Littérature scientifique sur le sujet « Learning with Limited Data »
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Articles de revues sur le sujet "Learning with Limited Data"
Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright et Nicholas Hopper. « GANDaLF : GAN for Data-Limited Fingerprinting ». Proceedings on Privacy Enhancing Technologies 2021, no 2 (29 janvier 2021) : 305–22. http://dx.doi.org/10.2478/popets-2021-0029.
Texte intégralTriantafillou, Sofia, et Greg Cooper. « Learning Adjustment Sets from Observational and Limited Experimental Data ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 11 (18 mai 2021) : 9940–48. http://dx.doi.org/10.1609/aaai.v35i11.17194.
Texte intégralZhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court et Jinzhong Yang. « Training deep‐learning segmentation models from severely limited data ». Medical Physics 48, no 4 (19 février 2021) : 1697–706. http://dx.doi.org/10.1002/mp.14728.
Texte intégralKim, Minjeong, Yujung Gil, Yuyeon Kim et Jihie Kim. « Deep-Learning-Based Scalp Image Analysis Using Limited Data ». Electronics 12, no 6 (14 mars 2023) : 1380. http://dx.doi.org/10.3390/electronics12061380.
Texte intégralChen, Jiaao, Derek Tam, Colin Raffel, Mohit Bansal et 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.
Texte intégralHan, Te, Chao Liu, Rui Wu et Dongxiang Jiang. « Deep transfer learning with limited data for machinery fault diagnosis ». Applied Soft Computing 103 (mai 2021) : 107150. http://dx.doi.org/10.1016/j.asoc.2021.107150.
Texte intégralJi, Xuefei, Jue Wang, Ye Li, Qiang Sun, Shi Jin et Tony Q. S. Quek. « Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model ». IEEE Communications Letters 24, no 10 (octobre 2020) : 2191–95. http://dx.doi.org/10.1109/lcomm.2020.3004877.
Texte intégralForestier, Germain, et Cédric Wemmert. « Semi-supervised learning using multiple clusterings with limited labeled data ». Information Sciences 361-362 (septembre 2016) : 48–65. http://dx.doi.org/10.1016/j.ins.2016.04.040.
Texte intégralWen, Jiahui, et Zhiying Wang. « Learning general model for activity recognition with limited labelled data ». Expert Systems with Applications 74 (mai 2017) : 19–28. http://dx.doi.org/10.1016/j.eswa.2017.01.002.
Texte intégralZhang, Ansi, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong et 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.
Texte intégralThèses sur le sujet "Learning with Limited Data"
Chen, Si. « Active Learning Under Limited Interaction with Data Labeler ». Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104894.
Texte intégralM.S.
Machine Learning (ML) has achieved huge success in recent years. Machine Learning technologies such as recommendation system, speech recognition and image recognition play an important role on human daily life. This success mainly build upon the use of large amount of labeled data: Compared with traditional programming, a ML algorithm does not rely on explicit instructions from human; instead, it takes the data along with the label as input, and aims to learn a function that can correctly map data to the label space by itself. However, data labeling requires human effort and could be time-consuming and expensive especially for datasets that contain domain-specific knowledge (e.g., disease prediction etc.) Active Learning (AL) is one of the solution to reduce data labeling effort. Specifically, the learning algorithm actively selects data points that provide more information for the model, hence a better model can be achieved with less labeled data. While traditional AL strategies do achieve good performance, it requires a small amount of labeled data as initialization and performs data selection in multi-round, which pose great challenge to its application, as there is no platform provide timely online interaction with data labeler and the interaction is often time inefficient. To deal with the limitations, we first propose DULO which a new setting of AL is studied: data selection is only allowed to be performed once. To further broaden the application of our method, we propose D²ULO which is built upon DULO and Domain Adaptation techniques to avoid the use of initial labeled data. Our experiments show that both of the proposed two frameworks achieve better performance compared with state-of-the-art baselines.
Dvornik, Mikita. « Learning with Limited Annotated Data for Visual Understanding ». Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM050.
Texte intégralThe ability of deep-learning methods to excel in computer vision highly depends on the amount of annotated data available for training. For some tasks, annotation may be too costly and labor intensive, thus becoming the main obstacle to better accuracy. Algorithms that learn from data automatically, without human supervision, perform substantially worse than their fully-supervised counterparts. Thus, there is a strong motivation to work on effective methods for learning with limited annotations. This thesis proposes to exploit prior knowledge about the task and develops more effective solutions for scene understanding and few-shot image classification.Main challenges of scene understanding include object detection, semantic and instance segmentation. Similarly, all these tasks aim at recognizing and localizing objects, at region- or more precise pixel-level, which makes the annotation process difficult. The first contribution of this manuscript is a Convolutional Neural Network (CNN) that performs both object detection and semantic segmentation. We design a specialized network architecture, that is trained to solve both problems in one forward pass, and operates in real-time. Thanks to the multi-task training procedure, both tasks benefit from each other in terms of accuracy, with no extra labeled data.The second contribution introduces a new technique for data augmentation, i.e., artificially increasing the amount of training data. It aims at creating new scenes by copy-pasting objects from one image to another, within a given dataset. Placing an object in a right context was found to be crucial in order to improve scene understanding performance. We propose to model visual context explicitly using a CNN that discovers correlations between object categories and their typical neighborhood, and then proposes realistic locations for augmentation. Overall, pasting objects in ``right'' locations allows to improve object detection and segmentation performance, with higher gains in limited annotation scenarios.For some problems, the data is extremely scarce, and an algorithm has to learn new concepts from a handful of examples. Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. While most current methods concentrate on the adaptation mechanism, few works have tackled the problem of scarce training data explicitly. In our third contribution, we show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform more sophisticated existing techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. By matching different networks outputs on similar input images, we improve model accuracy and robustness, comparing to classical ensemble training. Moreover, a single network obtained by distillation shows similar to the full ensemble performance and yields state-of-the-art results with no computational overhead at test time
Moskvyak, Olga. « Learning from limited annotated data for re-identification problem ». Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226866/1/Olga_Moskvyak_Thesis.pdf.
Texte intégralXian, Yongqin [Verfasser]. « Learning from limited labeled data - Zero-Shot and Few-Shot Learning / Yongqin Xian ». Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1219904457/34.
Texte intégralEriksson, Håkan. « Clustering Generic Log Files Under Limited Data Assumptions ». Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189642.
Texte intégralKomplexa datorsystem är ofta benägna att uppvisa anormalt eller felaktigt beteende, vilket kan leda till kostsamma driftstopp under tiden som systemen diagnosticeras och repareras. En informationskälla till feldiagnosticeringen är loggfiler, vilka ofta genereras i stora mängder och av olika typer. Givet loggfilernas storlek och semistrukturerade utseende så blir en manuell analys orimlig att genomföra. Viss automatisering är önsvkärd för att sovra bland loggfilerna så att källan till felen och anormaliteterna blir enklare att upptäcka. Det här projektet syftade till att utveckla en generell algoritm som kan klustra olikartade loggfiler i enlighet med domänexpertis. Resultaten visar att algoritmen presterar väl i enlighet med manuell klustring även med färre antaganden om datan.
Boman, Jimmy. « A deep learning approach to defect detection with limited data availability ». Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173207.
Texte intégralGuo, Zhenyu. « Data famine in big data era : machine learning algorithms for visual object recognition with limited training data ». Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46412.
Texte intégralAyllon, Clemente Irene [Verfasser]. « Towards natural speech acquisition : incremental word learning with limited data / Irene Ayllon Clemente ». Bielefeld : Universitätsbibliothek Bielefeld, 2013. http://d-nb.info/1077063458/34.
Texte intégralChang, Fengming. « Learning accuracy from limited data using mega-fuzzification method to improve small data set learning accuracy for early flexible manufacturing system scheduling ». Saarbrücken VDM Verlag Dr. Müller, 2005. http://d-nb.info/989267156/04.
Texte intégralTania, Zannatun Nayem. « Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292105.
Texte intégralDistribuerad databehandling möjliggör effektiv datalagring, bearbetning och hämtning men det medför säkerhets- och sekretessproblem. Sensorer är hörnstenen i de IoT-baserade rörledningarna, eftersom de ständigt samlar in data tills de kan analyseras på de centrala molnresurserna. Dessa sensornoder begränsas dock ofta av begränsade resurser. Helst är det önskvärt att göra alla insamlade datafunktioner privata, men på grund av resursbegränsningar kanske det inte alltid är möjligt. Att göra alla funktioner privata kan orsaka överutnyttjande av resurser, vilket i sin tur skulle påverka prestanda för hela systemet. I denna avhandling designar och implementerar vi ett system som kan hitta den optimala uppsättningen datafunktioner för att göra privata, med tanke på begränsningar av enhetsresurserna och systemets önskade prestanda eller noggrannhet. Med hjälp av generaliseringsteknikerna för data-anonymisering skapar vi användardefinierade injicerbara sekretess-kodningsfunktioner för att göra varje funktion i datasetet privat. Oavsett resurstillgänglighet definieras vissa datafunktioner av användaren som viktiga funktioner för att göra privat. Alla andra datafunktioner som kan utgöra ett integritetshot kallas de icke-väsentliga funktionerna. Vi föreslår Dynamic Iterative Greedy Search (DIGS), en girig sökalgoritm som tar resursförbrukningen för varje icke-väsentlig funktion som inmatning och ger den mest optimala uppsättningen icke-väsentliga funktioner som kan vara privata med tanke på tillgängliga resurser. Den mest optimala uppsättningen innehåller de funktioner som förbrukar minst resurser. Vi utvärderar vårt system på en Fitbit-dataset som innehåller 17 datafunktioner, varav 4 är viktiga privata funktioner för en viss klassificeringsapplikation. Våra resultat visar att vi kan erbjuda ytterligare 9 privata funktioner förutom de 4 viktiga funktionerna i Fitbit-datasetet som innehåller 1663 poster. Dessutom kan vi spara 26; 21% minne jämfört med att göra alla funktioner privata. Vi testar också vår metod på en större dataset som genereras med Generative Adversarial Network (GAN). Den valda kantenheten, Raspberry Pi, kan dock inte tillgodose storleken på den stora datasetet på grund av otillräckliga resurser. Våra utvärderingar med 1=8th av GAN-datasetet resulterar i 3 extra privata funktioner med upp till 62; 74% minnesbesparingar jämfört med alla privata datafunktioner. Att upprätthålla integritet kräver inte bara ytterligare resurser utan har också konsekvenser för de designade applikationernas prestanda. Vi upptäcker dock att integritetskodning har en positiv inverkan på noggrannheten i klassificeringsmodellen för vår valda klassificeringsapplikation.
Livres sur le sujet "Learning with Limited Data"
Zamzmi, Ghada, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman et Zhiyun Xue, dir. Medical Image Learning with Limited and Noisy Data. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7.
Texte intégralXue, Zhiyun, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru et Zhaohui Liang, dir. Medical Image Learning with Limited and Noisy Data. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44917-8.
Texte intégralFisher, Doug, et Hans-J. Lenz, dir. Learning from Data. New York, NY : Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4.
Texte intégralBig learning data. Alexandria, VA : ASTD Press, 2014.
Trouver le texte intégralHartemink, Alfred E., Alex McBratney et Maria de Lourdes Mendonça-Santos, dir. Digital Soil Mapping with Limited Data. Dordrecht : Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8592-5.
Texte intégral1964-, Hartemink Alfred E., McBratney A. B et Mendonça-Santos Maria de Lourdes, dir. Digital soil mapping with limited data. Dordrecht : Springer, 2008.
Trouver le texte intégralVelleman, Paul F. Learning data analysis with Data desk. New York : W.H. Freeman, 1993.
Trouver le texte intégralLearning data analysis with Data desk. New York : W.H. Freeman, 1989.
Trouver le texte intégralVNU Entertainment Media UK Limited and Book Data Limited : A report on the acquisition by VNU Entertainment Media UK Limited of Book Data Limited. London : Stationery Office, 2003.
Trouver le texte intégralDean, Jared. Big Data, Data Mining, and Machine Learning. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118691786.
Texte intégralChapitres de livres sur le sujet "Learning with Limited Data"
Bennett, James, Kitty Kautzer et Leila Casteel. « Analyzing question items with limited data ». Dans Data Analytics and Adaptive Learning, 230–41. New York : Routledge, 2023. http://dx.doi.org/10.4324/9781003244271-16.
Texte intégralHe, Xiangyu, et Jian Cheng. « Learning Compression from Limited Unlabeled Data ». Dans Computer Vision – ECCV 2018, 778–95. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01246-5_46.
Texte intégralChen, Shurui, Yufu Chen, Yuyin Lu, Yanghui Rao, Haoran Xie et Qing Li. « Chinese Word Embedding Learning with Limited Data ». Dans Web and Big Data, 211–26. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85896-4_18.
Texte intégralWang, Li-C. « Learning from Limited Data in VLSI CAD ». Dans Machine Learning in VLSI Computer-Aided Design, 375–99. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04666-8_13.
Texte intégralChan, Yung-Chieh, Jerry Zhang, Katie Frizzi, Nigel Calcutt et Garrison Cottrell. « Automated Skin Biopsy Analysis with Limited Data ». Dans Medical Image Learning with Limited and Noisy Data, 229–38. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7_22.
Texte intégralRamlan, Fitria Wulandari, et James McDermott. « Genetic Programming with Synthetic Data for Interpretable Regression Modelling and Limited Data ». Dans Machine Learning, Optimization, and Data Science, 142–57. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53969-5_12.
Texte intégralVu, Tu Thanh, Giang Binh Tran et Son Bao Pham. « Learning to Simplify Children Stories with Limited Data ». Dans Intelligent Information and Database Systems, 31–41. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05476-6_4.
Texte intégralNguyen, Minh-Tien, Viet-Anh Phan, Le Thai Linh, Nguyen Hong Son, Le Tien Dung, Miku Hirano et Hajime Hotta. « Transfer Learning for Information Extraction with Limited Data ». Dans Communications in Computer and Information Science, 469–82. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6168-9_38.
Texte intégralJain, Sanjay, et Efim Kinber. « On Learning Languages from Positive Data and a Limited Number of Short Counterexamples ». Dans Learning Theory, 259–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_21.
Texte intégralLiu, Alex X., et Rui Li. « Differentially Private and Budget Limited Bandit Learning over Matroids ». Dans Algorithms for Data and Computation Privacy, 347–82. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58896-0_13.
Texte intégralActes de conférences sur le sujet "Learning with Limited Data"
Malaviya, Maya, Ilia Sucholutsky et Thomas L. Griffiths. « Pushing the Limits of Learning from Limited Data ». Dans 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom : Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1583-0.
Texte intégralYang, Diyi, Ankur Parikh et Colin Raffel. « Learning with Limited Text Data ». Dans Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics : Tutorial Abstracts. Stroudsburg, PA, USA : Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-tutorials.5.
Texte intégralKhoshgoftaar, Taghi M., Chris Seiffert, Jason Van Hulse, Amri Napolitano et Andres Folleco. « Learning with limited minority class data ». Dans Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.76.
Texte intégralChang, Shiyu, Charu C. Aggarwal et Thomas S. Huang. « Learning Local Semantic Distances with Limited Supervision ». Dans 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.114.
Texte intégralSelf, Ryan, S. M. Nahid Mahmud, Katrine Hareland et Rushikesh Kamalapurkar. « Online inverse reinforcement learning with limited data ». Dans 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303883.
Texte intégralChen, Hanlin, et Peng Cao. « Deep Learning Based Data Augmentation and Classification for Limited Medical Data Learning ». Dans 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2019. http://dx.doi.org/10.1109/icpics47731.2019.8942411.
Texte intégralLee, Kyungjae, Sunghyun Park, Hojae Han, Jinyoung Yeo, Seung-won Hwang et Juho Lee. « Learning with Limited Data for Multilingual Reading Comprehension ». Dans Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA : Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1283.
Texte intégralLiu, Feng, Fengzhan Tian et Qiliang Zhu. « Ensembling Bayesian network structure learning on limited data ». Dans the sixteenth ACM conference. New York, New York, USA : ACM Press, 2007. http://dx.doi.org/10.1145/1321440.1321577.
Texte intégralMitchell, Frost, Aniqua Baset, Neal Patwari, Sneha Kumar Kasera et Aditya Bhaskara. « Deep Learning-based Localization in Limited Data Regimes ». Dans WiSec '22 : 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3522783.3529529.
Texte intégralIosifidis, Vasileios, et Eirini Ntoutsi. « Large Scale Sentiment Learning with Limited Labels ». Dans KDD '17 : The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3097983.3098159.
Texte intégralRapports d'organisations sur le sujet "Learning with Limited Data"
Safta, Cosmin, Kookjin Lee et Jaideep Ray. Predictive Skill of Deep Learning Models Trained on Limited Sequence Data. Office of Scientific and Technical Information (OSTI), octobre 2020. http://dx.doi.org/10.2172/1688570.
Texte intégralRhoades, Alan, et Ankur Mahesh. Title:Machine learning to generate gridded extreme precipitation data sets for global land areas with limited in situ measurements. Office of Scientific and Technical Information (OSTI), avril 2021. http://dx.doi.org/10.2172/1769784.
Texte intégralCassity, Elizabeth, et Debbie Wong. Teacher development multi-year studies. Insights on the challenges of data availability for measuring and reporting on student learning outcomes. Australian Council for Educational Research, 2022. http://dx.doi.org/10.37517/978-1-74286-677-2.
Texte intégralSukumar, Sreenivas R., et Carlos Emilio Del-Castillo-Negrete. Machine Learning for Big Data : A Study to Understand Limits at Scale. Office of Scientific and Technical Information (OSTI), décembre 2015. http://dx.doi.org/10.2172/1234336.
Texte intégralChoquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), avril 2019. http://dx.doi.org/10.55274/r0011568.
Texte intégralOgenyi, Moses. Looking back on Nigeria’s COVID-19 School Closures : Effects of Parental Investments on Learning Outcomes and Avoidance of Hysteresis in Education. Research on Improving Systems of Education (RISE), mars 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/040.
Texte intégralAsgedom, Amare, Shelby Carvalho et Pauline Rose. Negotiating Equity : Examining Priorities, Ownership, and Politics Shaping Ethiopia’s Large-Scale Education Reforms for Equitable Learning. Research on Improving Systems of Education (RISE), mars 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2021/067.
Texte intégralBergeron, Augustin, Arnaud Fournier, John Kabeya Kabeya, Gabriel Tourek et Jonathan L. Weigel. Using Machine Learning to Create a Property Tax Roll : Evidence from the City of Kananga, DR Congo'. Institute of Development Studies, octobre 2023. http://dx.doi.org/10.19088/ictd.2023.053.
Texte intégralMahat, Marian, Vivienne Awad, Christopher Bradbeer, Chengxin Guo, Wesley Imms et Julia Morris. Furniture for Engagement. University of Melbourne, février 2023. http://dx.doi.org/10.46580/124374.
Texte intégralQuak, Evert-Jan. K4D’s Work on the Indirect Impacts of COVID-19 in Low- and Middle- Income Countries. Institute of Development Studies (IDS), juin 2021. http://dx.doi.org/10.19088/k4d.2021.093.
Texte intégral