Artículos de revistas sobre el tema "Super learning"
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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.
Kita, Koji, Michifumi Yoshioka, Katsufumi Inoue, Naru Inage y Shohei Tsunekawa. "Figure Patches Learning-based Super-Resolution". IEEJ Transactions on Electronics, Information and Systems 136, n.º 7 (2016): 929–37. http://dx.doi.org/10.1541/ieejeiss.136.929.
Yang, Wenming, Fei Zhou, Rui Zhu, Kazuhiro Fukui, Guijin Wang y Jing-Hao Xue. "Deep learning for image super-resolution". Neurocomputing 398 (julio de 2020): 291–92. http://dx.doi.org/10.1016/j.neucom.2019.09.091.
Wang, Wenjun, Chao Ren, Xiaohai He, Honggang Chen y Linbo Qing. "Video Super-Resolution via Residual Learning". IEEE Access 6 (2018): 23767–77. http://dx.doi.org/10.1109/access.2018.2829908.
Yi Tang y Yuan Yuan. "Learning From Errors in Super-Resolution". IEEE Transactions on Cybernetics 44, n.º 11 (noviembre de 2014): 2143–54. http://dx.doi.org/10.1109/tcyb.2014.2301732.
R. Mhatre, Sneha y Jagdish W. Bakal. "A Review of Image Super Resolution using Deep Learning". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 5s (17 de mayo de 2023): 145–49. http://dx.doi.org/10.17762/ijritcc.v11i5s.6638.
Singh, Kajol y Manish Saxena. "A Review on Medical Image Super Resolution with Application of Deep Learning". SMART MOVES JOURNAL IJOSCIENCE 7, n.º 2 (27 de marzo de 2021): 25–29. http://dx.doi.org/10.24113/ijoscience.v7i2.368.
He, H., K. Gao, W. Tan, L. Wang, S. N. Fatholahi, N. Chen, M. A. Chapman y J. Li. "IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (30 de mayo de 2022): 31–37. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-31-2022.
Liu, Huanyu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan y Xiaqiong Yu. "DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution". Journal of Healthcare Engineering 2021 (9 de abril de 2021): 1–9. http://dx.doi.org/10.1155/2021/5594649.
Pllana, Duli. "Combining Teaching Strategies, Learning Strategies, and Elements of Super Learning Principles". Advances in Social Sciences Research Journal 8, n.º 6 (27 de junio de 2021): 288–301. http://dx.doi.org/10.14738/assrj.86.10366.
Ordyniak, S. y S. Szeider. "Parameterized Complexity Results for Exact Bayesian Network Structure Learning". Journal of Artificial Intelligence Research 46 (5 de marzo de 2013): 263–302. http://dx.doi.org/10.1613/jair.3744.
Jian, Zhang, Xu Tengteng, Qian Jianjun, Yuchen Xiao, Heng Zhang, Hongran Li y Cunhua Li. "Single Image Self-Learning Super-Resolution with Robust Matrix Regression". AATCC Journal of Research 8, n.º 1_suppl (septiembre de 2021): 135–42. http://dx.doi.org/10.14504/ajr.8.s1.17.
Lin, Xu, Qingqing Zhang, Hongyue Wang, Chaolong Yao, Changxin Chen, Lin Cheng y Zhaoxiong Li. "A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning". Remote Sensing 14, n.º 9 (2 de mayo de 2022): 2181. http://dx.doi.org/10.3390/rs14092181.
Maftuh, Muhammad Kholidin y Dayat Hidayat. "THE EFFECT OF SUPERITEM LEARNING MODEL ON INCREASING STUDENTs LEARNING ACHIEVEMENTS". (JIML) JOURNAL OF INNOVATIVE MATHEMATICS LEARNING 1, n.º 4 (28 de noviembre de 2018): 367. http://dx.doi.org/10.22460/jiml.v1i4.p367-373.
Davies, Molly Margaret y Mark J. van der Laan. "Optimal Spatial Prediction Using Ensemble Machine Learning". International Journal of Biostatistics 12, n.º 1 (1 de mayo de 2016): 179–201. http://dx.doi.org/10.1515/ijb-2014-0060.
He, Yifan, Wei Cao, Xiaofeng Du y Changlin Chen. "Internal Learning for Image Super-Resolution by Adaptive Feature Transform". Symmetry 12, n.º 10 (14 de octubre de 2020): 1686. http://dx.doi.org/10.3390/sym12101686.
Li, Xiaoyan, Lefei Zhang y Jane You. "Domain Transfer Learning for Hyperspectral Image Super-Resolution". Remote Sensing 11, n.º 6 (22 de marzo de 2019): 694. http://dx.doi.org/10.3390/rs11060694.
Leli, Vito M., Saeed Osat, Timur Tlyachev, Dmitry V. Dylov y Jacob D. Biamonte. "Deep learning super-diffusion in multiplex networks". Journal of Physics: Complexity 2, n.º 3 (10 de junio de 2021): 035011. http://dx.doi.org/10.1088/2632-072x/abe6e9.
Heo, Bo-Young y Byung Cheol Song. "Learning-based Super-resolution for Text Images". Journal of the Institute of Electronics and Information Engineers 52, n.º 4 (25 de abril de 2015): 175–83. http://dx.doi.org/10.5573/ieie.2015.52.4.175.
Singh, Nisha y Myna A.N. "Image Super-Resolution Using Deep Learning Technique". International Journal of Computer Sciences and Engineering 6, n.º 7 (31 de julio de 2018): 150–55. http://dx.doi.org/10.26438/ijcse/v6i7.150155.
Chae, Byungjoo, Jinsun Park, Tae-Hyun Kim y Donghyeon Cho. "Online Learning for Reference-Based Super-Resolution". Electronics 11, n.º 7 (28 de marzo de 2022): 1064. http://dx.doi.org/10.3390/electronics11071064.
Qin, Yu, Yuxing Li, Zhizheng Zhuo, Zhiwen Liu, Yaou Liu y Chuyang Ye. "Multimodal super-resolved q-space deep learning". Medical Image Analysis 71 (julio de 2021): 102085. http://dx.doi.org/10.1016/j.media.2021.102085.
Chen, Chaofeng, Dihong Gong, Hao Wang, Zhifeng Li y Kwan-Yee K. Wong. "Learning Spatial Attention for Face Super-Resolution". IEEE Transactions on Image Processing 30 (2021): 1219–31. http://dx.doi.org/10.1109/tip.2020.3043093.
Kawulok, Michal, Pawel Benecki, Szymon Piechaczek, Krzysztof Hrynczenko, Daniel Kostrzewa y Jakub Nalepa. "Deep Learning for Multiple-Image Super-Resolution". IEEE Geoscience and Remote Sensing Letters 17, n.º 6 (junio de 2020): 1062–66. http://dx.doi.org/10.1109/lgrs.2019.2940483.
Jiang, Zhuqing, Honghui Zhu, Yue Lu, Guodong Ju y Aidong Men. "Lightweight Super-Resolution Using Deep Neural Learning". IEEE Transactions on Broadcasting 66, n.º 4 (diciembre de 2020): 814–23. http://dx.doi.org/10.1109/tbc.2020.2977513.
Kumar, Neeraj y Amit Sethi. "Fast Learning-Based Single Image Super-Resolution". IEEE Transactions on Multimedia 18, n.º 8 (agosto de 2016): 1504–15. http://dx.doi.org/10.1109/tmm.2016.2571625.
Huang, Weiqin, Xiaorui Li, Yikai Gu, Xiaofu Du y Xiancheng Zhu. "Learning Enriched Features for Image Super Resolution". IEEE Access 10 (2022): 113583–97. http://dx.doi.org/10.1109/access.2022.3216672.
Tang, Yi, Pingkun Yan, Yuan Yuan y Xuelong Li. "Single-image super-resolution via local learning". International Journal of Machine Learning and Cybernetics 2, n.º 1 (12 de febrero de 2011): 15–23. http://dx.doi.org/10.1007/s13042-011-0011-6.
Shamsolmoali, Pourya, Abdul Hamid Sadka, Huiyu Zhou y Wankou Yang. "Advanced deep learning for image super-resolution". Signal Processing: Image Communication 82 (marzo de 2020): 115732. http://dx.doi.org/10.1016/j.image.2019.115732.
Naimi, Ashley I. y Laura B. Balzer. "Stacked generalization: an introduction to super learning". European Journal of Epidemiology 33, n.º 5 (10 de abril de 2018): 459–64. http://dx.doi.org/10.1007/s10654-018-0390-z.
Chaudhari, Akshay S., Zhongnan Fang, Feliks Kogan, Jeff Wood, Kathryn J. Stevens, Eric K. Gibbons, Jin Hyung Lee, Garry E. Gold y Brian A. Hargreaves. "Super‐resolution musculoskeletal MRI using deep learning". Magnetic Resonance in Medicine 80, n.º 5 (26 de marzo de 2018): 2139–54. http://dx.doi.org/10.1002/mrm.27178.
Hasan, Zahraa. "Deep Learning for Super Resolution and Applications". Galoitica: Journal of Mathematical Structures and Applications 8, n.º 2 (2023): 34–42. http://dx.doi.org/10.54216/gjmsa.080204.
Yang, Guangtong, Chen Li, Yudong Yao, Ge Wang y Yueyang Teng. "Quasi-supervised learning for super-resolution PET". Computerized Medical Imaging and Graphics 113 (abril de 2024): 102351. http://dx.doi.org/10.1016/j.compmedimag.2024.102351.
Geiss, Andrew, Sam J. Silva y Joseph C. Hardin. "Downscaling atmospheric chemistry simulations with physically consistent deep learning". Geoscientific Model Development 15, n.º 17 (5 de septiembre de 2022): 6677–94. http://dx.doi.org/10.5194/gmd-15-6677-2022.
Wu, Haozhe. "Super-Resolution of Lightweight Images Based on Deep Learning". Highlights in Science, Engineering and Technology 81 (26 de enero de 2024): 456–60. http://dx.doi.org/10.54097/f8y87181.
Dewi, Ratna Kumala. "INNOVATION OF BIOCHEMISTRY LEARNING IN WELCOMING THE SUPER SMART SOCIETY 5.0 ERA". INSECTA: Integrative Science Education and Teaching Activity Journal 2, n.º 2 (29 de noviembre de 2021): 197–208. http://dx.doi.org/10.21154/insecta.v2i2.3507.
Liu, Ding, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang y Thomas S. Huang. "Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach". IEEE Transactions on Image Processing 27, n.º 7 (julio de 2018): 3432–45. http://dx.doi.org/10.1109/tip.2018.2820807.
Yue, Bo, Shuang Wang, Xuefeng Liang y Licheng Jiao. "An external learning assisted self-examples learning for image super-resolution". Neurocomputing 312 (octubre de 2018): 107–19. http://dx.doi.org/10.1016/j.neucom.2018.05.076.
Yu, Li, Yunpeng Ma, Song Hong y Ke Chen. "Reivew of Light Field Image Super-Resolution". Electronics 11, n.º 12 (17 de junio de 2022): 1904. http://dx.doi.org/10.3390/electronics11121904.
Masihu, Junardin Muhamad y Edi Masihu. "Application of Super Item Learning Model in Improving Learning Outcomes of Photosynthesis Concept in Class VIII of SMP Al-Wathan Ambon". PEDAGOGIC: Indonesian Journal of Science Education and Technology 1, n.º 2 (1 de diciembre de 2022): 72–86. http://dx.doi.org/10.54373/ijset.v2i1.55.
Bhujade, Rakesh Kumar y Stuti Asthana. "An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution". International Journal of Emerging Technology and Advanced Engineering 12, n.º 11 (1 de noviembre de 2022): 153–58. http://dx.doi.org/10.46338/ijetae1122_16.