Artigos de revistas sobre o tema "Super learning"
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Long, Jun, Jinhuan Zhang e 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.
Texto completo da fonteDu, Ping, Jinhuan Zhang e 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.
Texto completo da fonteHaris, Muhammad, M. Rahmat Widyanto e Hajime Nobuhara. "Inception learning super-resolution". Applied Optics 56, n.º 22 (21 de julho de 2017): 6043. http://dx.doi.org/10.1364/ao.56.006043.
Texto completo da fonteGURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP". Herald of Khmelnytskyi National University. Technical sciences 307, n.º 2 (2 de maio de 2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.
Texto completo da fonteAitken, Michael R. F., Mark J. W. Larkin e Anthony Dickinson. "Super-learning of Causal Judgements". Quarterly Journal of Experimental Psychology B 53, n.º 1 (1 de fevereiro de 2000): 59–81. http://dx.doi.org/10.1080/027249900392995.
Texto completo da fonteLim, Alane. "Machine learning method puts the “super” in super-resolution spectroscopy". Scilight 2021, n.º 49 (3 de dezembro de 2021): 491108. http://dx.doi.org/10.1063/10.0009031.
Texto completo da fonteHan, Tong, Li Zhao e 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 janeiro de 2023): 58–65. http://dx.doi.org/10.2478/ijanmc-2023-0046.
Texto completo da fonteJiang, Jingyu, Li Zhao e 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 janeiro de 2022): 1–21. http://dx.doi.org/10.2478/ijanmc-2022-0001.
Texto completo da fonteDemontis, Ambra, Marco Melis, Battista Biggio, Giorgio Fumera e Fabio Roli. "Super-Sparse Learning in Similarity Spaces". IEEE Computational Intelligence Magazine 11, n.º 4 (novembro de 2016): 36–45. http://dx.doi.org/10.1109/mci.2016.2601702.
Texto completo da fonteStrack, Rita. "Deep learning advances super-resolution imaging". Nature Methods 15, n.º 6 (31 de maio de 2018): 403. http://dx.doi.org/10.1038/s41592-018-0028-9.
Texto completo da fonteKita, Koji, Michifumi Yoshioka, Katsufumi Inoue, Naru Inage e 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.
Texto completo da fonteYang, Wenming, Fei Zhou, Rui Zhu, Kazuhiro Fukui, Guijin Wang e Jing-Hao Xue. "Deep learning for image super-resolution". Neurocomputing 398 (julho de 2020): 291–92. http://dx.doi.org/10.1016/j.neucom.2019.09.091.
Texto completo da fonteWang, Wenjun, Chao Ren, Xiaohai He, Honggang Chen e Linbo Qing. "Video Super-Resolution via Residual Learning". IEEE Access 6 (2018): 23767–77. http://dx.doi.org/10.1109/access.2018.2829908.
Texto completo da fonteYi Tang e Yuan Yuan. "Learning From Errors in Super-Resolution". IEEE Transactions on Cybernetics 44, n.º 11 (novembro de 2014): 2143–54. http://dx.doi.org/10.1109/tcyb.2014.2301732.
Texto completo da fonteR. Mhatre, Sneha, e 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 maio de 2023): 145–49. http://dx.doi.org/10.17762/ijritcc.v11i5s.6638.
Texto completo da fonteSingh, Kajol, e Manish Saxena. "A Review on Medical Image Super Resolution with Application of Deep Learning". SMART MOVES JOURNAL IJOSCIENCE 7, n.º 2 (27 de março de 2021): 25–29. http://dx.doi.org/10.24113/ijoscience.v7i2.368.
Texto completo da fonteHe, H., K. Gao, W. Tan, L. Wang, S. N. Fatholahi, N. Chen, M. A. Chapman e 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 maio de 2022): 31–37. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-31-2022.
Texto completo da fonteLiu, Huanyu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan e 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.
Texto completo da fontePllana, Duli. "Combining Teaching Strategies, Learning Strategies, and Elements of Super Learning Principles". Advances in Social Sciences Research Journal 8, n.º 6 (27 de junho de 2021): 288–301. http://dx.doi.org/10.14738/assrj.86.10366.
Texto completo da fonteOrdyniak, S., e S. Szeider. "Parameterized Complexity Results for Exact Bayesian Network Structure Learning". Journal of Artificial Intelligence Research 46 (5 de março de 2013): 263–302. http://dx.doi.org/10.1613/jair.3744.
Texto completo da fonteJian, Zhang, Xu Tengteng, Qian Jianjun, Yuchen Xiao, Heng Zhang, Hongran Li e Cunhua Li. "Single Image Self-Learning Super-Resolution with Robust Matrix Regression". AATCC Journal of Research 8, n.º 1_suppl (setembro de 2021): 135–42. http://dx.doi.org/10.14504/ajr.8.s1.17.
Texto completo da fonteLin, Xu, Qingqing Zhang, Hongyue Wang, Chaolong Yao, Changxin Chen, Lin Cheng e Zhaoxiong Li. "A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning". Remote Sensing 14, n.º 9 (2 de maio de 2022): 2181. http://dx.doi.org/10.3390/rs14092181.
Texto completo da fonteMaftuh, Muhammad Kholidin, e 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 novembro de 2018): 367. http://dx.doi.org/10.22460/jiml.v1i4.p367-373.
Texto completo da fonteDavies, Molly Margaret, e Mark J. van der Laan. "Optimal Spatial Prediction Using Ensemble Machine Learning". International Journal of Biostatistics 12, n.º 1 (1 de maio de 2016): 179–201. http://dx.doi.org/10.1515/ijb-2014-0060.
Texto completo da fonteHe, Yifan, Wei Cao, Xiaofeng Du e Changlin Chen. "Internal Learning for Image Super-Resolution by Adaptive Feature Transform". Symmetry 12, n.º 10 (14 de outubro de 2020): 1686. http://dx.doi.org/10.3390/sym12101686.
Texto completo da fonteLi, Xiaoyan, Lefei Zhang e Jane You. "Domain Transfer Learning for Hyperspectral Image Super-Resolution". Remote Sensing 11, n.º 6 (22 de março de 2019): 694. http://dx.doi.org/10.3390/rs11060694.
Texto completo da fonteLeli, Vito M., Saeed Osat, Timur Tlyachev, Dmitry V. Dylov e Jacob D. Biamonte. "Deep learning super-diffusion in multiplex networks". Journal of Physics: Complexity 2, n.º 3 (10 de junho de 2021): 035011. http://dx.doi.org/10.1088/2632-072x/abe6e9.
Texto completo da fonteHeo, Bo-Young, e 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.
Texto completo da fonteSingh, Nisha, e Myna A.N. "Image Super-Resolution Using Deep Learning Technique". International Journal of Computer Sciences and Engineering 6, n.º 7 (31 de julho de 2018): 150–55. http://dx.doi.org/10.26438/ijcse/v6i7.150155.
Texto completo da fonteChae, Byungjoo, Jinsun Park, Tae-Hyun Kim e Donghyeon Cho. "Online Learning for Reference-Based Super-Resolution". Electronics 11, n.º 7 (28 de março de 2022): 1064. http://dx.doi.org/10.3390/electronics11071064.
Texto completo da fonteQin, Yu, Yuxing Li, Zhizheng Zhuo, Zhiwen Liu, Yaou Liu e Chuyang Ye. "Multimodal super-resolved q-space deep learning". Medical Image Analysis 71 (julho de 2021): 102085. http://dx.doi.org/10.1016/j.media.2021.102085.
Texto completo da fonteChen, Chaofeng, Dihong Gong, Hao Wang, Zhifeng Li e 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.
Texto completo da fonteKawulok, Michal, Pawel Benecki, Szymon Piechaczek, Krzysztof Hrynczenko, Daniel Kostrzewa e Jakub Nalepa. "Deep Learning for Multiple-Image Super-Resolution". IEEE Geoscience and Remote Sensing Letters 17, n.º 6 (junho de 2020): 1062–66. http://dx.doi.org/10.1109/lgrs.2019.2940483.
Texto completo da fonteJiang, Zhuqing, Honghui Zhu, Yue Lu, Guodong Ju e Aidong Men. "Lightweight Super-Resolution Using Deep Neural Learning". IEEE Transactions on Broadcasting 66, n.º 4 (dezembro de 2020): 814–23. http://dx.doi.org/10.1109/tbc.2020.2977513.
Texto completo da fonteKumar, Neeraj, e 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.
Texto completo da fonteHuang, Weiqin, Xiaorui Li, Yikai Gu, Xiaofu Du e Xiancheng Zhu. "Learning Enriched Features for Image Super Resolution". IEEE Access 10 (2022): 113583–97. http://dx.doi.org/10.1109/access.2022.3216672.
Texto completo da fonteTang, Yi, Pingkun Yan, Yuan Yuan e Xuelong Li. "Single-image super-resolution via local learning". International Journal of Machine Learning and Cybernetics 2, n.º 1 (12 de fevereiro de 2011): 15–23. http://dx.doi.org/10.1007/s13042-011-0011-6.
Texto completo da fonteShamsolmoali, Pourya, Abdul Hamid Sadka, Huiyu Zhou e Wankou Yang. "Advanced deep learning for image super-resolution". Signal Processing: Image Communication 82 (março de 2020): 115732. http://dx.doi.org/10.1016/j.image.2019.115732.
Texto completo da fonteNaimi, Ashley I., e 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.
Texto completo da fonteChaudhari, Akshay S., Zhongnan Fang, Feliks Kogan, Jeff Wood, Kathryn J. Stevens, Eric K. Gibbons, Jin Hyung Lee, Garry E. Gold e Brian A. Hargreaves. "Super‐resolution musculoskeletal MRI using deep learning". Magnetic Resonance in Medicine 80, n.º 5 (26 de março de 2018): 2139–54. http://dx.doi.org/10.1002/mrm.27178.
Texto completo da fonteHasan, 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.
Texto completo da fonteYang, Guangtong, Chen Li, Yudong Yao, Ge Wang e 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.
Texto completo da fonteGeiss, Andrew, Sam J. Silva e Joseph C. Hardin. "Downscaling atmospheric chemistry simulations with physically consistent deep learning". Geoscientific Model Development 15, n.º 17 (5 de setembro de 2022): 6677–94. http://dx.doi.org/10.5194/gmd-15-6677-2022.
Texto completo da fonteWu, Haozhe. "Super-Resolution of Lightweight Images Based on Deep Learning". Highlights in Science, Engineering and Technology 81 (26 de janeiro de 2024): 456–60. http://dx.doi.org/10.54097/f8y87181.
Texto completo da fonteDewi, 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 novembro de 2021): 197–208. http://dx.doi.org/10.21154/insecta.v2i2.3507.
Texto completo da fonteLiu, Ding, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang e Thomas S. Huang. "Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach". IEEE Transactions on Image Processing 27, n.º 7 (julho de 2018): 3432–45. http://dx.doi.org/10.1109/tip.2018.2820807.
Texto completo da fonteYue, Bo, Shuang Wang, Xuefeng Liang e Licheng Jiao. "An external learning assisted self-examples learning for image super-resolution". Neurocomputing 312 (outubro de 2018): 107–19. http://dx.doi.org/10.1016/j.neucom.2018.05.076.
Texto completo da fonteYu, Li, Yunpeng Ma, Song Hong e Ke Chen. "Reivew of Light Field Image Super-Resolution". Electronics 11, n.º 12 (17 de junho de 2022): 1904. http://dx.doi.org/10.3390/electronics11121904.
Texto completo da fonteMasihu, Junardin Muhamad, e 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 dezembro de 2022): 72–86. http://dx.doi.org/10.54373/ijset.v2i1.55.
Texto completo da fonteBhujade, Rakesh Kumar, e 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 novembro de 2022): 153–58. http://dx.doi.org/10.46338/ijetae1122_16.
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