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