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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)
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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 .
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.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. As language models , LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, [23] generative pre-training, ELMo, [24] and ULMFit. [25] Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus .
David W. Johnson (born 1940 in Muncie, Indiana) is a social psychologist whose research has focused on four overlapping areas: [1] cooperative, competitive, and individualistic efforts; constructive controversy; conflict resolution and peer mediation and experiential learning to teach interpersonal and small group skills. [2]
Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm, [ 1 ] developed by Marco Muselli, Senior Researcher at the Italian National Research Council CNR-IEIIT in Genoa .