Artículos de revistas sobre el tema "Learning with Limited Data"
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Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright y Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting". Proceedings on Privacy Enhancing Technologies 2021, n.º 2 (29 de enero de 2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.
Texto completoTriantafillou, Sofia y Greg Cooper. "Learning Adjustment Sets from Observational and Limited Experimental Data". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de mayo de 2021): 9940–48. http://dx.doi.org/10.1609/aaai.v35i11.17194.
Texto completoZhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court y Jinzhong Yang. "Training deep‐learning segmentation models from severely limited data". Medical Physics 48, n.º 4 (19 de febrero de 2021): 1697–706. http://dx.doi.org/10.1002/mp.14728.
Texto completoKim, Minjeong, Yujung Gil, Yuyeon Kim y Jihie Kim. "Deep-Learning-Based Scalp Image Analysis Using Limited Data". Electronics 12, n.º 6 (14 de marzo de 2023): 1380. http://dx.doi.org/10.3390/electronics12061380.
Texto completoChen, Jiaao, Derek Tam, Colin Raffel, Mohit Bansal y Diyi Yang. "An Empirical Survey of Data Augmentation for Limited Data Learning in NLP". Transactions of the Association for Computational Linguistics 11 (2023): 191–211. http://dx.doi.org/10.1162/tacl_a_00542.
Texto completoHan, Te, Chao Liu, Rui Wu y Dongxiang Jiang. "Deep transfer learning with limited data for machinery fault diagnosis". Applied Soft Computing 103 (mayo de 2021): 107150. http://dx.doi.org/10.1016/j.asoc.2021.107150.
Texto completoJi, Xuefei, Jue Wang, Ye Li, Qiang Sun, Shi Jin y Tony Q. S. Quek. "Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model". IEEE Communications Letters 24, n.º 10 (octubre de 2020): 2191–95. http://dx.doi.org/10.1109/lcomm.2020.3004877.
Texto completoForestier, Germain y Cédric Wemmert. "Semi-supervised learning using multiple clusterings with limited labeled data". Information Sciences 361-362 (septiembre de 2016): 48–65. http://dx.doi.org/10.1016/j.ins.2016.04.040.
Texto completoWen, Jiahui y Zhiying Wang. "Learning general model for activity recognition with limited labelled data". Expert Systems with Applications 74 (mayo de 2017): 19–28. http://dx.doi.org/10.1016/j.eswa.2017.01.002.
Texto completoZhang, Ansi, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong y Jianjun Hu. "Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning". IEEE Access 7 (2019): 110895–904. http://dx.doi.org/10.1109/access.2019.2934233.
Texto completoTulsyan, Aditya, Christopher Garvin y Cenk Undey. "Machine-learning for biopharmaceutical batch process monitoring with limited data". IFAC-PapersOnLine 51, n.º 18 (2018): 126–31. http://dx.doi.org/10.1016/j.ifacol.2018.09.287.
Texto completoPrasanna Das, Hari, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoffrey Tison, Alberto Sangiovanni-Vincentelli y Costas J. Spanos. "Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 11792–800. http://dx.doi.org/10.1609/aaai.v36i11.21435.
Texto completoZhou, Renzhe, Chen-Xiao Gao, Zongzhang Zhang y Yang Yu. "Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 15 (24 de marzo de 2024): 17132–40. http://dx.doi.org/10.1609/aaai.v38i15.29658.
Texto completoGuo, Runze, Bei Sun, Xiaotian Qiu, Shaojing Su, Zhen Zuo y Peng Wu. "Fine-Grained Recognition of Surface Targets with Limited Data". Electronics 9, n.º 12 (2 de diciembre de 2020): 2044. http://dx.doi.org/10.3390/electronics9122044.
Texto completoBernatchez, Renaud, Audrey Durand y Flavie Lavoie-Cardinal. "Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 12913–14. http://dx.doi.org/10.1609/aaai.v36i11.21593.
Texto completoAlzubaidi, Laith, Muthana Al-Amidie, Ahmed Al-Asadi, Amjad J. Humaidi, Omran Al-Shamma, Mohammed A. Fadhel, Jinglan Zhang, J. Santamaría y Ye Duan. "Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data". Cancers 13, n.º 7 (30 de marzo de 2021): 1590. http://dx.doi.org/10.3390/cancers13071590.
Texto completoAyaz, Adeeba, Maddu Rajesh, Shailesh Kumar Singh y Shaik Rehana. "Estimation of reference evapotranspiration using machine learning models with limited data". AIMS Geosciences 7, n.º 3 (2021): 268–90. http://dx.doi.org/10.3934/geosci.2021016.
Texto completoMazumder, Pratik y Pravendra Singh. "Protected attribute guided representation learning for bias mitigation in limited data". Knowledge-Based Systems 244 (mayo de 2022): 108449. http://dx.doi.org/10.1016/j.knosys.2022.108449.
Texto completoBardis, Michelle, Roozbeh Houshyar, Chanon Chantaduly, Alexander Ushinsky, Justin Glavis-Bloom, Madeleine Shaver, Daniel Chow, Edward Uchio y Peter Chang. "Deep Learning with Limited Data: Organ Segmentation Performance by U-Net". Electronics 9, n.º 8 (26 de julio de 2020): 1199. http://dx.doi.org/10.3390/electronics9081199.
Texto completoKrishnagopal, Sanjukta, Yiannis Aloimonos y Michelle Girvan. "Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach". Complexity 2018 (1 de noviembre de 2018): 1–15. http://dx.doi.org/10.1155/2018/6953836.
Texto completoTufek, Nilay, Murat Yalcin, Mucahit Altintas, Fatma Kalaoglu, Yi Li y Senem Kursun Bahadir. "Human Action Recognition Using Deep Learning Methods on Limited Sensory Data". IEEE Sensors Journal 20, n.º 6 (15 de marzo de 2020): 3101–12. http://dx.doi.org/10.1109/jsen.2019.2956901.
Texto completoHuang, Jianqing, Hecong Liu, Jinghang Dai y Weiwei Cai. "Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning". Journal of Quantitative Spectroscopy and Radiative Transfer 218 (octubre de 2018): 187–93. http://dx.doi.org/10.1016/j.jqsrt.2018.07.011.
Texto completoTorres, Alfonso F., Wynn R. Walker y Mac McKee. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data". Agricultural Water Management 98, n.º 4 (febrero de 2011): 553–62. http://dx.doi.org/10.1016/j.agwat.2010.10.012.
Texto completoHolzer, Jorge y Qian Qu. "Confidence of the trembling hand: Bayesian learning with data-limited stocks". Natural Resource Modeling 31, n.º 2 (12 de marzo de 2018): e12164. http://dx.doi.org/10.1111/nrm.12164.
Texto completoNiezgoda, Stephen R. y Jared Glover. "Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data". Metallurgical and Materials Transactions A 44, n.º 11 (22 de febrero de 2013): 4891–905. http://dx.doi.org/10.1007/s11661-013-1653-7.
Texto completoZhang, Jialin, Mairidan Wushouer, Gulanbaier Tuerhong y Hanfang Wang. "Semi-Supervised Learning for Robust Emotional Speech Synthesis with Limited Data". Applied Sciences 13, n.º 9 (6 de mayo de 2023): 5724. http://dx.doi.org/10.3390/app13095724.
Texto completoShin, Hyunkyung, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh y Honguk Woo. "Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions". Applied Sciences 12, n.º 14 (21 de julio de 2022): 7317. http://dx.doi.org/10.3390/app12147317.
Texto completoDYCHKA, Ivan, Kateryna POTAPOVA, Liliya VOVK, Vasyl MELIUKH y Olga VEDENIEIEVA. "ADAPTIVE DOMAIN-SPECIFIC NAMED ENTITY RECOGNITION METHOD WITH LIMITED DATA". MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, n.º 1 (28 de marzo de 2024): 82–92. http://dx.doi.org/10.31891/2219-9365-2024-77-11.
Texto completoAthey, Susan y Stefan Wager. "Policy Learning With Observational Data". Econometrica 89, n.º 1 (2021): 133–61. http://dx.doi.org/10.3982/ecta15732.
Texto completoRadino, Radino y Lia Fatika Yiyi Permatasari. "PAI Teacher Strategy in Improving Learning Effectiveness in Limited Face-to-Face Learning". Jurnal Pendidikan Agama Islam 19, n.º 2 (31 de diciembre de 2022): 249–62. http://dx.doi.org/10.14421/jpai.2022.192-06.
Texto completoWang, Jingjing, Zheng Liu, Rong Xie y Lei Ran. "Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data". Remote Sensing 13, n.º 4 (18 de febrero de 2021): 750. http://dx.doi.org/10.3390/rs13040750.
Texto completoLee, Young-Pyo, Ki-Yeon Kim y Yong Soo Kim. "Comparative Study on Predictive Power of Machine Learning with Limited Data Collection". Journal of Applied Reliability 19, n.º 3 (30 de septiembre de 2019): 210–25. http://dx.doi.org/10.33162/jar.2019.09.19.3.210.
Texto completoBang, Junseong, Piergiuseppe Di Marco, Hyejeon Shin y Pangun Park. "Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data". Applied Sciences 12, n.º 15 (25 de julio de 2022): 7450. http://dx.doi.org/10.3390/app12157450.
Texto completoYang, Qiuju, Yingying Wang y Jie Ren. "Auroral Image Classification With Very Limited Labeled Data Using Few-Shot Learning". IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2022.3151755.
Texto completoVillon, Sébastien, Corina Iovan, Morgan Mangeas, Thomas Claverie, David Mouillot, Sébastien Villéger y Laurent Vigliola. "Automatic underwater fish species classification with limited data using few-shot learning". Ecological Informatics 63 (julio de 2021): 101320. http://dx.doi.org/10.1016/j.ecoinf.2021.101320.
Texto completoOh, Yujin, Sangjoon Park y Jong Chul Ye. "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets". IEEE Transactions on Medical Imaging 39, n.º 8 (agosto de 2020): 2688–700. http://dx.doi.org/10.1109/tmi.2020.2993291.
Texto completoSaufi, Syahril Ramadhan, Zair Asrar Bin Ahmad, Mohd Salman Leong y Meng Hee Lim. "Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample". IEEE Transactions on Industrial Informatics 16, n.º 10 (octubre de 2020): 6263–71. http://dx.doi.org/10.1109/tii.2020.2967822.
Texto completoXue, Yongjian y Pierre Beauseroy. "Transfer learning for one class SVM adaptation to limited data distribution change". Pattern Recognition Letters 100 (diciembre de 2017): 117–23. http://dx.doi.org/10.1016/j.patrec.2017.10.030.
Texto completoSeliya, Naeem y Taghi M. Khoshgoftaar. "Software quality estimation with limited fault data: a semi-supervised learning perspective". Software Quality Journal 15, n.º 3 (10 de agosto de 2007): 327–44. http://dx.doi.org/10.1007/s11219-007-9013-8.
Texto completoBieker, Katharina, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz y Michael Dellnitz. "Deep model predictive flow control with limited sensor data and online learning". Theoretical and Computational Fluid Dynamics 34, n.º 4 (12 de marzo de 2020): 577–91. http://dx.doi.org/10.1007/s00162-020-00520-4.
Texto completoLuo, Xihaier y Ahsan Kareem. "Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data". Structural Safety 84 (mayo de 2020): 101918. http://dx.doi.org/10.1016/j.strusafe.2019.101918.
Texto completoChan, Zeke S. H., H. W. Ngan, A. B. Rad, A. K. David y N. Kasabov. "Short-term ANN load forecasting from limited data using generalization learning strategies". Neurocomputing 70, n.º 1-3 (diciembre de 2006): 409–19. http://dx.doi.org/10.1016/j.neucom.2005.12.131.
Texto completoJain, Sanjay y Efim Kinber. "Learning languages from positive data and a limited number of short counterexamples". Theoretical Computer Science 389, n.º 1-2 (diciembre de 2007): 190–218. http://dx.doi.org/10.1016/j.tcs.2007.08.010.
Texto completoWagenaar, Dennis, Jurjen de Jong y Laurens M. Bouwer. "Multi-variable flood damage modelling with limited data using supervised learning approaches". Natural Hazards and Earth System Sciences 17, n.º 9 (29 de septiembre de 2017): 1683–96. http://dx.doi.org/10.5194/nhess-17-1683-2017.
Texto completoFuhg, Jan Niklas, Craig M. Hamel, Kyle Johnson, Reese Jones y Nikolaos Bouklas. "Modular machine learning-based elastoplasticity: Generalization in the context of limited data". Computer Methods in Applied Mechanics and Engineering 407 (marzo de 2023): 115930. http://dx.doi.org/10.1016/j.cma.2023.115930.
Texto completoJeon, Byung-Ki y Eui-Jong Kim. "Solar irradiance prediction using reinforcement learning pre-trained with limited historical data". Energy Reports 10 (noviembre de 2023): 2513–24. http://dx.doi.org/10.1016/j.egyr.2023.09.042.
Texto completoMostafa, Reham R., Ozgur Kisi, Rana Muhammad Adnan, Tayeb Sadeghifar y Alban Kuriqi. "Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data". Water 15, n.º 3 (25 de enero de 2023): 486. http://dx.doi.org/10.3390/w15030486.
Texto completoMohammad Talebzadeh, Abolfazl Sodagartojgi, Zahra Moslemi, Sara Sedighi, Behzad Kazemi y Faezeh Akbari. "Deep learning-based retinal abnormality detection from OCT images with limited data". World Journal of Advanced Research and Reviews 21, n.º 3 (30 de marzo de 2024): 690–98. http://dx.doi.org/10.30574/wjarr.2024.21.3.0716.
Texto completoShe, Daoming, Zhichao Yang, Yudan Duan, Xiaoan Yan, Jin Chen y Yaoming Li. "A meta transfer learning method for gearbox fault diagnosis with limited data". Measurement Science and Technology 35, n.º 8 (9 de mayo de 2024): 086114. http://dx.doi.org/10.1088/1361-6501/ad4665.
Texto completoCagliero, Luca, Lorenzo Canale y Laura Farinetti. "Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories". Algorithms 16, n.º 10 (2 de octubre de 2023): 464. http://dx.doi.org/10.3390/a16100464.
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