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High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
Journal for the Education of the Gifted; Journal of Early Intervention; Journal of Learning Disabilities; Journal of Research in Special Educational Needs; Journal of Special Education and Rehabilitation; Learning Disability Quarterly; Remedial and Special Education; Research and Practice for Persons with Severe Disabilities
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
Journal of Economic Education; Journal of Education; Journal of Education for Sustainable Development; Journal of Education Policy; Journal of Educational Administration and History; Journal of Educational and Behavioral Statistics; Journal of Educational Measurement; Journal of Educational Media, Memory, and Society; The Journal of Educational ...
The Journal of Social Work Education is a quarterly peer-reviewed academic journal dedicated to education in the fields of social work and social welfare. It was established in 1965 as the Journal of Education for Social Work, obtaining its current name in 1985. It is published by Taylor & Francis on behalf of the Council on Social Work Education.
The Journal of Social Work is a peer-reviewed academic journal that covers research in the field of social work. The editor-in-chief is Steven M. Shardlow ( Keele University ). It was established in 2001 and is published by SAGE Publishing .
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them.
Few-shot learning [ edit ] A prompt may include a few examples for a model to learn from, such as asking the model to complete " maison → house, chat → cat, chien →" (the expected response being dog ), [ 33 ] an approach called few-shot learning .