed in various experiments, and a set of key parameters are varied to determine their effect on both the super-resolution and the PSF parameter recovery results. The use of quality measures is thus essential to quantify the improvements obtained from the algorithms. A set of measures is chosen that represents different aspects of image quality: the signal fidelity, the perceptual quality and the localization and scale of the edges.
Results indicate that both methods improve similarity to the ground truth and can in general refine the initial PSF parameter estimate towards the true value. Furthermore, the similarity measure results show that the chosen learning-based framework consistently improves a measure designed for perceptual quality.
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.
Superupplösning är ett allmänt studerat problem inom datorsyn, där syftet är att öka upplösningen på eller superupplösningsbilddata. I Video Super- Resolution kräver upprätthållande av tidsmässig koherens för på varandra följande videobilder sammanslagning av information från flera bilder för att superlösa en bildruta. Nuvarande djupinlärningsmetoder utför superupplösning i video, men de flesta av dem fokuserar på att arbeta med naturliga datamängder. I denna avhandling använder vi ett återkommande bakprojektionsnätverk för att arbeta med en datamängd av datorgenererad grafik, med exempelvis applikationer inklusive upsampling av film med låg upplösning för spelindustrin. Datauppsättningen kommer från en mängd olika spelinnehåll, återgivna i (3840 x 2160) upplösning. Målet med nätverket är att producera en uppskalad version av en ram med låg upplösning genom att lära sig en ingångskombination av en lågupplösningsram, en sekvens av intilliggande ramar och det optiska flödet mellan varje intilliggande ram och referensramen. Under grundinställningen tränar vi modellen för att utföra 2x uppsampling från (1920 x 1080) till (3840 x 2160) upplösning. Jämfört med den bicubiska interpoleringsmetoden uppnådde vår modell bättre resultat med en marginal på 2 dB för Peak Signal-to-Noise Ratio (PSNR), 0,015 för Structural Similarity Index Measure (SSIM) och 9.3 för Video Multimethod Assessment Fusion (VMAF) mätvärde. Dessutom demonstrerar vi vidare känsligheten i neuronal nätverk för förändringar i bildkomprimeringskvaliteten och ineffektiviteten hos distorsionsmätvärden för att fånga de perceptuella detaljerna exakt.
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.
This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid
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.
Segmentering av knäbrosket är ett viktigt steg för planering inför operationer och tillverkning av patientspecifika proteser. Idag segmenterar man knäbrosk med hjälp av MR-bilder tagna med en 3D-sekvens som både tidskrävande och rörelsekänsligt, vilket kan vara obehagligt för patienten. I samband med 3D-bildtagningar brukar även thick slice 2D-sekvenser tas för diagnostiska skäl, däremot är de inte anpassade för segmentering på grund av för tjocka skivor. På senare tid har djupinlärningsbaserade superupplösningsmetoder uppbyggda av så kallade feed-forwardmodeller visat sig vara väldigt framgångsrikt när det applicerats på verkliga- och medicinska bilder. Syftet med den här rapporten är att testa hur väl superupplösta thick slice 2D-sekvensbildtagningar fungerar för segmentering av ledbrosket i knät. De undersökta tillvägagångssätten är superupplösning av enkel- och flerkontrastbilder, där kontrasten är antingen baserade på 2D-sekvensen, 3D-sekvensen eller både och. Resultaten påvisar en liten förbättring av segmenteringnoggrannhet vid segmentering av enkelkontrastbilderna över baslinjen linjär interpolering. Däremot var det inte någon märkvärdig förbättring i superupplösning av flerkontrastbilderna. Även om superupplösning av flerkontrastmetoden inte gav någon märkbar förbättring segmenteringsresultaten så finns det fortfarande outforskade områden som inte tagits upp i det här arbetet som potentiellt skulle kunna utforskas i framtida arbeten.
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/.
Libros sobre el tema "Super learning":
McEwan, Elaine K. Managing attention & learning disorders: Super survival strategies. Wheaton, Ill: Harold Shaw Publishers, 1997.
Hunt, Richard William. Commentum super Martianum. Tavarnuzze (Firenze): SISMEL edizioni del Galluzzo, 2006.
Kenyon, Mary Potter. Home schooling from scratch: Simple living, super learning. Bridgman, MI: Gazelle Publications, 1996.
Hartman, Amanda. Super powers: Reading with print strategies and sight word power. Portsmouth, NH: Heinemann, 2015.
Jensen, Eric. Super teaching: Over 1,000 practical teaching strategies. 3a ed. San Diego,CA: Brain Store, 1998.
Hernandez, Lisa. The amazing 7-day, super-simple, scripted guide to teaching or learning decimals. West Hollywood, CA: Nova Press, 2014.
Allen, Margaret. The critters, colors, & clouds learning center book: Literacy-based activities for super science fun. Spring Branch, TX: Dr. Maggie Allen's Learning Express, 2004.
Balli, Chris. English language learning with super support: A workbook for ESL/ESOL/EFL/ELL students : Beginners. [United States]: Griselda Califa, LLC, 2016.
Super Learning. Brain Sync, 1995.
Super Speed Learning. Zygon International, Inc., 1994.
Capítulos de libros sobre el tema "Super learning":
Polley, Eric C., Sherri Rose y Mark J. van der Laan. "Super Learning". En Targeted Learning, 43–66. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_3.
van der Laan, Mark J. y David Benkeser. "Online Super Learning". En Springer Series in Statistics, 303–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4_18.
Rose, Sherri y Mark J. van der Laan. "Sequential Super Learning". En Springer Series in Statistics, 27–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4_3.
Du, Ping, Jinhuan Zhang y Jun Long. "Super-Sampling by Learning-Based Super-Resolution". En Algorithms and Architectures for Parallel Processing, 76–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05234-8_10.
Polley, Eric C. y Mark J. van der Laan. "Super Learning for Right-Censored Data". En Targeted Learning, 249–58. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_16.
Ye, Chuyang, Yu Qin, Chenghao Liu, Yuxing Li, Xiangzhu Zeng y Zhiwen Liu. "Super-Resolved q-Space Deep Learning". En Lecture Notes in Computer Science, 582–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32248-9_65.
Sui, Yao, Onur Afacan, Ali Gholipour y Simon K. Warfield. "MRI Super-Resolution Through Generative Degradation Learning". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 430–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87231-1_42.
Huang, Yi, Weixin Bian, Biao Jie, Zhiqiang Zhu y Wenhu Li. "Image Super-Resolution via Deep Dictionary Learning". En Lecture Notes in Computer Science, 21–32. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46314-3_2.
Schneider, Jan, Dirk Börner, Peter van Rosmalen y Marcus Specht. "The Booth: Bringing Out the Super Hero in You". En Adaptive and Adaptable Learning, 529–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45153-4_56.
Huang, Yongsong, Qingzhong Wang y Shinichiro Omachi. "Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN". En Machine Learning in Medical Imaging, 43–52. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_5.
Actas de conferencias sobre el tema "Super learning":
Landsborough, Jason, Stephen Harding y Sunny Fugate. "Learning from super-mutants". En GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3082525.
Karambelkar, Dattatray L. y P. J. Kulkarni. "Super-Resolution Using Manifold Learning". En 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.154.
Changhyun Kim, Kyuha Choi, Ho-young Lee, Kyuyoung Hwang y Jong Beom Ra. "Robust learning-based super-resolution". En 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651057.
McDonald, Andrew W. E. y Ali Shokoufandeh. "Sparse Super-Regular Networks". En 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00286.
Chiapputo, Nicholas y Colleen P. Bailey. "Memory-efficient single-image super-resolution". En Big Data IV: Learning, Analytics, and Applications, editado por Fauzia Ahmad, Panos P. Markopoulos y Bing Ouyang. SPIE, 2022. http://dx.doi.org/10.1117/12.2619142.
Suzuki, Kaiyu, Yasushi Kambayashi y Tomofumi Matsuzawa. "CrossSiam: k-Fold Cross Representation Learning". En Special Session on Super Distributed and Multi-agent Intelligent Systems. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010972500003116.
He, Huayong, Ze Li, Jianhong Li y Xiaocui Peng. "Image Super-Resolution through Pyramid Learning". En 2012 4th International Conference on Digital Home (ICDH). IEEE, 2012. http://dx.doi.org/10.1109/icdh.2012.76.
Pickup, L. C., S. J. Roberts y A. Zisserman. "Optimizing and Learning for Super-resolution". En British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.46.
Lu, Xiaoqiang, Haoliang Yuan, Yuan Yuan, Pingkun Yan, Luoqing Li y Xuelong Li. "Local learning-based image super-resolution". En 2011 IEEE 13th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2011. http://dx.doi.org/10.1109/mmsp.2011.6093843.
Li, Juan, Jin Wu, Shen Yang y Jin Liu. "Dictionary learning for image super-resolution". En 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6896189.
Informes sobre el tema "Super learning":
Rosencrans, David, Brandon Benton, Grant Buster, Andrew Glaws, Ryan King, Julie Lundquist, Jianyu Gu y Galen Maclaurin. 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), junio de 2023. http://dx.doi.org/10.2172/1984839.
Withers, Denissia. Engaging Community Food Systems through Learning Garden Programs: Oregon Food Bank's Seed to Supper Program. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.609.
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, junio de 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.