Artykuły w czasopismach na temat „Learning with Limited Data”
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Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright i Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting". Proceedings on Privacy Enhancing Technologies 2021, nr 2 (29.01.2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.
Pełny tekst źródłaTriantafillou, Sofia, i Greg Cooper. "Learning Adjustment Sets from Observational and Limited Experimental Data". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 11 (18.05.2021): 9940–48. http://dx.doi.org/10.1609/aaai.v35i11.17194.
Pełny tekst źródłaZhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court i Jinzhong Yang. "Training deep‐learning segmentation models from severely limited data". Medical Physics 48, nr 4 (19.02.2021): 1697–706. http://dx.doi.org/10.1002/mp.14728.
Pełny tekst źródłaKim, Minjeong, Yujung Gil, Yuyeon Kim i Jihie Kim. "Deep-Learning-Based Scalp Image Analysis Using Limited Data". Electronics 12, nr 6 (14.03.2023): 1380. http://dx.doi.org/10.3390/electronics12061380.
Pełny tekst źródłaChen, Jiaao, Derek Tam, Colin Raffel, Mohit Bansal i 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.
Pełny tekst źródłaHan, Te, Chao Liu, Rui Wu i Dongxiang Jiang. "Deep transfer learning with limited data for machinery fault diagnosis". Applied Soft Computing 103 (maj 2021): 107150. http://dx.doi.org/10.1016/j.asoc.2021.107150.
Pełny tekst źródłaJi, Xuefei, Jue Wang, Ye Li, Qiang Sun, Shi Jin i Tony Q. S. Quek. "Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model". IEEE Communications Letters 24, nr 10 (październik 2020): 2191–95. http://dx.doi.org/10.1109/lcomm.2020.3004877.
Pełny tekst źródłaForestier, Germain, i Cédric Wemmert. "Semi-supervised learning using multiple clusterings with limited labeled data". Information Sciences 361-362 (wrzesień 2016): 48–65. http://dx.doi.org/10.1016/j.ins.2016.04.040.
Pełny tekst źródłaWen, Jiahui, i Zhiying Wang. "Learning general model for activity recognition with limited labelled data". Expert Systems with Applications 74 (maj 2017): 19–28. http://dx.doi.org/10.1016/j.eswa.2017.01.002.
Pełny tekst źródłaZhang, Ansi, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong i 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.
Pełny tekst źródłaTulsyan, Aditya, Christopher Garvin i Cenk Undey. "Machine-learning for biopharmaceutical batch process monitoring with limited data". IFAC-PapersOnLine 51, nr 18 (2018): 126–31. http://dx.doi.org/10.1016/j.ifacol.2018.09.287.
Pełny tekst źródłaPrasanna Das, Hari, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoffrey Tison, Alberto Sangiovanni-Vincentelli i 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, nr 11 (28.06.2022): 11792–800. http://dx.doi.org/10.1609/aaai.v36i11.21435.
Pełny tekst źródłaZhou, Renzhe, Chen-Xiao Gao, Zongzhang Zhang i Yang Yu. "Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 15 (24.03.2024): 17132–40. http://dx.doi.org/10.1609/aaai.v38i15.29658.
Pełny tekst źródłaGuo, Runze, Bei Sun, Xiaotian Qiu, Shaojing Su, Zhen Zuo i Peng Wu. "Fine-Grained Recognition of Surface Targets with Limited Data". Electronics 9, nr 12 (2.12.2020): 2044. http://dx.doi.org/10.3390/electronics9122044.
Pełny tekst źródłaBernatchez, Renaud, Audrey Durand i Flavie Lavoie-Cardinal. "Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 11 (28.06.2022): 12913–14. http://dx.doi.org/10.1609/aaai.v36i11.21593.
Pełny tekst źródłaAlzubaidi, Laith, Muthana Al-Amidie, Ahmed Al-Asadi, Amjad J. Humaidi, Omran Al-Shamma, Mohammed A. Fadhel, Jinglan Zhang, J. Santamaría i Ye Duan. "Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data". Cancers 13, nr 7 (30.03.2021): 1590. http://dx.doi.org/10.3390/cancers13071590.
Pełny tekst źródłaAyaz, Adeeba, Maddu Rajesh, Shailesh Kumar Singh i Shaik Rehana. "Estimation of reference evapotranspiration using machine learning models with limited data". AIMS Geosciences 7, nr 3 (2021): 268–90. http://dx.doi.org/10.3934/geosci.2021016.
Pełny tekst źródłaMazumder, Pratik, i Pravendra Singh. "Protected attribute guided representation learning for bias mitigation in limited data". Knowledge-Based Systems 244 (maj 2022): 108449. http://dx.doi.org/10.1016/j.knosys.2022.108449.
Pełny tekst źródłaBardis, Michelle, Roozbeh Houshyar, Chanon Chantaduly, Alexander Ushinsky, Justin Glavis-Bloom, Madeleine Shaver, Daniel Chow, Edward Uchio i Peter Chang. "Deep Learning with Limited Data: Organ Segmentation Performance by U-Net". Electronics 9, nr 8 (26.07.2020): 1199. http://dx.doi.org/10.3390/electronics9081199.
Pełny tekst źródłaKrishnagopal, Sanjukta, Yiannis Aloimonos i Michelle Girvan. "Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach". Complexity 2018 (1.11.2018): 1–15. http://dx.doi.org/10.1155/2018/6953836.
Pełny tekst źródłaTufek, Nilay, Murat Yalcin, Mucahit Altintas, Fatma Kalaoglu, Yi Li i Senem Kursun Bahadir. "Human Action Recognition Using Deep Learning Methods on Limited Sensory Data". IEEE Sensors Journal 20, nr 6 (15.03.2020): 3101–12. http://dx.doi.org/10.1109/jsen.2019.2956901.
Pełny tekst źródłaHuang, Jianqing, Hecong Liu, Jinghang Dai i Weiwei Cai. "Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning". Journal of Quantitative Spectroscopy and Radiative Transfer 218 (październik 2018): 187–93. http://dx.doi.org/10.1016/j.jqsrt.2018.07.011.
Pełny tekst źródłaTorres, Alfonso F., Wynn R. Walker i Mac McKee. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data". Agricultural Water Management 98, nr 4 (luty 2011): 553–62. http://dx.doi.org/10.1016/j.agwat.2010.10.012.
Pełny tekst źródłaHolzer, Jorge, i Qian Qu. "Confidence of the trembling hand: Bayesian learning with data-limited stocks". Natural Resource Modeling 31, nr 2 (12.03.2018): e12164. http://dx.doi.org/10.1111/nrm.12164.
Pełny tekst źródłaNiezgoda, Stephen R., i Jared Glover. "Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data". Metallurgical and Materials Transactions A 44, nr 11 (22.02.2013): 4891–905. http://dx.doi.org/10.1007/s11661-013-1653-7.
Pełny tekst źródłaZhang, Jialin, Mairidan Wushouer, Gulanbaier Tuerhong i Hanfang Wang. "Semi-Supervised Learning for Robust Emotional Speech Synthesis with Limited Data". Applied Sciences 13, nr 9 (6.05.2023): 5724. http://dx.doi.org/10.3390/app13095724.
Pełny tekst źródłaShin, Hyunkyung, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh i Honguk Woo. "Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions". Applied Sciences 12, nr 14 (21.07.2022): 7317. http://dx.doi.org/10.3390/app12147317.
Pełny tekst źródłaDYCHKA, Ivan, Kateryna POTAPOVA, Liliya VOVK, Vasyl MELIUKH i Olga VEDENIEIEVA. "ADAPTIVE DOMAIN-SPECIFIC NAMED ENTITY RECOGNITION METHOD WITH LIMITED DATA". MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, nr 1 (28.03.2024): 82–92. http://dx.doi.org/10.31891/2219-9365-2024-77-11.
Pełny tekst źródłaAthey, Susan, i Stefan Wager. "Policy Learning With Observational Data". Econometrica 89, nr 1 (2021): 133–61. http://dx.doi.org/10.3982/ecta15732.
Pełny tekst źródłaRadino, Radino, i Lia Fatika Yiyi Permatasari. "PAI Teacher Strategy in Improving Learning Effectiveness in Limited Face-to-Face Learning". Jurnal Pendidikan Agama Islam 19, nr 2 (31.12.2022): 249–62. http://dx.doi.org/10.14421/jpai.2022.192-06.
Pełny tekst źródłaWang, Jingjing, Zheng Liu, Rong Xie i Lei Ran. "Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data". Remote Sensing 13, nr 4 (18.02.2021): 750. http://dx.doi.org/10.3390/rs13040750.
Pełny tekst źródłaLee, Young-Pyo, Ki-Yeon Kim i Yong Soo Kim. "Comparative Study on Predictive Power of Machine Learning with Limited Data Collection". Journal of Applied Reliability 19, nr 3 (30.09.2019): 210–25. http://dx.doi.org/10.33162/jar.2019.09.19.3.210.
Pełny tekst źródłaBang, Junseong, Piergiuseppe Di Marco, Hyejeon Shin i Pangun Park. "Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data". Applied Sciences 12, nr 15 (25.07.2022): 7450. http://dx.doi.org/10.3390/app12157450.
Pełny tekst źródłaYang, Qiuju, Yingying Wang i 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.
Pełny tekst źródłaVillon, Sébastien, Corina Iovan, Morgan Mangeas, Thomas Claverie, David Mouillot, Sébastien Villéger i Laurent Vigliola. "Automatic underwater fish species classification with limited data using few-shot learning". Ecological Informatics 63 (lipiec 2021): 101320. http://dx.doi.org/10.1016/j.ecoinf.2021.101320.
Pełny tekst źródłaOh, Yujin, Sangjoon Park i Jong Chul Ye. "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets". IEEE Transactions on Medical Imaging 39, nr 8 (sierpień 2020): 2688–700. http://dx.doi.org/10.1109/tmi.2020.2993291.
Pełny tekst źródłaSaufi, Syahril Ramadhan, Zair Asrar Bin Ahmad, Mohd Salman Leong i Meng Hee Lim. "Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample". IEEE Transactions on Industrial Informatics 16, nr 10 (październik 2020): 6263–71. http://dx.doi.org/10.1109/tii.2020.2967822.
Pełny tekst źródłaXue, Yongjian, i Pierre Beauseroy. "Transfer learning for one class SVM adaptation to limited data distribution change". Pattern Recognition Letters 100 (grudzień 2017): 117–23. http://dx.doi.org/10.1016/j.patrec.2017.10.030.
Pełny tekst źródłaSeliya, Naeem, i Taghi M. Khoshgoftaar. "Software quality estimation with limited fault data: a semi-supervised learning perspective". Software Quality Journal 15, nr 3 (10.08.2007): 327–44. http://dx.doi.org/10.1007/s11219-007-9013-8.
Pełny tekst źródłaBieker, Katharina, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz i Michael Dellnitz. "Deep model predictive flow control with limited sensor data and online learning". Theoretical and Computational Fluid Dynamics 34, nr 4 (12.03.2020): 577–91. http://dx.doi.org/10.1007/s00162-020-00520-4.
Pełny tekst źródłaLuo, Xihaier, i Ahsan Kareem. "Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data". Structural Safety 84 (maj 2020): 101918. http://dx.doi.org/10.1016/j.strusafe.2019.101918.
Pełny tekst źródłaChan, Zeke S. H., H. W. Ngan, A. B. Rad, A. K. David i N. Kasabov. "Short-term ANN load forecasting from limited data using generalization learning strategies". Neurocomputing 70, nr 1-3 (grudzień 2006): 409–19. http://dx.doi.org/10.1016/j.neucom.2005.12.131.
Pełny tekst źródłaJain, Sanjay, i Efim Kinber. "Learning languages from positive data and a limited number of short counterexamples". Theoretical Computer Science 389, nr 1-2 (grudzień 2007): 190–218. http://dx.doi.org/10.1016/j.tcs.2007.08.010.
Pełny tekst źródłaWagenaar, Dennis, Jurjen de Jong i Laurens M. Bouwer. "Multi-variable flood damage modelling with limited data using supervised learning approaches". Natural Hazards and Earth System Sciences 17, nr 9 (29.09.2017): 1683–96. http://dx.doi.org/10.5194/nhess-17-1683-2017.
Pełny tekst źródłaFuhg, Jan Niklas, Craig M. Hamel, Kyle Johnson, Reese Jones i Nikolaos Bouklas. "Modular machine learning-based elastoplasticity: Generalization in the context of limited data". Computer Methods in Applied Mechanics and Engineering 407 (marzec 2023): 115930. http://dx.doi.org/10.1016/j.cma.2023.115930.
Pełny tekst źródłaJeon, Byung-Ki, i Eui-Jong Kim. "Solar irradiance prediction using reinforcement learning pre-trained with limited historical data". Energy Reports 10 (listopad 2023): 2513–24. http://dx.doi.org/10.1016/j.egyr.2023.09.042.
Pełny tekst źródłaMostafa, Reham R., Ozgur Kisi, Rana Muhammad Adnan, Tayeb Sadeghifar i Alban Kuriqi. "Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data". Water 15, nr 3 (25.01.2023): 486. http://dx.doi.org/10.3390/w15030486.
Pełny tekst źródłaMohammad Talebzadeh, Abolfazl Sodagartojgi, Zahra Moslemi, Sara Sedighi, Behzad Kazemi i Faezeh Akbari. "Deep learning-based retinal abnormality detection from OCT images with limited data". World Journal of Advanced Research and Reviews 21, nr 3 (30.03.2024): 690–98. http://dx.doi.org/10.30574/wjarr.2024.21.3.0716.
Pełny tekst źródłaShe, Daoming, Zhichao Yang, Yudan Duan, Xiaoan Yan, Jin Chen i Yaoming Li. "A meta transfer learning method for gearbox fault diagnosis with limited data". Measurement Science and Technology 35, nr 8 (9.05.2024): 086114. http://dx.doi.org/10.1088/1361-6501/ad4665.
Pełny tekst źródłaCagliero, Luca, Lorenzo Canale i Laura Farinetti. "Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories". Algorithms 16, nr 10 (2.10.2023): 464. http://dx.doi.org/10.3390/a16100464.
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