Search Results
4 results for “few-shot”
Few-Shot Learning
Few-shot learning is a machine learning paradigm in which a model learns to perform new tasks or recognise new classes from only a small number of labelled training examples, often just one to five samples per class.
Meta-Learning
A machine learning paradigm in which models learn how to learn, acquiring inductive biases across a distribution of tasks so they can adapt rapidly to new tasks with minimal data.
Prompt Engineering
The practice of designing and optimising input instructions given to large language models to elicit accurate, relevant, and well-structured outputs for a given task or application.
Zero-Shot Learning
Zero-shot learning is a machine learning paradigm in which a model makes accurate predictions on categories it has never seen during training by leveraging semantic descriptions or attribute representations.