Results 81 to 90 of about 7,740,063 (200)
Multimodal meta-learning through meta-learned task representations
Neural Computing and ApplicationsAbstract Few-shot meta-learning involves training a model on multiple tasks to enable it to efficiently adapt to new, previously unseen tasks with only a limited number of samples. However, current meta-learning methods assume that all tasks are closely related and belong to a common domain, whereas in practice, tasks can be highly ...
Anna Vettoruzzo +2 more
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Informe de práctica empresarial
, 2023 Informe de práctica profesional del programa de Comunicación Social y PeriodismoEl 9 de noviembre de 1982, en el barrio El Triunfo de la ciudad de Villavicencio, se reunieron Rafael Mojica García, Nancy Leonor Espinel Riveros y Ramiro Mojica García ...
Vélez Barrera, Jhon Oscar
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Applied SciencesDeep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to observe,
Jaeuk Moon +3 more
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Few-shot SAR target classification via meta-learning with hybrid models
Frontiers in Earth ScienceCurrently, in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR), few-shot methods can save cost and resources while enhancing adaptability.
Qingtian Geng, Yaning Wang, Qingliang Li
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Meta-learning in Healthcare: A Survey
SN Computer ScienceAs a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization.
Alireza Rafiei +4 more
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MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks
MathematicsThis paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class-Wise Maximum Mean Discrepancy (MMD) approach to enhance domain adaptation.
Hsiau-Wen Lin +4 more
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Meta-Learning and the Full Model Selection Problem [PDF]
, 2014 When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning algorithm and evaluation ...
Sun, Quan
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Informe de práctica empresarial, Alcaldía de Restrepo
, 2023 Informe de práctica profesional del programa de Comunicación Social y PeriodismoLa Alcaldía de Restrepo se crea cuando por medio del Decreto 904 del 23 de septiembre de 1912 cuando el caserío La Concepción fue elevado a la categoría de municipio ...
Sandoval Cisneros, Laura Camila
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IEEE AccessWith the increasing demand for digitization, Optical Character Recognition (OCR) systems play a vital role in digitizing physical manuscripts. Several methods have been successfully deployed in the OCR domain.
Anirudha Ghosh +3 more
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Graph-based generation of a meta-learning search space
International Journal of Applied Mathematics and Computer Science, 2012 Meta-learning is becoming more and more important in current and future research concentrated around broadly defined data mining or computational intelligence. It can solve problems that cannot be solved by any single, specialized algorithm.
Jankowski Norbert
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